AI in real customer use. In weeks, not quarters.
From knowledge that lives only in heads to systems anyone can read and review. We work at any level — from the first diagnosis to the system that improves with every use.
Six levels — from the first step to the system that improves with every use.
Each level is a concrete state of the system, with signals that identify it and a deliverable that moves it to the next.
L0 In heads
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No one can explain in writing how the process works.
3–5 day diagnosis with a prioritized plan and a clear build-vs-buy call.
L1 Experimenting
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Everyone uses Cursor or ChatGPT their own way — no shared rules, no shared conventions.
Full review of what you've already built with AI, with a clear plan for what to fix first.
L2 Written
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Documents exist (written rules, registries) that capture the team's knowledge.
A search system over your documents with answers that cite the source — without the AI making things up.
L3 In real use
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An AI is in real use answering questions about your data — not generic data.
AI system in real customer use, with documentation your team can read, human review before each important action, and a log of every decision.
L4 Watchful
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The system proposes actions, detects what's unusual, and escalates hard decisions to a human.
Continuous improvement loops, automated quality checks, and real-usage metrics.
L5 Improves with use
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Each new use case costs less and less to add to the system.
Monthly retainer · one fixed point of contact · continuous capability expansion.
What a system like this can do for your team.
The areas we touch in most projects. Each one solves a concrete business problem — your situation picks the tools, not a closed catalog.
AI that does real work, without constant supervision
01- → Decides what to do with each new task — without someone walking it through step by step
- → Multiple AIs can work in parallel without stepping on each other
- → You can stop it at any moment from where your team already works (GitHub, Linear, your tools)
Technical details
- · Event-driven dispatch (GitHub, Linear, custom webhooks)
- · Worktree or container isolation
- · Skill-based routing and progressive tool disclosure
- · Conversation compaction, stop commands, loop prevention
Finds answers in your documents — without making things up
02- → Ask as if you were talking to a colleague who knows every one of your documents
- → Every answer cites the source — you see where it came from, no magic
- → Continuous quality checks — we catch when the system starts getting things wrong
Technical details
- · Document ingestion with canonical schemas
- · Vector stores — pgvector, Pinecone, Qdrant
- · Source-attributed answers — no hallucination without citation
- · Continuous evals on retrieval quality
AI that stays in its lane
03- → Limited permissions by type, amount, and time — like a credit card with a daily limit
- → Previews every important action before making it real
- → You can revoke access at any moment, without calling support
- → Visible history of every decision the system made
Technical details
- · Scoped authority models — session keys, scopes, expiration
- · Simulation-before-execute · rate limits · distributed locks
- · One-click revocability · user-visible audit trails
- · Encryption at rest · secrets management · rotation
Works where your team already works, not on a separate platform
04- → Deploys to the cloud your company already uses — not ours
- → Runs reliably without your team having to babysit it
- → You can see whether it's healthy or something's wrong, at any moment
- → Every change passes automated review before it reaches real customer use
Technical details
- · Deploy in the customer's cloud (not ours)
- · Observability — structured logs, metrics, health endpoints
- · Resilient crons with tunable batch + concurrency
- · CI/CD with automated review gates
Every project starts with one question.
“What level are you at and which tools serve you best?” The tools are a decision — not a commitment to a platform. The case studies are the evidence: each one documents what we chose, what we rejected, and why.
See case studiesWhat we don't do.
- 01 We don't build chatbots "just to have AI".
- 02 We don't sell proprietary platforms that lock you in.
- 03 We don't work with clients who can't (or won't) articulate the real problem they want to solve.
- 04 We don't promise "AI that replaces your team". We promise AI that makes your team disproportionately more effective.
- 05 We never launch a system without a human approving the important steps. Never.
Let's talk about your project.
30 minutes. We assess your case and give you a realistic timeline and architecture.
Within 5 days of the call. Architecture, phases, costs, tools chosen and why.
First launch in under 4 weeks from kickoff.
No pitch, no commitment.