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Minnesota Software Development Consultants | Modernization & AI Transformation

After working closely with our consultants and drawing from their extensive, hands-on experience helping teams implement AI in real applications, we’ve identified the areas where most organizations struggle—both when introducing AI into their systems and after it’s already in place.

These insights are reflected in the articles below, designed to help your team move beyond early experimentation, address real-world challenges, and confidently advance to the next stage of your business.
 

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Planning

Intertech’s software planning & requirement analysis process sets the foundation for the entire software development process.

Architecture & Design

Our software architecture and system design stage lays the groundwork for successful software implementation by providing a clear roadmap for building the system.

Custom Development

Intertech experts help you select languages and implement coding standards and development practices that are well-informed & collaborative when updating or creating new web -based and desktop applications.

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Intertech brings a comprehensive and integrated approach to software quality assurance (QA) and testing that fosters a commitment to delivering software of the highest quality.

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Each type of test serves a specific purpose in the software development process, contributing to the overall quality and reliability of the software. The choice of tests depends on the project’s requirements, goals, and the nature of the software being developed.

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Work with a team that understands cloud migration and cloud integration, as well as application architecture and development, so you get the “cloud full stack” experience from your dev-team.

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Post-AI Challenge Solutions

Pre-AI Integration Assistance

Bringing Discipline to AI Development

Eliminate Technical Debt

AI is writing code faster than teams can understand it—creating a new wave of technical debt inside modern software systems. We can help you use AI correctly.

How We Help

Identify the Right AI Opportunities

AI Confusion to Clear Strategy

The biggest AI problem most companies face isn’t the technology—it’s knowing where AI actually belongs. We can help you identify the right AI opportunities to transform your strategy.

How We Help

Introduce AI Capabilities Strategically Into Your Products

Adding AI Into Product & Platform

The race to add AI to software products is accelerating—but the companies that win are the ones that integrate AI strategically, not just quickly. Let us help your team.

How We Help

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The Architecture Problem

Most AI initiatives don’t fail because of the models—they fail because legacy systems were never designed to support them. Let our team take a look and provide solutions.

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Turn AI Pilots into Production

AI Pilot to AI Success

Early pilots often generate excitement at the beginning, but over time many of them stall before delivering meaningful value. We help you nderstand why and fix the problem.

How We Help

Turn Fragmented Data into an AI Advantage

Make Your Data AI-Ready

AI is only as powerful as the data behind it—and most organizations discover their data isn’t ready. We help bring your organization’s data foundation into order.

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Add AI Expertise Without Replacing Your Team

Upskill Your Developers for the AI Era

The biggest challenge in AI adoption isn’t hiring new engineers—it’s empowering the team you already have. Intertech transforms your team using proven real-world methods.

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Build AI Systems That “Pass”

The biggest barrier to AI adoption isn’t innovation—it’s ensuring your systems remain secure and compliant. Our senior AI consultants can help your team move forward.

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Start AI the Right Way

Many organizations know AI is critical to their future—they’re just not sure how to start without risking costly mistakes. Let Intertech point the way forward.

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Post-AI Challenge Solutions

Why Your AI Costs Are Spiraling (And How to Bring Them Under Control)

At First, The Numbers Don’t Look Dangerous.

AI costs rarely explode because of one bad vendor decision. More often, they rise because the system was never designed to control tokens, model usage, retries, context, caching, and API calls at scale.

Read About The Solution

Or…take an instant diagnostic assessment to find out where your AI system may be spending more than it should!

