Chicago · Enterprise AI

Enterprise AI for Chicago's financial, legal and insurance core

Chicago runs on regulated, document-heavy work: trading, custody, underwriting, litigation, drug development. I build the agentic workflows, retrieval systems and private AI infrastructure that take hours out of that work, with a KPI agreed on day one.

Talk about your Chicago team Industries I work with

30Fortune 500 headquarters in the Chicago market, 2026 Source

$725.7BReal GDP of the Chicago metro in 2023, chained 2017 dollars (from the chart data below) Source

9.43MResidents of the Chicago-Naperville-Elgin metro, July 2025 Source

Chicago is one of the largest corporate economies in the country. The metro's real output was $725.7 billion in 2023, about 9.4 million people live here, and RealPage counts 30 Fortune 500 headquarters in the Chicago market for 2026. It's also a mature economy: real GDP grew 5.0% from 2018 to 2023 by the BEA figures in the chart below. In a market like that, AI doesn't earn its budget by opening new territory. It earns it by making expensive, skilled people faster at the work they already do, and by doing that without creating new risk.

That's the lens I bring. I've spent 20 years in sales, paid acquisition, marketing technology, conversion optimization and analytics, including more than $50M in managed ad spend, so I measure AI the way a CFO would: cost per case, hours per matter, time to decision. I design and build agentic workflows, RAG and knowledge systems, and private or on-prem AI infrastructure, and I handle the security and compliance work that Chicago's regulators, auditors and general counsels will ask about.

Chicago's industries, and where AI moves the number.

The sectors that anchor the Chicago economy, and the specific work I do for each.

01 / Industry

Banking, trading & asset servicing

Chicago is where much of the country's risk gets priced and cleared. CME Group and Northern Trust are both headquartered in the city, and the operations behind derivatives clearing, custody and wealth management run on dense rulebooks, client agreements and exception queues.

How I help

I build retrieval systems that let operations, client service and compliance staff query rulebooks, fund documents and internal procedures and get answers with citations they can defend in an audit. I also build agents that triage operational exceptions, such as failed trades, reconciliation breaks and client inquiries, and draft a resolution for a human to approve. Where data can't leave the firm, models run on private infrastructure. The KPIs are exceptions cleared per analyst per day, client inquiry turnaround and escalation rate.

Enterprise AI for Financial Services

02 / Industry

Insurance

The Chicago area is a major insurance center. Allstate is headquartered in the northern suburbs, and Kemper, Old Republic and Health Care Service Corporation are headquartered in Chicago. Underwriting and claims both depend on reading long, inconsistent documents quickly and accurately.

How I help

I build document processing that reads submissions, loss runs, medical records and claim files and extracts the fields an underwriter or adjuster needs, with every value linked back to its source page. On top of that I build agents that assemble the file, check it against guidelines and route it, leaving the judgment call to your people. The KPIs are submission-to-quote time, claim cycle time and the share of files that need rework.

Enterprise AI for Insurance & Underwriting

03 / Industry

Legal

Chicago is a major legal market. Kirkland & Ellis, Sidley Austin, Mayer Brown and Baker McKenzie are all based here, alongside the legal departments of the city's corporate headquarters. Privilege and confidentiality rule out most off-the-shelf AI deployments.

How I help

I build knowledge systems over a firm's own precedent, clause libraries and matter files, deployed privately with matter-level access controls so ethical walls hold. Typical use cases are first-pass contract review against a playbook, due diligence extraction across data rooms, and research assistants that cite the exact document and page. The KPIs are hours per diligence review, first-draft turnaround and write-offs on routine work.

Enterprise AI for Legal & Law Firms

04 / Industry

Life sciences & pharmacy

The north suburbs hold a heavy concentration of healthcare companies: Abbott and AbbVie in Lake County, and Baxter International and Walgreens in Deerfield. Their regulatory, quality and pharmacovigilance functions produce and consume enormous volumes of controlled documents.

