AI integration in Chicago
Chicago's insurance and freight businesses run on documents: loss runs and applications on the insurance side, rate confirmations, bills of lading and carrier invoices on the freight side. Somebody reads each one and types the important fields into a system. That is the kind of work a language model can take most of, provided nobody pretends it is perfect.
We integrate document extraction into the workflow you already have. The model reads the document and proposes the fields, a person confirms or corrects them, and every correction becomes test data for the next prompt change. We measure accuracy per document type on your own anonymised examples before anything goes live, and we set the review threshold with you, so the model handles the clear cases and a person sees the ambiguous ones.
Chicago's software buyers are mostly not startups selling to other startups. The city's economy rests on trading and derivatives, around CME Group and the proprietary trading firms that grew up beside the exchanges; on freight and logistics, because this is where much of the country's rail and trucking traffic meets; on insurance, from carriers to the brokers and agencies around them; and on manufacturing and distribution across the Midwest. These are established, profitable businesses, and a good deal of their software is older than some of the engineers who maintain it.
So the work is integration and modernisation more often than a blank page. Freight systems speak EDI and carrier APIs that disagree about what happened to a load. Insurers run on policy administration systems nobody wants to replace and everybody wants to build around. Trading firms keep their latency-critical code with in-house specialists, and buy the layer around it: risk and reconciliation reports, operations dashboards, internal tools and data pipelines. The strongest pitch in Chicago is rarely a rewrite. It is a clean, documented system built beside the old one, with work moved across one piece at a time.
Built In puts the average base salary for a software engineer in Chicago at around $133,000, and every employer in the city competes with the trading firms for the same engineers. The result is a familiar backlog: the carrier integration that has to be done properly once, the agency portal everyone complains about, the internal tools the engineering team is too busy to reach. Those are defined pieces of work, which suits an engagement. It starts with a fixed-price two-week piece, and the code belongs to you from the first commit.
We move our working day for Illinois. Four hours of every working day overlap with your morning in Central time, stand-up included, so calls, reviews and decisions happen live with the engineer who writes the code. Everything decided or discovered is written down in your repository and tracker, and runbooks and architecture notes are written as we go, so the system built beside the old one is documented from its first day.
We are the right fit for a defined piece of modernisation, an integration that has to be done properly once, or the internal tools your own engineers are too busy to reach. Senior capacity starts within days, the first piece is a fixed-price two-week engagement, and agencies serving Chicago's insurers can put the work under their own name.
Four ways this arrives.
A prompt that behaved on twenty hand-picked inputs meets ten thousand real ones. We build the evaluation set from your actual traffic first, so a change can be judged rather than argued about.
Usually a large context sent on every call, or a big model doing a job a small one can do. We measure where the tokens go before recommending anything.
Logging that captures the prompt, the retrieved context, the model version and the output, so a support question has an answer that is not a guess.
A fallback path, a timeout that is shorter than your user’s patience, and a degraded mode that is honest about being degraded.
A weekly maintenance pulse where Claude writes only the prose, checked against a JSON schema and a whitelist of numbers, with a template that takes over when the model fails.
Read the write-up →Asked by Chicago teams.
How do you work with teams in Chicago?+
We are in Bengaluru and move our working day for Illinois, so four hours of every working day overlap with your morning in Central time, stand-up included. Calls, reviews and decisions happen in that window. The rest of the work lives in your tools: messages in Slack, code in GitHub, tickets in Linear. The person on every call is the engineer who writes the code, so a question about the system gets answered by the person who built it.
Do you build for Chicago freight and logistics companies?+
Yes. The work is usually the layer that turns messy events into a shipment you can trust: EDI load tenders, status updates and invoices parsed into one event model, carrier tracking APIs reconciled against it, and proof-of-delivery photos from a field app that works offline. Around that sit the operations screens a brokerage floor uses all day, and the pipelines that turn loads and lanes into pricing and margin reports.
How does your rate compare to hiring in Chicago?+
Built In puts the average base salary for a software engineer in Chicago at about $133,000, before benefits and bonus, and you are competing with trading firms for that person. Our published rate is $35/hour, or $5,400 a month for an embedded engineer, with a $5,000 minimum. There is no recruiting time and no employment overhead, and work starts within days. A fixed-price two-week first piece at $2,800 lets you judge us on output.
Can you build alongside our existing systems rather than replacing them?+
Yes, and in Chicago that is most of the work. We read the old system, write down what it actually does, and build the new piece beside it. Work moves across one workflow at a time, with both running until the last piece lands, so the business keeps running without a cut-over weekend. We did exactly this on FM360, moving a pipeline across module by module rather than in one cut-over.
Will you tell us if we do not need a model?+
Yes, and it happens often. Several requests we have taken turned out to be a search problem, a rules engine, or a form with better defaults. We would rather say that in week one than bill for a year of prompt tuning.
Whose API keys?+
Yours, in your accounts, with the spend visible to you. We never proxy your traffic through infrastructure we control.
What about our data going to a provider?+
We map exactly what leaves your systems and where it lands before anything is wired up. Where that is not acceptable, self-hosted open-weight models on your own hardware are a real option and we will price both.
Do you fine-tune?+
Rarely, and not as a first move. Retrieval and prompt structure fix most of what people bring to us as a fine-tuning problem, at a fraction of the cost and with none of the retraining treadmill.