AI Cost Exposure Diagnostic
Find Out Where Your AI System May Be Spending More Than It Should
This detailed five-step diagnostic is designed for software leaders who want to understand whether AI costs are being controlled by architecture — or allowed to expand through prompts, tokens, retries, model usage, and hidden workflow complexity.
The goal is not simply to calculate a score. The goal is to surface cost issues that may be difficult to see from API invoices alone — especially when AI usage is growing across features, teams, and production workflows.
Step 1 of 5
Cost Visibility
This section looks at whether your team can actually see where AI spend is coming from — not just at the vendor invoice level, but by feature, workflow, prompt, and user interaction.
Why this matters: Without visibility, cost optimization becomes guesswork. Leaders may know AI spending is increasing, but not which system behaviors are causing it.
Question 1 of 20
Can you identify your top 3 most expensive AI-powered features or workflows?
This reveals whether AI cost can be traced to specific system behavior or only viewed as a broad monthly expense.
Please select an answer before continuing.
Question 2 of 20
Do you know the average AI cost per user interaction, transaction, or workflow?
This helps determine whether AI cost can be connected to business activity, product usage, or customer value.
Please select an answer before continuing.
Question 3 of 20
When AI costs spike, how quickly can your team trace the cause?
Cost spikes are easier to control when teams can identify the prompt, workflow, retry loop, model choice, or usage pattern behind them.
Please select an answer before continuing.
Question 4 of 20
Who owns AI cost management today?
AI cost control tends to weaken when no one owns it across engineering, product, architecture, and operations.
Please select an answer before continuing.
Step 2 of 5
Prompt & Token Efficiency
This section examines whether prompts and context are being intentionally controlled or quietly expanding over time.
Why this matters: Token growth is one of the most common silent cost drivers. Prompts often get longer as edge cases are added, and retrieval systems often pass more context than the model truly needs.
Question 5 of 20
How often are prompts reviewed and optimized after deployment?
Prompts often keep accumulating instructions. Without review, working prompts can become expensive prompts.
Please select an answer before continuing.
Question 6 of 20
How is context usually sent to the model?
Sending too much context increases token usage and can also make responses less focused.
Please select an answer before continuing.
Question 7 of 20
What best describes your prompt size over time?
Prompt growth is a common sign that the system is compensating for weak structure through more instructions.
Please select an answer before continuing.
Question 8 of 20
For retrieval or document-based AI, how aggressively do you limit what gets passed into the prompt?
Retrieval systems can become expensive when they pull too much information into every request.
Please select an answer before continuing.
Step 3 of 5
Model Usage Strategy
This section evaluates whether your system is using the right model for the right task.
Why this matters: Many AI systems overpay by sending simple tasks to expensive models. A routing strategy can reduce cost while preserving quality where stronger reasoning is actually needed.
Question 9 of 20
Do you route requests based on task complexity?
Not every request needs the most capable model. Routing helps reserve higher-cost models for higher-value or higher-complexity tasks.
Please select an answer before continuing.
Question 10 of 20
Approximately what percentage of requests use your most expensive model?
This helps reveal whether the system may be overusing premium models for routine work.
Please select an answer before continuing.
Question 11 of 20
Do you dynamically escalate or downgrade model usage?
A cost-aware system can start with a lower-cost path and escalate only when confidence, complexity, or failure conditions require it.
Please select an answer before continuing.
Question 12 of 20
How often are simple classification, formatting, or extraction tasks sent to a high-end model?
Routine tasks are one of the easiest areas to overspend if every AI request goes through the same model path.
Please select an answer before continuing.
Step 4 of 5
Architectural Cost Controls
This section identifies whether the architecture itself helps prevent avoidable AI calls.
Why this matters: Caching, retry limits, deterministic logic, and usage guardrails can dramatically reduce unnecessary model calls before they become recurring production costs.
Question 13 of 20
Do you cache AI responses, embeddings, or workflow outputs?
Caching can prevent repeated model calls when the same or similar request does not require a fresh response.
Please select an answer before continuing.
Question 14 of 20
How are retries and fallback calls controlled?
Retries, fallbacks, and repeated calls can quietly multiply costs when error handling is not carefully bounded.
Please select an answer before continuing.
Question 15 of 20
How often is AI used where deterministic code, rules, or templates could handle the task?
AI should be used where it adds value. Predictable tasks are often better handled through normal application logic.
Please select an answer before continuing.
Question 16 of 20
Do individual AI features have budget thresholds or usage guardrails?
Feature-level thresholds help prevent one workflow or release from unexpectedly driving a large cost increase.
Please select an answer before continuing.
Step 5 of 5
Scale Readiness
This section looks at whether your AI costs are likely to remain predictable as usage increases.
Why this matters: A prototype can appear inexpensive at low volume. The real test is whether cost, quality, and performance remain controlled when users, features, and workflows expand.
Question 17 of 20
If usage doubled tomorrow, what would happen to AI cost?
This tests whether cost is predictable enough to support broader adoption.
Please select an answer before continuing.
Question 18 of 20
Is AI cost considered during feature design?
Cost control is strongest when it is part of architecture and product planning, not only a reaction after launch.
Please select an answer before continuing.
Question 19 of 20
Does your team actively evaluate quality vs. cost tradeoffs?
The best model is not always the most expensive model. Teams need to compare quality, speed, reliability, and cost together.
Please select an answer before continuing.
Question 20 of 20
Are production AI costs reviewed as part of ongoing engineering operations?
AI systems need operational review after launch because usage patterns, prompts, workflows, and costs change over time.
Please select an answer before continuing.
Your Results
What Your Answers Reveal
Enter your information below to receive a copy of the results, to better assist you in analyzing and speaking with your team. A copy will also be sent to our AI experts so if you choose to speak with us, our team will already have an understanding of where your AI system may need stronger cost controls, prompt discipline, model routing, caching, usage visibility, or architectural review.
Please complete all fields before submitting.
Thank you. Your AI Cost Exposure Diagnostic has been submitted.

Why Your Team Doesn’t Trust Your AI (And What to Do About It)

Take a closer look at your AI system

Most teams initially assume that trust issues stem from the model itself—its accuracy, its training data, or its limitations. But in practice, the deeper issue is almost always a lack of observability.

Read About The Solution

Or… take an instant diagnostic assessment to see what your AI system is doing!

AI Observability & Trust Assessment
Can You Actually See What Your AI Is Doing?
Take a few minutes to complete the AI Observability & Trust Assessment and identify where your AI system may be operating as a black box.

