Bigcircle does not place engineers. Retrieval and fine-tuning are only as good as the data under them, so the work is ingestion from messy sources, schemas that fix metadata at entry, and lineage from every output back to the source row. We deliver that as a studio engagement through our data pipelines for AI service.
Bigcircle does not place data engineers. Our team builds the ingestion and the governed layer that retrieval and fine-tuning run on, with metadata fixed at ingestion and lineage back to the source. On a fund reconciliation platform Bigcircle built, each custodian has its own mapping and every exception records how it was resolved and by whom.
A data engineer on a Bigcircle team builds the ingestion and the governed layer that retrieval and fine-tuning run on, and keeps the path from source to store traceable.
A store with no path back to the source hides whether the bug is retrieval or data, while the data engineer on a Bigcircle team keeps that path as the job.
Pipelines traceable from the source row to retrieval and training, built by our team.
See what the data layer under AI involves in production, what Bigcircle has built for reconciliation and ESG filings, and the three ways to buy it.
Built by Bigcircle
Each custodian gets its own mapping, so a format change touches one mapping, and every exception is a record with a lifecycle that names how it was resolved and by whom. Break rate is monitored per source, and a sudden drop is treated as being as suspicious as a spike.
Read the case study →In production
Entity, period, unit and framework are carried as hard metadata from parsing rather than inferred at query time, and lineage runs back to the source page. Analysis cycles moved from weeks to hours.
Read the case study →Our own system, in daily use
Every source is ingested into one metadata schema at entry, because interpreting later means interpreting repeatedly and inconsistently.
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 data pipeline as a studio engagement through its data pipelines for AI and RAG systems services, with lineage that traces any output to its source row.
On an ESG filing platform in production, Bigcircle carries entity, period, unit and framework as hard metadata from parsing, with lineage back to the source page. On a fund reconciliation platform, each custodian has its own mapping, so a format change touches one mapping, and exceptions are records with a lifecycle.
Bigcircle starts with a conversation about your sources and what has to be built on them. 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 data work, the build starts from the actual documents, then the ingestion path, chunking to the structure, and making the pipeline incremental. 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 data work the exercise breaks a pipeline so its output can no longer be traced to the source row. 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.
Retrieval measured apart from the answer, with a citation behind every claim, 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.
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.