Bigcircle does not place engineers. When calling an API is not enough, the model itself has to change: fine-tuning on your rules, structured output that holds at scale, a comparison against the base model, and serving that stays fast without dropping the task. We deliver that as a studio engagement through our fine-tuning and self-hosted model services.
Bigcircle does not place LLM engineers. When calling an API is not enough, our team fine-tunes on your rules, compares the fine-tune against the base model on the same inputs, and changes serving only when that comparison says the task held. On our trademark drafting system, fine-tuning handles structure and register while citations come only from retrieval, because a fine-tune cannot make an authority exist.
An LLM engineer on a Bigcircle team changes the model itself when calling an API is not enough, and publishes the comparison against the base model before a serving change ships.
An API call leaves the model unchanged, while the LLM engineer on a Bigcircle team fine-tunes against the client's rules and will not change serving until that comparison says the quality held.
Fine-tunes compared against the base model on the same inputs, with serving changed only when quality held, built by our team.
See what changing the model involves in production, how Bigcircle compares a fine-tune against the base model, and the three ways to buy that work.
In production since 2023
Supervised fine-tuning handles structure and register, and citations come only from retrieval over a whitelisted index, because fine-tuning improves how a citation is phrased and cannot make an authority exist. Production logging compares the base and fine-tuned models on the same inputs, so a regression from a model change shows up as a score movement.
Read the case study →In production
Built alongside the client’s team: a compact, quantized speech model running CPU-only inside the app, with decoding biased toward sales vocabulary. Transcription error rate fell from roughly 18% to 10% across nine rounds of tuning.
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. When the model itself has to change, Bigcircle delivers that work as a studio engagement through its fine-tuning, self-hosted models and model routing services, with the comparison against the base model done before anything ships.
Bigcircle fine-tuned the model behind a trademark drafting system in production since 2023, with production logging that compares the base and fine-tuned models on the same inputs. Bigcircle also put a compact, quantized speech model inside a desktop app, running CPU-only, where transcription error rate fell from roughly 18% to 10% across nine rounds of tuning.
Bigcircle starts with a conversation about the model and the task. 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 model work, the build runs through proving a prompt cannot do it, building the dataset, training and comparing against the base model, and planning the retraining. 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 model work the exercise breaks the comparison with the base model, and the engineer has to show which model should ship. 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.
LLM features with a source behind every RAG answer and an eval that can fail the release, built by our team.
Versioned prompts and an eval suite that can block a merge, built by our team.
Model and prompt rollouts with a rollback, and traces a person on call can use, 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.