This diagnostic helps software leaders uncover gaps in prompt tracing, logging, evaluation, monitoring, governance, and human oversight—so your team can better understand why AI produces certain outputs, where trust is breaking down, and what practical steps can help make the system more explainable, auditable, and reliable in production.
Visibility Area 1
AI Logging: Can your team see the inputs, outputs, and context behind each response?
Trust begins with a basic question: can your team reconstruct what happened? AI systems need structured records of prompts, responses, retrieved data, model settings, timestamps, users, and application context. Without that foundation, teams are often left guessing when an output seems wrong.
Visibility Area 1
Do you log the full prompt and response for important AI interactions?
Without prompt and response logging, teams often cannot determine whether a bad output came from the user input, system prompt, model behavior, missing context, or downstream application logic.
Please select an answer before continuing.
Visibility Area 1
Do you capture the retrieved data or documents used to generate AI responses?
For RAG and knowledge-based systems, the answer is only as trustworthy as the context retrieved. If you cannot see what the model used, you cannot confidently explain the output.
Please select an answer before continuing.
Visibility Area 1
Do your logs include model settings, timestamps, user/session details, and application context?
AI behavior can change based on model version, temperature, token limits, user role, workflow, and surrounding system state. These details matter when debugging or auditing behavior.
Please select an answer before continuing.
Visibility Area 2
Prompt Tracing: Can you follow how an AI output was generated from start to finish?
Modern AI systems rarely make a single model call. They may retrieve documents, call tools, transform prompts, chain multiple steps, and pass outputs into other systems. Prompt tracing helps your team follow that path instead of treating the final answer as a mystery.
Visibility Area 2
Can your team trace an AI request across retrieval, prompts, tools, and final output?
AI workflows often involve multiple steps. If those steps are invisible, developers may only see the final answer, not the path that produced it.
Please select an answer before continuing.
Visibility Area 2
Can developers replay or inspect a specific AI interaction when something goes wrong?
The ability to inspect a specific interaction turns AI debugging from speculation into analysis. It helps teams identify whether the issue was prompt design, source data, tool use, or model behavior.
Please select an answer before continuing.
Visibility Area 2
Are AI traces connected to your normal application logs or request IDs?
AI should not be isolated from the rest of the system. Connecting AI traces to application logs helps teams diagnose problems across the full production workflow.
Please select an answer before continuing.
Visibility Area 3
Evaluation Frameworks: Are you measuring AI quality in a repeatable way?
AI systems cannot be evaluated only by whether an answer sounds good in a demo. Leaders need ways to measure relevance, accuracy, completeness, safety, and business fit across realistic scenarios. Without evaluation, quality becomes anecdotal.
Visibility Area 3
Do you have defined test scenarios for evaluating AI outputs?
AI quality should be tested against realistic user needs, edge cases, and business-critical scenarios—not only through informal review or one-off demos.
Please select an answer before continuing.
Visibility Area 3
Are AI outputs scored for quality, relevance, accuracy, safety, or business fit?
Because AI outputs vary, teams need evaluation rubrics that measure whether responses are useful and appropriate, not merely whether they are grammatically polished.
Please select an answer before continuing.
Visibility Area 3
Do you run regression checks when prompts, models, data, or workflows change?
Small changes can produce unexpected shifts in AI behavior. Regression evaluation helps prevent improvements in one area from creating failures in another.
Please select an answer before continuing.
Visibility Area 4
Monitoring and Alerts: Can your team detect AI issues before users lose confidence?
AI failures are often subtle before they become visible. Monitoring helps identify changes in output quality, hallucination patterns, cost, latency, usage, and unusual behavior before they become larger business or customer issues.
Visibility Area 4
Do you monitor AI behavior in production after deployment?
AI systems should be monitored like production systems. Without monitoring, teams often discover issues only after users lose confidence.
Please select an answer before continuing.
Visibility Area 4
Do you track warning signals such as hallucinations, unusual outputs, cost spikes, or latency issues?
AI risk is not limited to incorrect answers. Cost, speed, usage patterns, source quality, and unexpected output changes can all signal operational problems.
Please select an answer before continuing.
Visibility Area 4
Do you have alerts or review workflows when AI behavior crosses a risk threshold?
Monitoring is only useful if the organization can act on it. Alerts and review workflows help teams respond before small failures become larger incidents.
Please select an answer before continuing.
Visibility Area 5
Governance and Human Oversight: Does your organization know who is accountable for AI behavior?
AI trust is not only technical. Teams also need ownership, escalation paths, review processes, and human oversight for high-risk outputs. When accountability is unclear, teams hesitate to rely on AI even when the technology appears promising.
Visibility Area 5
Is someone clearly accountable for AI behavior in production?
AI systems need defined ownership. If no one owns the behavior, no one owns the improvement, escalation, or risk response process.
Please select an answer before continuing.
Visibility Area 5
Do users or internal teams have a way to flag, correct, or provide feedback on AI outputs?
Human feedback helps teams identify patterns that logs alone may miss. It also gives users a way to build confidence that issues are being addressed.
Please select an answer before continuing.
Visibility Area 5
Are high-risk AI outputs reviewed by humans before decisions or actions are taken?
Some AI use cases require stronger oversight than others. Human review is especially important when outputs affect customers, compliance, financial decisions, security, or sensitive workflows.
Please select an answer before continuing.
Your Assessment Results
Where Your AI System May Lack Visibility and Trust
Enter your information below to receive a copy of the results, to better assist you in analyzing and speaking with your team. A copy will also be sent to our AI experts so if you choose to speak with us, our team will already have an understanding of where your AI system may need stronger observability, tracing, evaluation, monitoring, or governance.
Please complete all fields before submitting.
Thank you. Your AI Observability & Trust Assessment has been submitted and a copy has been sent to your email.

The AI Technical Debt Explosion (And How to Prevent It)

Why AI Is Creating More Debt

There’s a pattern emerging across development teams that have embraced AI coding tools—and it’s not showing up in dashboards or velocity charts right away. In fact, in the early stages, everything looks like success.

Read About The Solution

Or…take an instant diagnostic assessment to find out where AI may be creating technical debt in your codebase!

AI Technical Debt Risk Assessment
Find Out Where AI May Be Creating Technical Debt in Your Codebase
Take a few minutes to complete this assessment and identify where AI-generated code may be weakening architecture, consistency, maintainability, testing discipline, or long-term development speed.

