A demo is not a product
The week-one demo wows the room. The other nine tenths of the work, evals, guardrails, monitoring, escalation and integration, is what turns it into something a business can lean on.
The pitch
Production AI that does real work. Operated. Humans on the calls that matter.
You have seen the demo. The real question is whether it survives contact with your customers, your data, and a Monday morning in production.
Everyone can buy the same models. The advantage is the system around them: your data, your workflows and your judgment, turned into AI employees that run in production. We operate them, keep the accuracy on a dashboard, and put people exactly where the stakes demand.
The problem
Most AI pilots die between demo and production.
Not because the model was not clever enough. Because nobody built the system around it, and nobody owned the result.
You cannot staff the plumbing
The specialists who build eval harnesses, guardrails and agent orchestration are scarce and expensive, and the req has been open for months. Meanwhile the roadmap slips and a competitor ships.
The gap is an operating model
Pilots do not stall for want of a smarter model. They stall for want of the operating model around it: who owns the accuracy number, who reviews the edge cases, and what happens at 2am.
What we do
AI employees on your real workflows.
Not a chatbot bolted to the side. Staff onboarded to your tools, given a scope and a manager, measured like people, and built on the part AI cannot copy: your data, your workflows, your judgment.
01
Support and customer operations
Agents that resolve tickets, intake and questions end to end, with humans on the edge cases. On your tone, your systems and your rules, by chat, voice and WhatsApp.
- Support agents
- Voice and WhatsApp
- Escalation built in
Documents, research and back office
Contracts, claims and reports read in minutes with audit trails, plus research, reporting, scheduling and reconciliation run continuously by AI staff you can supervise.
- Document intelligence
- Back-office ops
- Audit trails
Go-to-market and revenue
Lead scoring the revenue team believes, autonomous content pipelines, and the in-product copilot your roadmap keeps deferring, shipped and adopted.
- Lead scoring
- Content pipelines
- In-product copilots
Why now
The capability got cheap. The operating model did not.
Two years ago you could not buy this. One year ago you could not afford to build it. The models kept getting cheaper and longer-context, month after month.
~10x / yr
cheaper for a fixed level of capability, roughly 1,000x in three years. a16z named it LLMflation.
1,000,000+
tokens of context in today's frontier models: whole case files, contracts and codebases in a single prompt.
2027
the year Gartner expects more than 40 percent of agentic AI projects to be canceled, for want of an operating model, not a smarter one.
The bottleneck moved. It is no longer intelligence, it is the operating model around it, and that is what we build.
How we deliver
Prove. Harden. Run.
Because we build the whole system, not just the model. A working system on your real cases, hardened until quality is a score you can read, then run together in production.
Prove
A working system on your real cases. Not a deck.
Harden
Tested hard, watched live. Quality becomes a score.
Run
Run together. AI does the volume, people make the calls.
AI does the volume. People make the calls. Security everywhere.
Why it is hard to copy
The moat is not the model.
Everyone has the same models. What compounds is everything around them, and none of it copies over in a weekend.
The data flywheel
Every human correction becomes an eval case and a training example. Accuracy climbs week over week on your data, and the lead compounds the longer we run.
We run it, and we own the number
Not a handoff and a runbook. We operate the system with dashboards, SLAs and accountability for the accuracy, cost and uptime it delivers.
Ten industries of depth
Domain judgment, from fintech onboarding to insurance suitability to clinical guardrails, encoded in the evals and the review rubrics. Hard-won, not prompt-deep.
Security-first, from day one
Least-privilege access, full audit logs, and personal data masked before models see it, designed to support DPDP, GDPR and HIPAA. Your data stays yours.
Proof, in production
Shipped, not slideware.
two dayseight minutes
Biometric customer onboarding for a fintech, working offline in the field.
about an hourminutes
A compliant insurance needs-analysis session, automated end to end and offline.
From our production work
- An autonomous AI newsroom we operate researches, writes and publishes every day, at under two dollars per article.
- Document agents read contracts, claims and reports in minutes, with every conclusion traceable to its source.
- Grain quality graded from a single phone photo, at parity with the lab, ending grader-to-grader drift.
- Skin analysis from a selfie in under a minute, with guidance routed toward professionals where it matters.
Recent and under NDA: an autonomous AI newsroom that researches, writes and publishes every day; a voice AI interviewer for a hiring platform; AI-authenticity screening; care-worker matching built on vector search; a WhatsApp-native digital therapist; strata-report and contract intelligence; calibration tooling for automotive ECUs; and high-throughput transcription pipelines.
How we engage
Built for companies with something to win.
A few clients at a time, from first bet to operating scale. Funded startups and enterprises worldwide, $10M to $100M and beyond.
Sprint
Proven with a working system. Not a deck.
Build and scale
A dedicated AI team that ships and hands over clean.
Operate
Your AI workforce, run as a managed service with SLAs.
Bring us the AI bet everyone says is too hard.
One call. An honest read on what AI can and cannot do about it, and what it takes to run it in production.