Bigcircle is an AI implementation studio. The agent, retrieval system, voice agent or eval suite a role would have built is delivered as a studio engagement by the team that scopes it, in your repositories, for products where a wrong answer has consequences.
Bigcircle does not place engineers or do staff augmentation. The system a role on this page would build is delivered as a studio engagement: production hardening for a prototype that stalled, a fixed-scope build, or a studio-led programme. A core team hired and trained by the founder leads it, contractors from a pool of about 100 Bigcircle has vetted and already engaged on client projects join when it needs more people, and you own the code.
In place of a hire: production hardening, a fixed-scope build, or a studio-led programme. Bigcircle does not place engineers, and each of the three delivers a working system.
| Criterion | Production hardening | Fixed-scope build | Studio-led programme |
|---|---|---|---|
| Who leads | We finish it | We build it | We run it |
| What you get | Your prototype, taken into production | One working system at a fixed scope and price | A studio team of engineers and QA, led by our founder as fractional CTO |
| What changes | 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. | Discovery, build, evals and deployment sit under one point of accountability, against a scope and price agreed before we start. | We run the build rhythm and make the hard technical calls; you bring the product context. |
| When it fits | You have a prototype that will not cross into production. | The problem is clear and you want the result, not a team to manage. | You have a roadmap and no senior AI team to run it. |
| How Bigcircle handles this | The agent can only take the actions our code allows, guardrails check each one before it runs, and LLM evals score every output before a person relies on it. You own the code. | One point of accountability covers discovery, build, evals and deployment, in your repositories. You own the code. | The team that scopes the system builds it, and every engineer on it stays on one product. You own the code. |
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.
If the question is an implementation partner, a full-time hire, or an agency, use the build-or-partner guide. Choosing between the three is the engagement guide.
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.
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.
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.
You are buying a working system rather than a person’s time, so four things work differently from a placement.
The work happens in your repositories under your IP and confidentiality terms. You own the code in every engagement.
The people on the scoping call write the code and take the system into production. Nothing is handed to a second team after the scope is agreed.
The core team on your system is hired and trained by our founder and stays on it from start to finish, instead of being split across several accounts at once. When the build needs more people, contractors from a pool of about 100 we have vetted and already engaged on client projects join it.
Discovery, build, evals and deployment sit under one point of accountability. You review a working system against what we agreed, not a person’s hours.
The work is built around these three, so a wrong answer is caught before a person relies on it or an agent acts on it.
On our trademark drafting system, every draft passes a legal-integrity harness on six axes before it reaches the attorney, and base and fine-tuned models are compared on the same inputs so a regression shows up as a number.
We never assume the agent got it right. On our DevOps agent, a validation loop and an engineer’s review of an architecture diagram sit between the model and the AWS account.
On our ESG filing platform, generated analysis keeps its citations and source-page links, and a conclusion without support is not presented as fact. That rule is enforced at generation.
You own the code in every engagement, and the work happens in your repositories.
Who we build for
Companies whose AI has to be right, where a wrong answer or a wrong action costs money, a customer or a regulator’s attention.
Most people reach this page by searching for an engineer to hire. If one of these is the reason, one of the three engagements on this page covers it.
Each page covers what that work involves in production, what we have built in the area, and the engagement that delivers it. If the question is whether the team you have can ship it, the team readiness assessment scores five dimensions, and a full pass takes about five minutes. If you want to do this work rather than buy it, write from the careers page.
GENERATIVE_AI
LLM features with a source behind every RAG answer and an eval that can fail the release, built by our team.
LLM_ENGINEERING
Fine-tunes compared against the base model on the same inputs, with serving changed only when quality held, built by our team.
AI_AGENTS
Agents whose every action is checked before it runs and logged so it can be replayed, built by our team.
RAG_RETRIEVAL
Retrieval measured apart from the answer, with a citation behind every claim, built by our team.
LANGCHAIN
Which LangChain abstractions to keep and which to replace with a direct SDK call, decided by our team on your build.
LLAMAINDEX
Retrieval fixed along the ingestion path, not only in the vector store, by our team.
CREWAI
Multi-agent runs with a ceiling on delegation and a budget on the whole run, built by our team.
MLOPS_INFRA
Model and prompt rollouts with a rollback, and traces a person on call can use, built by our team.
VOICE_AI
Realtime voice agents with interruption handling and a full record of every call, built by our team.
DATA_ENGINEERING
Pipelines traceable from the source row to retrieval and training, built by our team.
PROMPT_EVAL
Versioned prompts and an eval suite that can block a merge, built by our team.
Calling a model is the easy part. This is the engineering that decides whether the feature survives real traffic, real cost, and real customers.
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 shortlist and no interviews. The first conversation is about the system, and the first deliverable is a scope you can hold us to.
An engineer replies within two hours on weekdays. The first call is 30 minutes on what you are trying to ship and what is in the way.
What we would build, in what order, and what it depends on from your side.
Production hardening, a fixed-scope build, or a studio-led programme, depending on whether you have a prototype, a clear problem, or a roadmap.
The team that scoped it builds it, with evals and a safety net in place before it takes traffic. You own the code.
Real builds for named clients, each with the status you can check in the case study.
In production as a shipped product
A desktop AI chief-of-staff that plans a goal, acts across email, calendar, documents and CRM, and checks claimed completion against the real state of the application. Credentials stay in an encrypted local vault and never enter the prompt. We shipped retrieval over its tool registry before the major labs published the pattern as a documented standard.
Solarpunk · Executive operationsIn production in a signed, notarized desktop app
Working alongside the Nurix team on AnyTeam, we built live-meeting detection, audio capture and an on-device transcription model fast enough for live calls. Meeting detection had no reference implementation to copy. Audio never leaves the laptop, because there is no upload path.
Nurix · Sales copilotsIn production, citation-grounded
Research over dense ESG filings that keeps citations and source-page links on generated analysis and never presents an unsupported conclusion as fact. Each new filing is fact-checked against the knowledge base, so a restatement surfaces as a flag instead of a second truth.
Still have a question? Mail hello@bigcircle.ai and we’ll get back within two hours on weekdays.
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.