How I help

I build retrieval systems over SOPs, validation records and regulatory correspondence so quality and regulatory teams find the controlling answer fast, with a citation. I build intake agents that read adverse event and complaint reports, classify them and pre-fill case records for a reviewer, with the audit trail regulated work requires. The KPIs are case processing time, backlog size and first-pass accuracy against your reviewers.

HIPAA-Compliant Enterprise AI for Healthcare

Chicago by the numbers.

Public data on the size and direction of the Chicago economy.

Chicago metro real GDPReal GDP of the Chicago-Naperville-Elgin, IL-IN-WI MSA, billions of chained 2017 dollars, 2018 to 2023

Show the numbers

Year	$B
2018	691.2
2019	697.5
2020	659.6
2021	698
2022	715.6
2023	725.7

Source: U.S. Bureau of Economic Analysis via FRED. FRED series RGMP16980 (millions of chained 2017 dollars, annual), converted to billions and rounded to one decimal. FRED marks this series as discontinued; 2023 is the latest value published.

Chicago metro nonfarm jobsTotal nonfarm employment in the Chicago-Naperville-Elgin, IL-IN MSA, annual average, thousands, 2020 to 2025

Show the numbers

Year	K jobs
2020	4,345.5
2021	4,445.2
2022	4,629.2
2023	4,705.8
2024	4,727.5
2025	4,754.2

Source: U.S. Bureau of Labor Statistics via FRED. FRED series CHIC917NA (BLS State and Area CES, monthly, seasonally adjusted, thousands). Annual values are the average of the 12 monthly values for each year, computed by us and rounded to one decimal.

Chicago's AI research base is substantial. The University of Chicago's Data Science Institute runs interdisciplinary data science and AI research, education and industry partnerships. The University of Illinois System's Discovery Partners Institute bought a permanent headquarters at 250 S. Wacker Drive, a building of nearly 245,000 square feet, to focus on AI, quantum and advanced computing and to connect that research to finance, healthcare, manufacturing and energy companies. In the southwest suburbs, Argonne National Laboratory operates Aurora, its exascale supercomputer built for both simulation and AI and machine learning workloads.

For a Chicago enterprise, the constraint usually isn't access to talent or ideas. It's the distance between a pilot that impressed a steering committee and a production system that risk, legal and IT will all put their names to. That's the gap I'm hired to close.

I'm based in Minnesota, an easy flight from O'Hare or Midway, and I work with Chicago companies remote-first. The build runs remotely with a working demo every week. I come on-site where being in the room pays for itself: the kickoff, the workshops where we map the process with the people who actually do the work, and the launch. Every engagement is fixed scope and fixed price, tied to a KPI we agree on before any code is written, so your team can see week by week whether the number is moving.

Questions from Chicago teams.

Do you come to Chicago, or is everything remote?

Both. Day-to-day build work is remote with weekly demos. I come on-site for the sessions where it matters: kickoff, process workshops with your operators and the production launch. Minneapolis to Chicago is a short flight, so scheduling is easy.

Can you build AI for a law firm without exposing privileged material?

Yes. I deploy privately, inside your cloud tenant or on-prem, with matter-level permissions so ethical walls carry through to the AI system. Documents aren't sent to third-party services unless you approve it, and every answer cites its source.

We're an insurer with legacy policy and claims systems. Do we need to modernize first?

Usually not. Most of the value sits in the documents and queues around those systems. I build on what you have, read from the core systems where access allows, and scope the work so it pays back without waiting for a platform migration.

How do you prove the system is working?

We pick one KPI on day one, such as cycle time, cost per case or hours per matter, and measure the baseline before building. Weekly demos show progress against it, and I set up evaluation so accuracy is measured against your own reviewers, not a vendor benchmark.

Put AI to work in Chicago, tied to a number.

Tell me the process or the problem. You'll get a straight answer on what it's worth and what it would take.

Let's talk

Enterprise AI for Chicago's financial, legal and insurance core

Chicago's industries, and where AI moves the number.

Banking, trading & asset servicing

Insurance

Legal

Life sciences & pharmacy

Chicago by the numbers.

Questions from Chicago teams.

Put AI to work in Chicago, tied to a number.

Other markets I work in.

Sources