Agents that act on real systems.
Agents that take real actions on real systems (desktop, cloud, the OS) through MCP tooling. A 24/7 chief-of-staff agent, a cloud agent that deploys to live AWS, a real-time meeting copilot.
AI implementation for products where a wrong answer has consequences.
- Fixed-scope builds
- one working system, scoped and priced before we start
- Production hardening
- the prototype that stalled, taken through evals, edge cases and scale
- Studio-led programmes
- a team led by our founder that owns delivery on your roadmap
Teams that ship with us.
At a glance
Bigcircle builds AI for products where a wrong answer has consequences, and the case studies say which systems shipped and which were only tested.
Choose a fixed-scope build, production hardening for a prototype that stalled, or a programme a studio team runs. A work page keeps a not-deployed status when that is the truth.
- Who this is for
- Product and engineering leaders who need AI built and operated in production.
- Topics
- AI implementation
- Production AI systems
- Production case studies
In short
Bigcircle (bigcircle.ai) is an AI-native implementation studio for production AI where a wrong action is expensive. The agent is treated as untrusted and checked in code before it acts, and the model never sees the raw record. The team that scopes a system builds it. You buy a fixed-scope build, production hardening for a stalled prototype, or a studio-led programme, and you own the code.
Compare the options
Production hardening, a fixed-scope build, or a studio-led programme. The difference is how much of the delivery Bigcircle runs.
| Criterion | Production hardening | Fixed-scope build | Studio-led programme |
|---|---|---|---|
| Who leads | You built it, we finish it | We own 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 3 to 8 engineers and QA |
| What you keep | Your code, with an eval set, model routing for cost and latency, and a safety net under it. | You bring the problem. Bigcircle delivers discovery, build, evals, and deployment against a fixed scope and price. | You bring the product context. Bigcircle owns the delivery rhythm and the hard technical calls. |
| 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 is treated as untrusted and checked in code before it acts. You own the code. | One point of accountability covers discovery, build, evals, and deployment. Reversible tokenization means the model never sees the raw record. You own the code. | The founder leads the team as fractional CTO. The same check in code and the same code ownership apply. |
What we build,
already in production.
Grounded document & data intelligence.
Citation-backed extraction and search over dense, regulated documents, with humans kept in the loop. A year-long build for an institutional client, an ESG and IMF research platform.
The hard 80% that makes it trustworthy.
Evals, model routing, on-device inference, a PII-anonymization layer, and audit trails. The reusable toolkit that turns a clever model into something a serious company can actually run on.
Executive operationsWork 01
Solarpunk
A desktop AI chief-of-staff that plans a goal, acts across email, calendar, docs, and CRM, and checks its own work, with credentials that never leave the device. We built dynamic tool-discovery for it months before the labs made it a standard.
Tasks done without a human
55% → 80%
SalesWork 02
AnyTeam
A real-time sales co-pilot that detects a live meeting, transcribes it on-device, and writes a clean, CRM-ready summary the moment the call ends. No audio ever leaves the laptop.
In the world to ship live-meeting detection
~2nd
Cloud infrastructureWork 03
Skionis
A DevOps agent that turns plain-English requests into safe, validated cloud changes, using MCP tools to inspect, apply, and roll back, all scoped to your own account.
Template errors before deploy
10% → 4%
# WHY AI STALLS
Building AI is easy now. Shipping it isn’t.
We have already crossed that line. Many times.
We have taken AI to production in legal, cloud infrastructure, sales, and regulated document workflows. Hand us the build, or the prototype that stalled.
Prompting a demo is a weekend. Production AI (evals, model routing, grounding, the failure modes) is a different discipline, and most teams are learning it on your timeline.
It demos beautifully, then meets real customers. Latency spikes, costs balloon, it hallucinates on the inputs nobody tested, and there’s no one on call. That’s where projects stall.
Engineers who have actually taken AI to production are scarce and hard to assess. By the time you’ve hired one, the roadmap has slipped two quarters.
The team that builds yours has already shipped AI in production.
