AI Consultant · Chicago, IL

Hire an AI consultant in Chicago.

A COO at Boeing, Caterpillar, Hyatt, or United Airlines who wants to cut operational costs with AI gets three vendor proposals with wildly different approaches and no framework for evaluating them independently. Each vendor says their approach is best. Each vendor is selling something.

The independent consultant's job is to evaluate those proposals objectively, identify what vendors are not telling the client, and recommend the right path. The 5-to-8-week engagement produces a ranked use case list, a build vs. buy recommendation for the top three, and a 90-day roadmap. Chicago's law firms need the same independent evaluation for document automation AI, where vendors oversell capabilities that do not hold up in specific practice area workflows.

Fixed engagement, scoped to your needs. Five to eight weeks. Written deliverables yours to keep.

How we work

Tell us about your AI decision.

The vendor evaluation problem.

Chicago's large enterprises, Boeing, Caterpillar, Hyatt, United Airlines, and Kraft Heinz, have AI mandates from the C-suite. The board wants AI progress. The operations team has a problem list. Vendors have pitched solutions to both. The problem is that none of the three groups has the same information set, and the vendors have the most information advantage.

A vendor's demo is built to show their product at its best. The accuracy metrics in the proposal are measured on data that resembles the vendor's training data, not your production environment. The implementation timeline assumes a clean integration path that your legacy systems will not provide. These gaps between the vendor pitch and production reality are not lies. They are the natural result of a sales process.

Chicago's legal sector faces a version of this problem specific to document automation. Loop Capital, Sidley Austin, and dozens of Loop-area law firms have looked at AI contract review tools. The vendor demos are compelling. The reality in production is that most AI contract review tools perform well on standard commercial agreements and poorly on the specialized agreements in specific practice areas. A firm that deploys an AI tool for M&A due diligence review and discovers it misses critical provisions in specialized representations and warranties clauses has a problem that the vendor demo did not reveal.

The independent consultant's evaluation asks: what happens when you run this tool on your actual documents, not the demo dataset? That test takes a week. It saves six months of a failed deployment.

What an independent assessment changes.

An independent assessment gives you a consistent evaluation framework applied to every vendor and every use case. The criteria do not change between vendor A and vendor B. The accuracy claims are tested against your data. The integration complexity is estimated by someone who does not benefit from underestimating it.

For Chicago enterprises receiving multiple AI proposals, that consistency is the primary value. Three vendors with three different approaches to the same problem cannot all be evaluated accurately by a team that is hearing about AI for the first time in the context of those proposals. An independent consultant who has evaluated similar proposals for comparable companies provides a reference class the internal team does not have.

The other thing an independent assessment changes: the permission to recommend doing nothing. An internal team tasked with selecting an AI vendor has a structural incentive to select one. An independent consultant who recommends that none of the three proposals make sense and that the right move is to wait 18 months until the company's data infrastructure supports AI deployment can make that recommendation without career risk.

What the engagement produces.

Ranked use case list with scoring rationale

Every AI use case your organization has under consideration, scored on ROI, feasibility, data readiness, and integration complexity. The reasoning behind each score is written out so you can challenge it.

Build vs. buy recommendation for the top three

For each of your top three use cases: what it costs to build, what off-the-shelf vendors charge, which path makes more sense, and why. With real cost estimates attached.

Vendor evaluation matrix

A structured comparison of the vendors relevant to your use cases, evaluated against your specific requirements. Includes a section on what each vendor's proposal does not address.

90-day implementation roadmap

A step-by-step plan for implementing your top one or two use cases. Clear enough that any development team can follow it, whether you engage us or another team for the build.

How we work.

1
Discovery call
One hour. We understand the AI decisions you are trying to make, the vendor proposals you have received, and the internal stakeholders involved. We scope the engagement and give you a fixed price.
2
Structured interviews
Sessions with operations, IT, and business stakeholders. We review any vendor proposals you have received and assess your data readiness for the use cases under consideration.
3
Analysis and drafting
Use case scoring, vendor evaluation, build vs. buy analysis, and a 90-day roadmap. We test vendor accuracy claims against your data where possible. Draft delivered for your review.
4
Review and handoff
Final session with your team and any relevant stakeholders. All deliverables are yours. No retainer required.

Questions about AI consulting in Chicago.