This diagnostic is designed for software leaders who want to understand whether AI is helping the team move faster in a sustainable way—or quietly making the codebase harder to understand, modify, test, and support six months from now.
Risk Area 1
AI Usage Patterns: Is AI being used with enough visibility and discipline?
AI-generated code can enter a codebase quickly, but leaders often lack visibility into where it is being used, how much of it is reaching production, and whether developers are accepting output they do not fully understand. This section looks at whether AI usage is intentional or simply spreading through the development process informally.
Risk Area 1
Do you know where AI-generated or AI-assisted code is entering the codebase?
Leaders cannot manage what they cannot see. If AI-assisted code is being committed without visibility, it becomes difficult to evaluate risk, quality, ownership, or long-term maintainability.
Please select an answer before continuing.
Risk Area 1
Do developers have clear guidance on when AI should and should not be used for production code?
AI can be useful for many development tasks, but some areas require more caution, including security, data access, business rules, architecture, and regulated workflows.
Please select an answer before continuing.
Risk Area 1
Are developers expected to understand and explain AI-generated code before it is merged?
The risk is not that AI writes code. The risk is that no one fully owns the reasoning behind it after it enters the system.
Please select an answer before continuing.
Risk Area 2
Code Standards: Is AI reinforcing your patterns—or introducing new ones every time?
AI tools often generate code that looks reasonable in isolation but does not always match the standards, naming conventions, libraries, or design patterns your team relies on. Over time, small inconsistencies can become system-wide friction.
Risk Area 2
Does AI-generated code consistently follow your team’s established coding standards?
AI may produce code that is syntactically correct but inconsistent with your naming conventions, project structure, preferred libraries, or implementation patterns.
Please select an answer before continuing.
Risk Area 2
Are repeated problems solved using the same approved patterns across the codebase?
Technical debt grows when the same type of problem is solved five different ways, especially when each solution looks reasonable in isolation.
Please select an answer before continuing.
Risk Area 2
Do you have reference examples or internal patterns that guide AI-assisted development?
AI performs better when it is guided by your standards. Without examples, it may generate generic solutions that do not fit your system.
Please select an answer before continuing.
Risk Area 3
Architecture Integrity: Is AI-generated code staying within the system design?
AI can solve the immediate prompt without understanding your architecture, domain boundaries, service responsibilities, or long-term design intent. This section examines whether the architecture is being protected as code is generated faster.
Risk Area 3
Does AI-generated code stay within intended layers, services, and domain boundaries?
Architecture weakens when business logic, data access, integration logic, or UI behavior starts appearing in the wrong places simply because the generated code works.
Please select an answer before continuing.
Risk Area 3
Are AI-assisted changes reviewed for architectural fit, not just functional correctness?
A feature can work and still damage the system. AI-aware review needs to evaluate whether the implementation belongs where it was placed.
Please select an answer before continuing.
Risk Area 3
Are senior technical leaders involved when AI is used for larger or more complex code changes?
AI can make large changes appear easier than they are. Without architectural oversight, fast implementation can introduce long-term structural cost.
Please select an answer before continuing.
Risk Area 4
Maintainability: Will developers understand this code six months from now?
The most expensive AI-generated code is not always the code that fails immediately. It is often the code that works today but becomes difficult to modify, explain, debug, document, or safely extend later.
Risk Area 4
Is AI-generated code easy for other developers to read and modify later?
Maintainability depends on whether the next developer can understand the code quickly, not just whether the original developer got it working.
Please select an answer before continuing.
Risk Area 4
Does AI-generated code avoid unnecessary complexity or over-engineering?
AI can sometimes produce abstractions, helper functions, or patterns that appear sophisticated but make the system harder to reason about.
Please select an answer before continuing.
Risk Area 4
Is documentation keeping up with AI-assisted changes?
When code changes faster than documentation, diagrams, and shared understanding, the system becomes increasingly dependent on tribal knowledge.
Please select an answer before continuing.
Risk Area 5
Testing and Validation: Is faster code output matched by stronger protection?
As AI increases the speed of code creation, testing becomes even more important. If output rises faster than validation discipline, bugs, regressions, and hidden assumptions can accumulate throughout the system.
Risk Area 5
Is AI-generated code required to meet the same testing standards as human-written code?
AI speed is only useful if the system can safely absorb it. Testing should not become optional simply because code was generated quickly.
Please select an answer before continuing.
Risk Area 5
Are edge cases and failure paths tested when AI generates or modifies logic?
AI often handles the obvious path well. Many defects appear in boundary conditions, unexpected inputs, and integration scenarios.
Please select an answer before continuing.
Risk Area 5
Has test coverage improved as AI-assisted development has increased?
If code output increases but test coverage does not, technical debt can accumulate faster than the team can detect it.
Please select an answer before continuing.
Risk Area 6
Code Review and Oversight: Are reviews adapted for AI-generated code?
Traditional code review often focuses on whether the code works. AI-aware review must also ask whether the code fits the architecture, duplicates existing logic, weakens security, or increases cognitive load for future developers.
Risk Area 6
Does your code review process include AI-specific review questions?
AI-generated code should be reviewed for duplication, maintainability, architecture, security, explainability, and long-term ownership.
Please select an answer before continuing.
Risk Area 6
Are reviewers checking whether AI-generated code duplicates existing functionality?
AI may generate new logic instead of finding existing functionality. This can create hidden duplication and conflicting behavior over time.
Please select an answer before continuing.
Risk Area 6
Do reviewers have enough time and context to evaluate AI-assisted changes properly?
If AI increases the volume of code entering review, the review process itself must adapt or it becomes a bottleneck with less depth.
Please select an answer before continuing.
Risk Area 7
Emerging Symptoms: Are you already seeing the slowdown beneath the speed?
AI-driven technical debt is often invisible at first. Teams may feel faster while the codebase becomes harder to change. This section identifies early warning signs that short-term gains may be turning into long-term friction.
Risk Area 7
Are changes taking longer because developers must first untangle inconsistent or unclear code?
One of the first signs of technical debt is that even simple changes require more investigation, more caution, and more cleanup than expected.
Please select an answer before continuing.
Risk Area 7
Are bugs becoming harder to trace to their root cause?
When generated code introduces inconsistent patterns or hidden dependencies, debugging becomes slower and more dependent on individual knowledge.
Please select an answer before continuing.
Risk Area 7
Is onboarding new developers becoming harder because the system is less consistent?
A maintainable codebase teaches new developers how it works. A fragmented codebase forces them to learn exceptions, workarounds, and undocumented patterns.
Please select an answer before continuing.
Your Assessment Results
Where AI May Be Increasing Technical Debt
Enter your information below to receive a copy of the results, to better assist you in analyzing and speaking with your team. A copy will also be sent to our AI experts so if you choose to speak with us, our team will already have an understanding of where your codebase, architecture, review process, testing discipline, or development standards may need stronger AI guardrails.
Please complete all fields before submitting.
Thank you. Your AI Technical Debt Risk Assessment has been submitted and a copy has been sent to your email.

