Bigcircle does not place engineers. A generative AI feature in production needs retrieval grounded in your data, models wired to your systems through tools and APIs, an eval that can fail a bad release, and latency and cost that hold under real load. We deliver that as a studio engagement, in your repositories.
Bigcircle does not place generative AI engineers. Our team builds the feature itself, from prompt to product, with a source behind every RAG answer and an eval that can fail a bad release, in your repositories. Our trademark drafting system has run in production since 2023 with a fine-tuned model, whitelisted retrieval and schema-constrained decoding in the same request.
A generative AI engineer on a Bigcircle team ships an LLM feature from prompt to product, with a source behind every RAG answer and an eval that can fail the release.
An engineer who has not had the citation or the eval broken can ship answers with no source, while Bigcircle will not put an engineer who cannot restore that check on a client build.
LLM features with a source behind every RAG answer and an eval that can fail the release, built by our team.
See what a production generative AI feature involves, what Bigcircle has built with cited answers and release-gating evals, and the three ways to buy it.
In production since 2023
A fine-tuned model, retrieval over a whitelisted index of case law and statutes, and schema-constrained decoding work in the same request. A practising trademark attorney tested it and approved its direction and output quality, and the attorney still edits and signs every draft.
Read the case study →In production
Layout-aware parsing keeps tables, sections and footnotes intact, and every field carries a confidence score that routes low-confidence extractions to review. Generated analysis keeps its citations and source-page links, enforced at generation.
Read the case study →In production
A desktop agent that plans a goal, acts across email, calendar, documents and CRM, and checks its own work. Credentials stay in an encrypted local vault and never enter the prompt.
Read the case study →Who builds it: Bigcircle's core team scopes the system and leads the build. Every engineer on that team cleared five stages of vetting and stays on one product.
Every engagement is a working system, built in your repositories under your IP by the team that scoped it. The three differ only in how much of the delivery we run.
We finish it
Your team built something that works in the demo and stalls on real inputs. We define what good means and build the eval set, route models for cost and latency, lock down the data path, and put a safety net under it before it takes traffic.
Best when you have a prototype that will not cross into production.
We build it
You bring the problem and we deliver the working AI system. Discovery, build, evals and deployment sit under one point of accountability, against a scope and price agreed before we start.
Best when the problem is clear and you want the result, not a team to manage.
We run it
A studio team of engineers and QA, led by our founder as your fractional CTO and architect, owns delivery on your AI roadmap. We run the build rhythm and make the hard technical calls; you bring the product context.
Best when you have a roadmap and no senior AI team to run it.
Choosing between the three is the engagement guide.
Every engineer on the core team that leads your system cleared these before joining. Each stage asks whether they can keep AI running once real customers are using it.
400 applicants
3%of applicants join
the team
What removes them
One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
No. Bigcircle does not place engineers or do staff augmentation. Bigcircle builds the generative AI feature itself as a studio engagement through its production AI engineering service: retrieval grounded in your data, models wired in through MCP tools and APIs, and evals that measure quality before release.
Bigcircle built a trademark drafting system that has run in production since 2023, combining a fine-tuned model, retrieval over a whitelisted index and schema-constrained decoding, and a practising attorney approved its output. Bigcircle also built an ESG filing platform where generated analysis keeps its citations, and a desktop chief-of-staff agent that acts across email, calendar, documents and CRM.
Bigcircle starts with a conversation about the feature. You get a written scope covering what Bigcircle would build, in what order, and what it depends on from your side, then choose production hardening for a prototype you already have, a fixed-scope build at an agreed scope and price, or a studio-led programme led by our founder as fractional CTO. For a generative AI feature, the work runs through defining what good means, routing for cost and latency, locking down the data path, and shipping with a safety net. The work happens in your repositories, and you own the code.
Every engineer on Bigcircle's core team cleared five stages before joining, and the top 3% of applicants remain. Vetting starts from a system they already built, then breaks something and watches them fix it. For generative AI work the exercise breaks a feature so its answers no longer cite a source, or an eval no longer fails a bad output. They also say how they would know it is working, how they would make it fast without running up the bill, and what they do first when it is late and the AI got it wrong. They lead client builds as a team.
Fine-tunes compared against the base model on the same inputs, with serving changed only when quality held, built by our team.
Agents whose every action is checked before it runs and logged so it can be replayed, built by our team.
Retrieval measured apart from the answer, with a citation behind every claim, built by our team.
Bigcircle does not place engineers. Describe the system and what is in the way, and an engineer replies within two hours on weekdays. A written scope follows within a week.