AI Systems
AI systems that reach production.
Most AI projects stall between the demo and the deployment. We build the part that survives real users, real data, and real edge cases.
The argument
The problem was never the model.
You can get an impressive demo in an afternoon. What takes real work is everything after: real data with real permissions, the cases where it shouldn’t answer, measuring whether the output is good, and making it something your team trusts without checking.
The six systems
The six we build most often.
Each has a defined job and a manual process it removes. The fixed price comes after we've scoped it.
Support Agent
Answers customer questions from your real documentation and ticket history, and escalates to a human when it isn't confident rather than guessing.
Replaces the first-response queue
3–4 weeks
Sales & Lead Agent
Qualifies inbound, answers pre-sales questions, books into your calendar, and hands warm context to whoever takes the call.
Replaces leads going cold in an inbox
2–3 weeks
Document Extraction
Turns invoices, contracts, applications, and forms into structured data your systems can act on, with a confidence threshold and a human review path.
Replaces manual data entry
3–5 weeks
Internal Knowledge Assistant
One place your team can ask questions and get answers grounded in your own documents, with sources cited so anyone can check them.
Replaces “does anyone know…” in Slack
3–4 weeks
Content Operations Pipeline
Research, drafting, and repurposing wired into your existing publishing workflow, with a human at the approval step.
Replaces the content bottleneck
2–4 weeks
Integration Agent
Moves data between systems that were never designed to talk to each other, handling the field mapping and, more importantly, the failures.
Replaces copy-paste between tools
2–4 weeks
Evaluation & monitoring
How you'll know it's working.
Most AI work skips this, which is exactly why most AI work quietly stops being used.
Evaluation set
Built from your real cases, not synthetic examples. It's the regression test for behavior that has no compiler to catch it.
Monitoring
Volume, failure rate, escalation rate, and cost - visible without asking us for a report.
Defined failure behavior
What the system does when it isn't confident, written down and agreed before launch.
What we integrate with
OpenAI · Anthropic · Supabase · Postgres · n8n · Make · Slack · Stripe · Cal.com · Google Workspace
Questions
About the AI part.
Will our data be used to train models?
No. We architect for that explicitly and document exactly where your data goes, which provider processes it, and what is retained.
What if the AI gets it wrong?
Every system has a defined confidence threshold, an escalation path, and a human review step wherever the cost of being wrong is real. Designing that is part of the build, not an afterthought.
Can you work with the AI tools we already pay for?
Usually yes - and that's often the cheapest path. Part of the audit is finding what you're already paying for that isn't switched on.
How do we know it's actually working?
Every system ships with an evaluation set built from your real cases and a monitoring view. You'll answer that question with data instead of a feeling - and you'll know the day it stops.
Have an AI project that stalled?
Most stall in the same three places. Tell us where yours is.