Why AI Is Slowing Down Your App—and How to Fix It

Where Latency Actually Comes From & The Solution

AI latency can quickly turn a promising feature into a frustrating user experience. This diagnostic helps software leaders identify where slow AI response times may be entering the application.

Read About The Solution

Or…take an instant diagnostic assessment to find out what may be slowing down your AI system!

AI Latency Diagnostic
Find Out What May Be Slowing Down Your AI System
AI latency is rarely caused by one thing. It may be coming from the model, the prompt, the retrieval layer, synchronous request handling, external APIs, or the way results are delivered to users.
This diagnostic helps you identify where delays may be entering your AI application so your team can better understand whether the issue is architectural, model-related, data-related, or tied to user experience design.
User Experience
Do users see useful progress while the AI is working?
Latency is not only about total response time. If users stare at a blank screen while the system waits for a full AI response, the application feels slower and less reliable.
Please select an answer before continuing.
User Experience
Have you defined acceptable response-time expectations for each AI workflow?
Not every AI feature needs the same latency target. A chatbot, document analysis tool, recommendation engine, and background summarization process may each need different expectations.
Please select an answer before continuing.
Request Architecture
Are long-running AI tasks separated from the user request cycle?
One of the most common causes of AI latency is forcing the user interface to wait while the system completes a long-running model call, retrieval process, or multi-step workflow.
Please select an answer before continuing.
Request Architecture
Can independent AI steps run in parallel instead of sequentially?
Many AI pipelines are slower than necessary because retrieval, preprocessing, scoring, validation, or multiple model calls are performed one after another even when some of those steps could run at the same time.
Please select an answer before continuing.
Model Strategy
Do you route simple and complex tasks to different models or execution paths?
Large models may be appropriate for complex reasoning or generation, but they are often unnecessary for classification, routing, extraction, tagging, or simple transformations.
Please select an answer before continuing.
Model Strategy
Do you measure latency by model, task type, and request size?
Average response time can hide the real issue. Teams need to know which models, workflows, and request types are creating the slowest user experiences.
Please select an answer before continuing.
Prompt and Payload Design
Are prompts and context payloads intentionally limited to what the task requires?
Oversized prompts increase processing time, cost, and variability. Many systems send too much context to the model because the retrieval and prompt design have not been tuned.
Please select an answer before continuing.
Prompt and Payload Design
Are repeated instructions, templates, or context blocks cached or standardized?
Many systems rebuild the same prompt structures repeatedly. Standardizing and caching reusable prompt components can reduce unnecessary processing and improve consistency.
Please select an answer before continuing.
Retrieval and Data Layer
Do you measure retrieval time before the model is called?
In RAG systems, the delay often starts before inference. Vector search, database calls, document filtering, reranking, and permissions checks can add significant latency.
Please select an answer before continuing.
Retrieval and Data Layer
Are embeddings, retrieval results, or frequent lookups cached where appropriate?
AI systems often repeat expensive retrieval work. Caching embeddings, common retrieval results, and repeated lookups can reduce latency without changing the user-facing feature.
Please select an answer before continuing.
Infrastructure and Observability
Do you have fallback plans when an external AI provider is slow or unavailable?
External LLM APIs introduce latency your team does not fully control. Strong systems plan for provider variability, timeout handling, retries, degraded responses, or alternate paths.
Please select an answer before continuing.
Infrastructure and Observability
Can your team see the full AI request path from user action to final response?
To fix latency, teams need visibility across the full chain: user request, application logic, retrieval, prompt construction, model call, validation, post-processing, and response delivery.
Please select an answer before continuing.
Your Results
What Your Answers Reveal About AI Latency
Enter your information below to receive a copy of the results, to better assist you in analyzing and speaking with your team. A copy will also be sent to our AI experts so if you choose to speak with us, our team will already have an understanding of where your AI system may be experiencing latency, architectural bottlenecks, retrieval delays, model routing issues, or response delivery challenges.
Please complete all fields before submitting.
Thank you. Your AI Latency Diagnostic has been submitted and a copy has been sent to your email.

The Scaling Problem That Kills Most AI Initiatives

The Hidden Shift From Capability to Reliability

A prototype proves that AI can work. Production requires that it must work—consistently, predictably, and at scale. That shift is not incremental. It’s architectural.

Read About The Solution

Or…take an instant diagnostic assessment to find out why your AI isn’t scaling!

AI Scaling System Assessment
Why Your AI Isn’t Scaling – System Assessment
This assessment is designed for teams that have already proven AI can work in a prototype, but now need to understand why it may be struggling under production conditions.

