AI & Machine Learning Company in Chicago

A scoped AI proof of concept at Chicago rates runs USD $50,000 to $110,000 over six to ten weeks, covering data audit, a baseline model, and a hosted demo. A custom production ML model (fraud scoring, claim image triage, churn prediction, document extraction) typically lands at USD $140,000 to $350,000 including MLOps, monitoring, and a NIST AI RMF-aligned model card. Full production AI systems with RAG, multiple models, fine-tuning, and enterprise integrations to Salesforce, Workday, Epic, or Guidewire range from USD $300,000 to $1.5M. Chicago rates sit below the Bay Area and NYC but above many Midwest peers because of the trading-firm and Tempus talent premium. We give fixed-fee proposals in USD rather than open time-and-materials estimates.

The Illinois Biometric Information Privacy Act (740 ILCS 14) is the single biggest legal consideration for AI in Illinois. Any system that captures, stores, or uses biometric identifiers (facial geometry, voiceprints, fingerprints, retinal scans, hand geometry) or biometric information derived from them must obtain written informed consent before capture, publish a retention and destruction schedule, refrain from selling or profiting from biometric data, and apply reasonable storage standards. Damages are $1,000 per negligent and $5,000 per intentional violation, settled in the hundreds of millions against Meta, TikTok, and Google. For AI builds this is especially consequential for facial recognition, drive-thru voice ordering (McDonald's faced direct BIPA exposure on its voice AI pilot), HR video interview screening, security camera analytics, and any model that fingerprints behavioural patterns granularly enough to identify a specific person. Our discovery includes a BIPA exposure memo your general counsel can sign off.

Yes, with realistic scoping. We do not pretend to replace Citadel, Jump, or DRW research desks, but we build the infrastructure layer most trading-adjacent fintechs actually need, including feature pipelines on tick and order-book data, reinforcement learning research harnesses, execution model evaluation frameworks, model risk documentation aligned with SR 11-7 for bank-regulated affiliates, and on-prem or colo GPU deployments at Equinix CH1 through CH4 to keep inference inside the latency budgets that matter. We respect the IP sensitivity of trading research, ship under strict NDA, default to self-hosted open-weight models rather than closed APIs that exfiltrate prompts, and our engineers come from quant-adjacent backgrounds where appropriate. We have shipped against the latency, compliance, and security expectations Chicago trading firms set.

The Illinois Artificial Intelligence Video Interview Act (820 ILCS 42) requires employers using AI to analyse video interviews to notify each applicant before the interview, explain how the AI works and what characteristics it uses, obtain consent, limit sharing of the video, and destroy videos on request within thirty days. We build HR-tech AI under these constraints when serving Chicago HR-tech platforms (Hireology, Paylocity adjacent vendors, applicant tracking systems used by Lettuce Entertain You, Hyatt, or United Airlines). That means a consent-capture step before recording, a plain-language explanation surface, role-based access controls on stored video, automated destruction workflows, and an audit log proving compliance. When BIPA overlaps (and it usually does, because facial geometry analysis triggers both statutes) we apply the stricter of the two regimes by default.

When a project requires genuine research (novel model architectures, exascale HPC workloads, simulation-coupled ML, rare-event detection at LHC scale) we scope collaborations with Argonne National Laboratory, Fermilab, the University of Chicago Data Science Institute, or Northwestern McCormick faculty rather than overselling in-house capability. Argonne's Aurora exascale system and the Argonne Leadership Computing Facility (ALCF) open up workloads most commercial GPU clusters cannot touch, and the INCITE program offers allocations for qualifying scientific AI projects. Our core team handles applied engineering, MLOps, and productionisation, which is where most Chicago AI projects actually stall. For standard work (RAG, fine-tuning, classical ML, computer vision on known architectures) no academic partner is needed. We will tell you up front which bucket your problem fits into.

We default to AWS us-east-2 (Ohio) and us-east-1 (Virginia) plus GCP us-central1 (Iowa) for storage, training, and inference, with Azure East US 2 as the third option when Azure OpenAI is in scope. For LLMs that must stay inside US borders (HIPAA-protected clinical data, trading research IP, federal Protected workloads, BIPA-protected biometrics) we use Anthropic and OpenAI through Bedrock or Azure with US-only routing or self-host Llama 3 and Mistral on US-hosted GPU instances. Cross-border is acceptable only with an explicit Privacy Impact Assessment and is typically reserved for non-sensitive internal tooling. We document residency in the model card and the data processing agreement so HIPAA, BIPA, and PIPA audits have a clear answer without a forensic reconstruction.

A typical Chicago enterprise model (claim image triage at Allstate scale, fraud scoring for Discover-adjacent fintechs, clinical AI under FDA SaMD, drive-thru voice AI under BIPA constraints, document extraction for CME Group fintech) takes fourteen to twenty-four weeks from kickoff to production, assuming clean historical data, NIST AI RMF documentation as part of scope, and a designated client-side model risk owner. Week 1 to 4 is data audit, BIPA and PIPA scoping, and baseline. Week 5 to 14 is modelling, iteration, bias evaluation, and challenger testing. Week 15 to 20 is MLOps, monitoring, shadow mode deployment, and security review. Week 21 onward is gradual rollout with feature flags. If FDA SaMD clearance is in scope the timeline extends meaningfully because the clearance path itself is independent of engineering velocity.

Most custom AI work qualifies for the US federal R&D tax credit under Section 41, which returns a credit on qualified research expenses including wages, supplies, and contract research for activity that involves technological uncertainty and a process of experimentation. Illinois layers its own Research and Development Tax Credit on top, currently 6.5 percent of qualifying expenses incurred in Illinois, and Chicago-based startups can also pursue the EDGE credit and the Illinois Angel Investment Credit for early-stage equity financing. Eligible AI activity generally includes novel modelling, architecture experimentation, algorithmic uncertainty, and systematic investigation, not routine integration or vendor configuration. We deliver time-tracked logs, technical narratives, experiment histories, and failed-hypothesis documentation aligned with IRS Form 6765 and Illinois Schedule 1299-D. Final eligibility sits with your CPA and tax counsel.