Bigcircle does not place engineers. A retrieval system in production depends on ingestion that keeps the structure of messy documents, hybrid search, a citation behind every claim, and retrieval measured on its own, because a prompt cannot repair a passage that was never fetched. We deliver that as a studio engagement through our RAG systems service.
Bigcircle does not place RAG engineers. Our team builds the retrieval system and measures retrieval apart from the answer, with hybrid search and a citation behind every claim. On an ESG filing platform in production, parsing keeps tables and footnotes intact and every field carries a confidence score that sends weak extractions to review.
A RAG engineer on a Bigcircle team measures retrieval on its own, with hybrid search, reranking, and a citation behind every claim.
Tuning the prompt to hide a missing passage leaves the index wrong, while the RAG engineer on a Bigcircle team scores retrieval apart from the answer.
Retrieval measured apart from the answer, with a citation behind every claim, built by our team.
See what a production retrieval system involves, what Bigcircle has built over ESG filings and its own research engine, and the three ways to buy it.
In production
Layout-aware parsing keeps tables and footnotes in their original structure, a knowledge graph sits beside vector search because neither can answer the other’s questions alone, and every field carries a confidence score that routes weak extractions to review.
Read the case study →Our own system, in daily use
Hybrid retrieval runs semantic and keyword search together over a vector database with a custom chunker, and a claim without a supporting retrieved passage does not appear in the output. On one run it reduced 420 sources to 6 pieces of grounded evidence.
Read the case study →In production since 2023
Retrieval runs over a whitelisted index of case law, statutes and registry data that is the entire universe of citable authority, with jurisdiction and class filters applied at retrieval.
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 retrieval system as a studio engagement through its RAG systems and document extraction services, with retrieval measured apart from the generated answer and a citation behind every claim.
Bigcircle built an ESG filing platform in production that combines a knowledge graph with vector search and keeps citations and source-page links on generated analysis. Bigcircle's own prospect research engine runs semantic and keyword search together, drops any claim without a supporting passage, and on one run reduced 420 sources to 6 pieces of grounded evidence.
Bigcircle starts with a conversation about your documents and the questions they have to answer. 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 retrieval, the build runs through the eval set first, retrieval before generation, then tuning the answer, then wiring it to the index that will actually run. 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 retrieval work the exercise breaks the index, and the engineer has to show whether the passage was missing or the answer ignored 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.
LLM features with a source behind every RAG answer and an eval that can fail the release, built by our team.
Pipelines traceable from the source row to retrieval and training, built by our team.
Fine-tunes compared against the base model on the same inputs, with serving changed only when quality held, 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.