In a few minutes, you will identify where your AI system may be exposed across orchestration, latency, data readiness, cost behavior, guardrails, and production monitoring. The goal is not to grade your team. The goal is to give you a clearer view of what must be strengthened before the system can scale reliably.
System Layer 1
Orchestration: Is the AI workflow structured enough to survive production?
A prototype can often run as a simple chain of prompts and manual handoffs. Production requires a controlled workflow where each stage is visible, testable, and able to fail without taking down the entire experience.
System Layer 1
Is your AI workflow broken into clear stages rather than one opaque chain?
A scalable system separates retrieval, model interaction, validation, fallback handling, and downstream integration so each part can be tested and improved.
Please select an answer before continuing.
System Layer 1
Can individual stages fail without breaking the entire user experience?
Production systems need graceful failure. If one dependency breaks, the system should know whether to retry, fallback, escalate, or return a limited response.
Please select an answer before continuing.
System Layer 1
Can your team debug where a bad answer or failure originated?
If the team cannot isolate whether the issue came from input handling, retrieval, prompting, model behavior, validation, or integration, scaling will become difficult to support.
Please select an answer before continuing.
System Layer 2
Latency and Load: Will the experience still work when real users arrive?
AI systems often feel fast during a demo because traffic is low and expectations are forgiving. Under real usage, stacked model calls, retrieval steps, APIs, and validation layers can create unacceptable delays.
System Layer 2
Do you know how many model calls are made for a typical user request?
Every model call adds time, cost, and variability. Many AI systems struggle because prototype workflows quietly become multi-call production workflows.
Please select an answer before continuing.
System Layer 2
Have you defined acceptable response-time targets for production use?
Without clear latency expectations, teams often discover too late that what was acceptable in a demo feels slow or unreliable to actual users.
Please select an answer before continuing.
System Layer 2
Are you using caching, batching, parallel processing, or model selection to control performance?
Scaling often requires architectural choices that reduce repeated work, avoid unnecessary model calls, and keep the experience responsive under load.
Please select an answer before continuing.
System Layer 3
Data Readiness: Is your AI grounded in production-quality information?
Prototypes often rely on curated examples. Production AI has to deal with inconsistent records, changing business meaning, missing fields, stale knowledge, and legacy systems that were never designed for AI.
System Layer 3
Has the system been tested against messy, incomplete, or unexpected production inputs?
AI prototypes often pass clean examples. Production users and systems introduce ambiguity, missing context, inconsistent formatting, and edge cases.
Please select an answer before continuing.
System Layer 3
Can the system trace important AI responses back to trusted data sources?
When an answer matters, the organization needs to know what information shaped it and whether that information was current, authorized, and reliable.
Please select an answer before continuing.
System Layer 3
Do you validate or normalize inputs before they reach the AI layer?
The model should not be expected to compensate for every data quality issue. Strong systems improve the inputs before asking AI to reason over them.
Please select an answer before continuing.
System Layer 4
Guardrails and Validation: What prevents a bad output from becoming a business problem?
In a prototype, a person can review the answer. In production, the system needs controls that validate outputs, catch failure patterns, and define what happens when the AI is uncertain or wrong.
System Layer 4
Are AI outputs validated before they are shown to users or passed into downstream systems?
Raw model output should be treated as a candidate response. Validation helps prevent unsupported answers, malformed data, or risky recommendations from moving forward.
Please select an answer before continuing.
System Layer 4
Does the system know when to refuse, escalate, or provide a limited response?
A scalable AI system needs defined boundaries. It should not attempt to answer everything simply because the model can generate a response.
Please select an answer before continuing.
System Layer 4
Are business rules enforced by application logic rather than left to the model alone?
Models are not a substitute for deterministic business rules, security controls, compliance requirements, or workflow logic.
Please select an answer before continuing.
System Layer 5
Operations: Can the system be monitored, improved, and supported over time?
AI systems change as usage, data, models, prompts, and user expectations change. Scaling requires observability, feedback loops, cost tracking, and ownership after launch.
System Layer 5
Are you monitoring cost per request, token usage, latency, and failure patterns?
Production AI needs operational visibility. Without it, cost and performance issues can grow quietly until they become budget or user-experience problems.
Please select an answer before continuing.
System Layer 5
Do you review real user interactions to improve prompts, retrieval, validation, and workflows?
AI systems improve through feedback. Teams need a repeatable process for learning from real production behavior.
Please select an answer before continuing.
System Layer 5
Is there clear ownership for maintaining the AI system after launch?
Scaling fails when responsibility is unclear. Production AI needs owners for performance, reliability, data quality, governance, and user trust.
Please select an answer before continuing.
Your Assessment Results
Where Your AI System May Be Struggling to Scale
Enter your information below to receive a copy of the results, to better assist you in analyzing and speaking with your team. A copy will also be sent to our AI experts so if you choose to speak with us, our team will already have an understanding of where your AI system may need stronger architecture, orchestration, validation, performance controls, or production readiness planning.
Please complete all fields before submitting.
Thank you. Your AI Scaling System Assessment has been submitted and a copy has been sent to your email.

Why Your AI Is Giving Wrong Answers in Production (And How to Fix It)

Most AI Systems Don’t Fail in the Demo

At the root of most of these problems is a design pattern we see repeatedly: the model is doing too much, and the system around it is doing too little.

Read About The Solution

Or…take an instant diagnostic assessment of the ten questions every system should pass before you trust it in production!

