Agents that act on real systems.
Plan, act through MCP tooling on the desktop, the cloud, and the OS, check their own work, and escalate when the stakes justify a human. Credentials never leave the device.
The AI systems we have built,and how each one works.
Agents that act on live systems, citation-grounded document intelligence, and voice agents. Every case study below is the architecture, the failure modes we hit, and the numbers we measured. We can walk you through any of them live.
In short
Each Bigcircle case study is the architecture, the failure modes, and the numbers measured, and any of them can be walked through live. The work clusters as agents that act on live systems, citation-grounded document intelligence, and sub-second voice.
- Executive operationsDynamic tool discoveryVenture-Backed B2B SaaS Built an AI Chief-of-Staff AgentA 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.55%80%Tasks done without a humanEngineering write-up
- SalesOn-device inferenceSeries A SalesTech Built an On-Device AI Meeting Intelligence PlatformA 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.~2ndIn the world to ship live-meeting detectionEngineering write-up
- Cloud infrastructureHuman-gated tool executionSeed-Stage DevOps SaaS Built an AI Cloud Operations AgentA 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.10%4%Template errors before deployEngineering write-up
- Legal & IPCitation-bounded retrievalSeries A LegalTech Built an AI Trademark Drafting PlatformA fine-tuned LLM with RAG over a trademark and IP library, built so the model can only cite firm-approved authorities. A practising trademark attorney tested it extensively and approved its direction and output quality.40%98%Five-part template adherenceEngineering write-up
- ESG & institutionalClaim-to-evidence lineageInstitutional ESG Analytics Built an AI Filing Intelligence PlatformAgentic search over dense ESG and institutional filings. It extracts the data, grounds every answer in a citation, fact-checks new documents against the knowledge base, and builds interactive reports on top.Every answerCited and fact-checkedEngineering write-up
- Fund operationsFigure-to-source lineageInstitutional Fund Manager Built an AI Reconciliation PlatformFund and portfolio management in one place. It automatically pulls in statements and brokerage data from every custodian, then handles reconciliation, dashboards, and compliance in a single view.DayshoursMonth-end reconciliationEngineering write-up
- Market intelligenceEvidence-locked retrievalBigcircle Built an AI Prospect Intelligence EngineCompany and market intelligence at scale. Hybrid RAG over a vector database turns web-scale research into clean, structured answers, every one locked to a real source rather than invented.AfternoonminutesPer full-profile workupEngineering write-up
- InsuranceTyped tool boundary at 900msEnterprise Insurer Built an AI Voice Claims PlatformAn insurance voice agent that handles policy, claim, and payment questions over the phone through MCP tools. Fast, fully auditable, and built to hold its response time under heavy call volume.900msTime to respondEngineering write-up
- Real estateConstraint resolution on live inventoryPropTech Platform Built an AI Voice Property Discovery AgentOur voice agent, retargeted for real estate. Buyers explore listings by phone in English, Hindi, and Hinglish: conversational, fast, and grounded in live inventory.<1.3sTime to respondEngineering write-up
- Sales coachingRubric-governed scoringBigcircle Built an AI Sales Roleplay Coaching PlatformA voice-based sales coaching simulator. A voice agent runs realistic roleplay calls, throws objections and counter-questions, and a rubric-based engine turns each one into a skill-by-skill scorecard.20–40m2–3mManager review timeEngineering write-up
The demo is the easy part. Everything here survived the rest.
Three problems, shipped over and over.
Grounded document & data intelligence.
Citation-backed extraction and search over dense, regulated filings, with a validator that rejects anything outside the approved index. Every answer traces to a real source.
Real-time voice built for peak load.
Multilingual agents that answer in about a second, verify the caller, and log every call as a structured, replayable record. Built on one runtime we reuse across verticals.
Common questions
How should I read a Bigcircle case study?
Read the mechanism and the case together. Bigcircle opens each card with the industry and the engineering that made the build hard, then the title and the measured number. Skionis reads the live AWS account before it plans, and an engineer approves a diagram of what will exist rather than the YAML. It is in production, and Bigcircle demos it against a real AWS account.
What does each case study cover?
The architecture, the failure modes, and the numbers measured, with one status line for where the system runs: in production for a client, built and tested end to end for a client, or Bigcircle's own system in daily use. Bigcircle will walk you through any of them live.
What separates the agent, document, and voice clusters?
Bigcircle separates them by the failure each build had to survive. Agents check the work before a change runs. Brandiligence's validator rejects unresolved citations, and it is in production, verified by the practising attorney it was built for. Prospex is Bigcircle's own system, in daily use on our pipeline: a claim with no retrieved source does not appear. Voice reuses one runtime and logs every call as a structured record.
What can Bigcircle demo?
Any case study on this index. Bigcircle will walk you through the architecture, the failure modes, and the numbers measured, live. On AnyTeam there is no upload path for customer audio: capture and transcription stay on the rep's machine, so customer audio never leaves the laptop. It is in production inside a signed and notarized desktop app.
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.