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 engineering for products where a wrong answer has consequences.
- Embedded engineering
- senior engineers inside your team’s standups
- Outcome-driven builds
- scoped, end to end — we own the result, not the hours
- The last mile
- evals, edge cases, scale — where AI usually stalls
Teams that ship with us.
At a glance
Work with Bigcircle through an engineer embedded in your team or a team that leads the build. Review the services and case studies to choose the right model for your product.
- Who this is for
- Product and engineering leaders who need to build and operate AI in production.
- Topics
- Embedded engineers
- AI engineering services
- Production case studies
Production-proven,
not prompt-deep.
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.
Fewer failed changes
10% → 4%
# WHY AI STALLS
Building AI is easy now. Shipping it isn’t.
We have already crossed that line. Many times.
Our engineers have taken AI to production in legal, cloud infrastructure, sales, and regulated document workflows. Hire one, or hand us the build.
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.
Our engineers have 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
One bar of talent.
Embed it, or hand us the build.
One bar of talent: engineers who have already taken AI to production. Embed them in your team, or hand us the build. The only thing that changes is how much of the delivery we run.
The choice between an embedded engineer, a CTO-led pod, and a fixed-scope build is the engagement guide. Choosing between us, a full-time hire, and an agency is the staffing guide.
You lead01
Embed an engineer
A production-proven AI engineer embeds in your team and your codebase, in your timezone window, bringing LLM, RAG, and agent depth straight into your roadmap. You set priorities and own the code. We handle employment. The low-risk way to start.
Best when you have the direction and the team, but not the AI depth.
1–3embedded engineers
We lead02
CTO-led pod
A managed squad of engineers and QA, led by our founder as your fractional CTO and architect. We own the delivery rhythm and the hard technical calls; you bring the product context. The depth of a senior AI leader without the hire.
Best when you have a roadmap but no senior AI team to run it.
3–8managed squad
We own it03
Fixed-scope build
You bring the problem, we ship the working AI system. Discovery, build, evals, and deployment, all under one point of accountability against a fixed scope and price.
Best when you want the result, not the hiring and management overhead.
Fixedscoped outcome
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 hiring bar, real systems in production, builds that have held up for years, and AI shipped where failure is expensive.
Top 3%five-stage gauntlet
AI engineers who clear our client bar
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.
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.”
Questions buyers ask first.
- What is Bigcircle?
- Bigcircle is an AI engineering company. Our engineers build production AI systems (agents, retrieval, document intelligence, voice), and they stay on one product from start to finish instead of being split across several accounts at once.
- What is the difference between embedding an engineer, a CTO-led pod and a fixed-scope build?
- It is how much of the delivery we run. An embedded engineer (one to three) joins your team and codebase; you set priorities and own the code. A CTO-led pod (three to eight engineers and QA) is led by our founder as your fractional CTO, and we own the delivery rhythm and the hard technical calls. A fixed-scope build is a working AI system shipped against a fixed scope and price, with discovery, build, evals and deployment under one point of accountability.
- How fast can an engineer start?
- Most clients see a shortlist within two to five business days and have someone embedded inside two weeks, because we match from engineers who are already vetted.
- Who owns the code and the IP?
- You do. Engineers work in your repositories under your IP and confidentiality terms. We handle employment, payroll and compliance.
Let's build something that ships.
Tell us what you're building. We'll tell you whether you need an engineer embedded or the whole build led, what's achievable, and where the real bottlenecks are.