AI Reliability Diagnostic
10 Questions Every System Should Pass Before You Trust It in Production
This step-by-step diagnostic is designed to help you identify where your AI system may be relying too heavily on the model—and not enough on structure, validation, guardrails, and fallback paths.
Question 1 of 10
Have you clearly defined what the AI is allowed to do?
Reliable systems narrow the scope. If the AI is simply expected to answer anything, variability and risk increase quickly.
Please select an answer before continuing.
Question 2 of 10
Is the AI grounded in trusted, verifiable data?
AI without grounding will guess. In production, every important answer should come from known, reliable, and traceable data sources.
Please select an answer before continuing.
Question 3 of 10
Do you validate AI outputs before they reach the user?
The first AI response should be treated as a candidate answer, not a final answer. Validation helps prevent unsupported or incomplete responses from reaching users.
Please select an answer before continuing.
Question 4 of 10
Can your system detect when the AI is likely wrong?
Most AI failures do not throw errors. They produce confident-sounding responses that may be incomplete, unsupported, or incorrect.
Please select an answer before continuing.
Question 5 of 10
Do you have clear fallback paths when AI fails?
Every AI system will fail at some point. Strong systems know when to say they do not know, escalate to a human, or provide a safer limited response.
Please select an answer before continuing.
Question 6 of 10
Are you logging more than just API calls?
Logging requests is not enough. You need visibility into inputs, outputs, context, retrieval quality, validation failures, and user feedback.
Please select an answer before continuing.
Question 7 of 10
Is responsibility clearly divided between AI and the system?
AI should handle language and reasoning, while application code enforces business logic and data systems provide truth.
Please select an answer before continuing.
Question 8 of 10
Have you tested the system with real-world inputs, not just ideal ones?
Production users will submit unclear, messy, incomplete, and unexpected inputs. Reliability depends on testing against that reality.
Please select an answer before continuing.
Question 9 of 10
Are outputs consistent across repeated requests?
Some variability is expected, but unmanaged inconsistency damages user trust and makes the system difficult to support.
Please select an answer before continuing.
Question 10 of 10
Do you continuously monitor and improve the system?
AI is not a set-it-and-forget-it capability. It needs feedback loops, review processes, and ongoing refinement.
Please select an answer before continuing.
Your Results
What Your Answers Reveal
Enter your information below to receive a copy of the results, to better assist you in analyzing and speaking with your team. A copy will also be sent to our AI experts so if you choose to speak with us, our team will already have an understanding of where your AI system may need stronger guardrails, validation, observability, or fallback information.
Please complete all fields before submitting.
Thank you. Your AI Reliability Diagnostic has been submitted.
Minnesota’s Leading Software Consulting Company Addresses the Top Concerns of Technology Decision-Makers

Intertech is Minnesota’s leading software development consulting firm, providing experienced onshore consultants who help businesses assess and modernize outdated software systems. Our proven team delivers clear direction and answers the questions that keep you up at night.

Common Search Series

How do we add AI to our software without risking the business?

Author: Intertech’s Consulting Research Lab

As AI capabilities begin appearing across modern software products, many leadership teams are asking how these features can realistically be added to their own platforms. Copilots, intelligent search, and predictive insights are quickly becoming expected by users. But integrating AI into an existing application requires careful architecture, data readiness, and thoughtful product design. Intertech’s senior AI consultants help organizations evaluate where AI creates real value and integrate those capabilities into existing systems without destabilizing the platform.

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Common Search Series

Do we need AI engineers or can our developers learn it?

Author: Intertech’s Consulting Research Lab

AI introduces new concepts such as embeddings, vector databases, and model orchestration that many development teams have not yet encountered. As organizations explore AI adoption, leaders must decide whether to hire new specialists or develop the skills of their current teams. Intertech’s senior AI consultants help organizations implement AI systems while mentoring internal developers, allowing teams to build lasting capability without replacing the talent they already have.

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Common Search Series

How do we modernize legacy systems for AI?

Author: Intertech’s Consulting Research Lab

Many organizations rely on software platforms built long before AI integration became practical. These systems often contain tightly coupled architectures, outdated APIs, and fragmented data structures that limit new capabilities. When teams attempt to introduce AI, they quickly discover the platform cannot support modern workflows. Intertech’s senior architects and AI consultants help organizations modernize systems step by step, preparing legacy platforms to support new AI capabilities without requiring a full rewrite.

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Common Search Series

How do we govern AI use in software development?

Author: Intertech’s Consulting Research Lab

AI coding assistants are rapidly becoming part of everyday development workflows. While these tools can accelerate productivity, they also introduce new concerns around code quality, security, and architectural consistency. Engineering leaders are increasingly looking for ways to introduce guardrails around AI-assisted development. Intertech’s senior consultants help organizations establish practical governance models that allow teams to benefit from AI tools while protecting software quality and long-term maintainability.

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Common Search Series

How do we prepare our data for AI?

Author: Intertech’s Consulting Research Lab

Many organizations discover that the biggest barrier to AI adoption is not the models themselves but the condition of their data. Information is often scattered across legacy systems, SaaS platforms, and disconnected databases. Without strong data foundations, AI initiatives quickly stall. Intertech’s senior AI consultants help organizations assess their data environment, integrate key sources, and create the architecture needed to support reliable AI applications.

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Common Search Series

Why did our AI project fail?

Author: Intertech’s Consulting Research Lab

Many organizations have already experimented with AI through pilots such as chatbots, document analysis tools, or internal assistants. Yet a large number of these initiatives stall before reaching production. In most cases, failure is not caused by the technology itself but by unclear use cases, weak data foundations, or architectural limitations. Intertech’s senior AI consultants help diagnose why projects stalled and transform early experiments into reliable, production-ready AI systems.

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Transforming Your Development Process Through The Human Use of AI Agents!

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Product Overview
Where AI falls short, Unifi by Intertech™ bridges the gaps, adding targeted agents and human guidance, exactly where work gets stuck—triage, scaffolding, PR checks, and integration with other systems and outside organizations. If you want AI that helps you ship faster, modernize cleanly, and control spend, without the AI hype, contact us and let’s map it out together.
Unifi by Intertech - 1

Custom Software Development

Custom Software Development Consulting tailored to your business goals. Intertech’s Minnesota-based senior developers design, build, and deploy secure, scalable, and high-performance software that drives measurable results and long-term value.