The hard parts, already shipped.
Anyone's demo can call a model. The distance to production is the unglamorous middle: identity, orchestration, data boundaries, model routing, guardrails, and the audit record. Four of those six are governance, and we build them as plumbing rather than policy — enforced in the request path on every call, not asserted in a document.
Where governance actually lives.
- Identity
Every run acts as a scoped principal with its own token, never as the application.
Read the long version - Permission scoping
The tool bus advertises what exists. The token decides what this run may actually invoke.
Read the long version - Approval gates
Irreversible actions are promoted one at a time, on evidence, not switched on as a batch.
Read the long version - Data boundaries
Personal data is encoded before the model sees it and decoded after it answers.
Read the long version - Guardrails
Every response is checked against policy on the way out, before it reaches your systems.
Read the long version - The record
Every step lands an entry you can replay afterwards, including the ones that failed.
Read the long version
The same team either way.
You choose how much it runs.
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.
Choosing between the three is the engagement guide. Choosing between an implementation partner, building in-house, and an agency is the build-or-partner guide.
We finish it01
Production hardening
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.
Yoursprototype to production
We build it02
Fixed-scope build
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.
Fixedscope and price
We run it03
Studio-led programme
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.
3–8studio team
The numbers firms usually keep off the homepage.
AI delivery is too easy to posture around. So we lead with the awkward proof: a narrow bar for who builds it, real systems in production, builds that have held up for years, and AI shipped where failure is expensive.
Top 3%five-stage gauntlet
Engineers who clear our bar, and the only people on your build
A five-stage gauntlet: a real generative-AI build, a live system-design review, and a frontier check. Fewer than three in a hundred make it through, and we do not subcontract.
200+in production
AI systems shipped, not demoed
RAG, agents, and voice carrying real traffic across B2B SaaS, fintech, insurtech, cloud, and legal. The demo is the easy part; we have done the unglamorous rest.
2023live, not a pilot
Our earliest GenAI build still runs daily
A fine-tuned, citation-grounded system a practicing attorney tested and signed off on, live since 2023. The useful work starts after launch: evals, drift, and reliability under real users.
900msunder real load
Voice AI that holds under peak load
A multilingual insurance agent answering on the line in under a second, every call verified, logged, and replayable. Real-time AI where slow is a dropped customer.
“They'd already solved the exact failure modes we kept hitting.”
“Evals on day two, and they caught a failure mode our team would have shipped.”
“One engineer who had shipped this before moved faster than the pod we were planning.”
Common questions
- What is Bigcircle?
- Bigcircle is an AI-native studio and implementation partner. We build production AI systems (agents, retrieval, document intelligence, voice) for companies whose AI has to be right, and the team that scopes a system stays on it from start to finish instead of being split across several accounts at once.
- What is the difference between production hardening, a fixed-scope build and a studio-led programme?
- It is how much of the delivery we run. Production hardening takes a prototype your team built through an eval set, model routing for cost and latency, a locked-down data path and a safety net. A fixed-scope build is a working AI system delivered against a fixed scope and price, with discovery, build, evals and deployment under one point of accountability. A studio-led programme is a team of three to eight engineers and QA, led by our founder as your fractional CTO, that owns delivery on your AI roadmap.
- Do you place engineers in our team?
- No. Bigcircle does not do staff augmentation. We deliver the system, and the people who build it are ours: hired and trained by the founder, kept on one product, and never subcontracted.
- How is this different from prototyping with AI coding tools?
- A prototype tells you whether the idea works on the inputs you tried. Production has to know how often it fails on the ones you did not: an eval set, checks in code before an agent acts, cost and latency under real traffic, and someone accountable when it is wrong. That second part is the work we sell.
- Who owns the code and the IP?
- You do. The work happens in your repositories under your IP and confidentiality terms.
Let's build something that ships.
Tell us what you're building. We'll tell you whether it needs a fixed-scope build, a prototype taken to production, or a programme we run, what's achievable, and where the real bottlenecks are.