How do you evaluate an AI vendor's accuracy claims independently, and what do you ask for beyond the demo?: Ask for a confusion matrix on a held-out test set that matches your production data distribution, not accuracy metrics measured on training data or a curated demo dataset. Ask for false positive and false negative rates separately. For a Chicago manufacturer evaluating an AI quality inspection system, the relevant number is not overall accuracy but the rate at which the system misses defective parts, and whether that rate is better or worse than your current human inspection process. Ask whether the test set was collected from operations similar to yours, and ask for the names of two customers you can call who use the product in a production environment comparable to yours. A vendor who cannot provide a reachable reference customer is a vendor whose accuracy claims are unverified.
What does a build vs. buy analysis look like for a large Chicago manufacturer with complex operational data?: The analysis starts with a data readiness assessment. Most AI build decisions fail not because the technology is wrong but because the operational data the AI would need is insufficiently structured, labeled, or accessible. For a manufacturer like Caterpillar or Boeing, that means reviewing whether equipment sensor data is captured in a format that AI models can consume, whether historical failure data has the labels necessary for supervised learning, and whether the data infrastructure can support near-real-time inference. If data readiness is low, the build vs. buy question is premature. Once data readiness is confirmed, the analysis compares off-the-shelf industrial AI platforms against custom development on five dimensions: capability fit, integration cost, vendor lock-in risk, ongoing maintenance burden, and total cost over three years.
How do you handle the situation where the right recommendation is to tell the client not to build anything yet?: We make that recommendation when it is the right answer, and we make it in writing with the reasoning behind it. The most common scenario is a company that has been sold on an AI use case but does not have the data infrastructure to support it. Building the model without the data pipeline is building on a foundation that does not exist. The honest recommendation is to invest in data infrastructure first and revisit AI in 12 to 18 months. A second common scenario: the problem the AI is intended to solve is currently solved by a process that works adequately, and the ROI case for AI does not justify the investment. That answer does not generate any revenue for us, which is exactly why it is credible.
What is the difference between hiring an AI consultant and hiring an AI vendor to do a discovery workshop?: An AI vendor's discovery workshop is a sales process. The output is a proposal for the vendor's product. The vendor's incentive is to find use cases that fit their technology, not to find the use cases with the highest ROI for your business. An independent consultant has no product to sell. The output is a recommendation that may include using that vendor's product, a competitor's product, an open-source solution, or no AI at all. For Chicago enterprises receiving multiple vendor proposals simultaneously, the independent consultant's role is to evaluate those proposals against a consistent framework and tell you what the vendors are not telling you about the gaps between their demo and your production environment.

Facing an AI decision you want an independent view on?

Hire an AI consultant in Chicago.

Fixed engagement, scoped to your needs. Five to eight weeks. Written deliverables yours to keep.

The vendor evaluation problem.

The independent consultant's evaluation asks: what happens when you run this tool on your actual documents, not the demo dataset? That test takes a week. It saves six months of a failed deployment.

What an independent assessment changes.

What the engagement produces.

Ranked use case list with scoring rationale

Build vs. buy recommendation for the top three

For each of your top three use cases: what it costs to build, what off-the-shelf vendors charge, which path makes more sense, and why. With real cost estimates attached.

Vendor evaluation matrix

A structured comparison of the vendors relevant to your use cases, evaluated against your specific requirements. Includes a section on what each vendor's proposal does not address.

90-day implementation roadmap

A step-by-step plan for implementing your top one or two use cases. Clear enough that any development team can follow it, whether you engage us or another team for the build.

How we work.

Discovery call

One hour. We understand the AI decisions you are trying to make, the vendor proposals you have received, and the internal stakeholders involved. We scope the engagement and give you a fixed price.

Structured interviews

Sessions with operations, IT, and business stakeholders. We review any vendor proposals you have received and assess your data readiness for the use cases under consideration.

Analysis and drafting

Use case scoring, vendor evaluation, build vs. buy analysis, and a 90-day roadmap. We test vendor accuracy claims against your data where possible. Draft delivered for your review.

Review and handoff

Final session with your team and any relevant stakeholders. All deliverables are yours. No retainer required.

Questions about AI consulting in Chicago.

How do you evaluate an AI vendor's accuracy claims independently, and what do you ask for beyond the demo?

Ask for a confusion matrix on a held-out test set that matches your production data distribution, not accuracy metrics measured on training data or a curated demo dataset. Ask for false positive and false negative rates separately. For a Chicago manufacturer evaluating an AI quality inspection system, the relevant number is not overall accuracy but the rate at which the system misses defective parts, and whether that rate is better or worse than your current human inspection process. Ask whether the test set was collected from operations similar to yours, and ask for the names of two customers you can call who use the product in a production environment comparable to yours. A vendor who cannot provide a reachable reference customer is a vendor whose accuracy claims are unverified.

What does a build vs. buy analysis look like for a large Chicago manufacturer with complex operational data?

The analysis starts with a data readiness assessment. Most AI build decisions fail not because the technology is wrong but because the operational data the AI would need is insufficiently structured, labeled, or accessible. For a manufacturer like Caterpillar or Boeing, that means reviewing whether equipment sensor data is captured in a format that AI models can consume, whether historical failure data has the labels necessary for supervised learning, and whether the data infrastructure can support near-real-time inference. If data readiness is low, the build vs. buy question is premature. Once data readiness is confirmed, the analysis compares off-the-shelf industrial AI platforms against custom development on five dimensions: capability fit, integration cost, vendor lock-in risk, ongoing maintenance burden, and total cost over three years.

How do you handle the situation where the right recommendation is to tell the client not to build anything yet?

We make that recommendation when it is the right answer, and we make it in writing with the reasoning behind it. The most common scenario is a company that has been sold on an AI use case but does not have the data infrastructure to support it. Building the model without the data pipeline is building on a foundation that does not exist. The honest recommendation is to invest in data infrastructure first and revisit AI in 12 to 18 months. A second common scenario: the problem the AI is intended to solve is currently solved by a process that works adequately, and the ROI case for AI does not justify the investment. That answer does not generate any revenue for us, which is exactly why it is credible.

What is the difference between hiring an AI consultant and hiring an AI vendor to do a discovery workshop?

An AI vendor's discovery workshop is a sales process. The output is a proposal for the vendor's product. The vendor's incentive is to find use cases that fit their technology, not to find the use cases with the highest ROI for your business. An independent consultant has no product to sell. The output is a recommendation that may include using that vendor's product, a competitor's product, an open-source solution, or no AI at all. For Chicago enterprises receiving multiple vendor proposals simultaneously, the independent consultant's role is to evaluate those proposals against a consistent framework and tell you what the vendors are not telling you about the gaps between their demo and your production environment.

Facing an AI decision you want an independent view on?