Bigcircle does not place engineers. Making quality measurable means versioned prompts, an eval set pulled from real traffic, metrics that match the task, a gate on the release, and red-team cases next to the happy path. We deliver that as a studio engagement through our LLM evaluation service.
Bigcircle does not place prompt or evaluation engineers. Our team builds the suite that makes quality a number a release can fail on: versioned prompts, regression suites, LLM-as-judge beside human review, and red-team cases. On our trademark drafting system, every draft passes a legal-integrity harness on six axes before it reaches the attorney.
A prompt and evaluation engineer on a Bigcircle team turns quality into a number a release can fail: versioned prompts, regression suites, and a red-team case beside the happy path.
A feeling that the answer improved cannot block a merge, while the evaluation engineer on a Bigcircle team ships a suite that can.
Versioned prompts and an eval suite that can block a merge, built by our team.
See how Bigcircle turns a quality feeling into a suite that can block a merge, what it has built for legal drafting and coaching, and the three ways to buy it.
In production since 2023
Every draft passes a 0 to 10 legal-integrity harness on six axes before it reaches the attorney, and attorney corrections are kept as the signal automated scoring cannot see.
Read the case study →Built by Bigcircle
The score comes from a versioned, schema-driven rubric and the model writes only the qualitative read, so a model upgrade cannot shift every historical comparison. Score distributions per persona are the calibration signal.
Read the case study →In production
Validation failures are kept as the system’s own record of what the model gets wrong, and that record is used to improve the loop rather than 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 eval suite as a studio engagement through its LLM evaluation and AI observability services, with versioned prompts, a gate on the release, and red-team cases beside the happy path.
Bigcircle built a 0 to 10 legal-integrity harness on six axes that scores every trademark draft before an attorney sees it. On RepliCoach, Bigcircle's own sales roleplay coach, the score comes from a versioned rubric and the model writes only the qualitative read, so a model upgrade cannot shift historical comparisons.
Bigcircle starts with a conversation about what good output means for your system. 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 evals, the work runs through pulling a set from real traffic, picking metrics that match the task, gating the release, and keeping the suite alive. 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 eval work the exercise makes an answer worse while the suite still passes, and the engineer has to show which check should have caught it. 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.
LLM features with a source behind every RAG answer and an eval that can fail the release, built by our team.
Agents whose every action is checked before it runs and logged so it can be replayed, 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.