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Web App & Feature Development

Building dynamic, user-friendly, and secure web applications that enhance user experience and streamline business operations. As a Minnesota software consulting firm, Intertech delivers web solutions that scale, perform, and integrate seamlessly with your existing systems.

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Mobile Application Development

Designing and developing responsive, high-performance mobile applications for iOS and Android. Intertech’s onshore consultants in Minnesota create mobile apps that connect customers, boost engagement, and extend your digital capabilities.

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Cloud Consulting and Migration

Helping businesses transition to and thrive in the cloud. Intertech’s cloud consulting and migration experts—based in Minnesota—optimize infrastructure, ensure security, and manage data across AWS, Azure, and hybrid environments to deliver scalability and cost efficiency.

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Summary Of All Software Consulting Services

System Integration

Ensuring your systems and applications work together seamlessly. Intertech’s software integration consulting connects critical platforms and automates workflows, improving performance and collaboration across your business.

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Software Architecture and Design

Creating scalable, maintainable, and future-ready architectures. Intertech’s senior software architects deliver solutions that adapt to evolving business needs while ensuring reliability, performance, and long-term success.

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AI and Machine Learning

Implementing AI-driven software consulting solutions that automate processes, deliver predictive insights, and personalize user experiences. Powered by Unifi by Intertech™, our proprietary agentic AI system, Intertech helps Minnesota and national clients modernize development workflows, enhance decision-making, and improve efficiency through applied machine learning and intelligent automation.

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DevOps, Agile & CI/CD

Streamlining software delivery through proven DevOps and Agile consulting. Intertech’s Minnesota-based engineers design CI/CD pipelines that accelerate releases, improve collaboration, and ensure fast, reliable software updates from concept to deployment.

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Proudly serving Minnesota, the Twin Cities, and clients nationwide since 1991.

According to a recent Standish Group CHAOS report, more than 66% of all software projects begin by focusing on the wrong things—solutions you thought you needed but didn’t, and fail. At Intertech, we help you identify these unknowns and establish a clear, agile track before you invest in something that may become obsolete before completion.

“Intertech’s unwavering dedication to the project caught me off guard, to the extent that it no longer feels like a consulting arrangement but rather as if they are an intrinsic part of our team.”

Database Administrator | Brunswick – Mercury Marine

Enterprise Software Solutions from Minnesota to Your Industry!

Automating manual, repetitive tasks in industries where accuracy is critical can mean the difference between project delays and being ahead of schedule. Intertech’s on-demand software development consulting helps organizations streamline operations, boost precision, and deliver measurable efficiency gains.

More Success Stories

Custom-Assembled Maintenance Forecast Platform

Before contacting Intertech, a leading energy consultancy and operations management company spent millions of dollars per year on third-party software to run a system of forecasting and diagnostic tools.

Proof of Concept & Implementation of Modern Machine Interface

Demonstrated a new way to make the machines more intuitive and usable to the parent company.

New Graphical User Interface (GUI)

To keep up with their competitors, the client needed to update their management system.

GET TO KNOW US

Client Relationship Manager

Brady Barthold

As a Senior Software Solutions Specialist, Brady provides software development services to Fortune 500, small, and medium-sized companies, helping each add features, modernize end-of-life software, and introduce AI agents to help cut costs. He completed his bachelor of arts degree from the University of St. Thomas and has a track record helping others succeed.

 

“Work smart & hard is my philosophy, and never stop learning.”

 

How Has The Transition To The Consulting Side Of Intertech Been For You?

I enjoyed my time working with the senior consultants within Intertech and I feel I will be able to contribute more to my client’s projects as a consulting specialist, especially with the knowledge I have gained working with different teams and getting that real-world expertise prior to representing the team.

Sideline

Brady enjoys playing hockey and golf and spending time with his family & friends. He also enjoys coaching youth hockey in his free time. Brady has volunteered for several years at Open Arms of Minnesota. He has one fur kid, a chocolate labrador named Bosco.

Client’s, Past Customers, and Friends… Refer A Project and Reap The Benefits For Your Company.

Refer us to an acquaintance, colleague, or former colleague who can use one or more of our consultant’s to help with a project within their organization and when we have a face-to-face meeting with them (online or in-person), we’ll either credit the project or send you a visa cash card (your choice) as a thank you for your stamp of approval.
Referrals - Monetary Rewards & Details

Accurate Quotes. Detailed Options.

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Located In Minnesota. Helping The World.

If you are looking for a Midwest hard-working group of software-developing consultants, you have come to the right place. Centrally located between St. Paul and Minneapolis and in the heart of Eagan, Minnesota, we are at your service.

Give us a call — +651. 288. 7000 — and we’ll meet you in the lobby by the fireplace with a cup of coffee and the information you need to modernize or pivot to new technology successfully.

860 Blue Gentian Road, Suite 200, Eagan, MN 55121-1567

Mercury Marine Trusts Intertech — You Can Too!
Intertech Headquarters

Intertech Philanthropy Is Making a difference in Minnesota!

Grants and scholarships. Volunteerism. Employee matching gifts. These are just some of the ways Intertech gives back to the communities where we live, work and serve our customers.

Since the founding in 2003, our Intertech Foundation has provided financial support to over 100 families with terminally ill children, working closely with Ronald McDonald House Charities, The Crescent Cove Respite & Hospice Home for Kids, and other organizations to provide funds to families in need. As a technology company, we are also interested in lending our support through our STEM scholarship program, focused on young people with interest in computer science.

The Intertech Foundation is a non-profit 501(c)(3) private foundation, formed by Tom and Linda Salonek.

2026 STEM Scholarship