Working solutions in days. Production-grade by design.
AI-native from first call to production. Parallel AI agents do the heavy lifting, a written spec keeps them honest, and automated checks decide what ships. Every engagement compounds: the skills library, harness, and agent team are reused, so each new client starts further along than the last.
The operating model
Agents write. Checks verify. Humans decide. Click a stage.Prototype fast. Harden for production.
Eight steps, bracketed by two commercial gates. Steps 1 to 4 put a working preview in the client’s hands in days. Steps 5 to 8 turn the approved picture into a governed, monitored production system.
About 15 days, $10K to $15K, credited forward. NDA and DPA before any data moves. Nothing gets built without a number the sponsor agrees to.
A 9-section FRD with numbered acceptance criteria. No production build without a signed FRD and a handoff pack. Scope changes are numbered change requests.
Every change ships the same way
Interactive simulationGrounded, tested, and swappable.
Code does the math. Models explain, classify, and challenge. Toggle below to see what separates an engineered AI system from a prompt wrapper.
See the difference
Is the linen blazer on sale? What does it cost in a medium?
Press run to see the reply.
Five patterns in production
Select a patternHard calls go to a council, not a single opinion
Systems that recover on their own, and know when not to.
Every agent workflow runs as a durable Inngest function. When something breaks, the platform retries, resumes, or fails over inside strict limits. Anything that needs a code change becomes a pull request a human approves.
Durable execution
Inngest functions with step-level retries and backoff. Completed steps are memoized, so a retry resumes at the failed step without repeating side effects. Per-client concurrency keys, scheduled refreshes, and one-click replay of failed runs.
Rolling outOps agent
Listens to Inngest failure events and error tracking, classifies the incident, and takes allowlisted actions only: retry, re-run, restart, or fail over to the fallback model.
RoadmapFix agent
When the cause is code, an agent reproduces it as a failing test and opens a pull request. The workflow pauses until a human approves. It never merges its own fix.
RoadmapLearn
Each resolved incident updates the runbook and the skills library, so the next occurrence is handled one tier lower, faster, and without a human.
RoadmapIncident simulator
Pick an incident and press run.
Shadow mode first: the agent proposes and logs what it would do, and humans compare it with what they actually did. Each action class is promoted to active only after it earns a clean track record.
Guardrails
- Action allowlist: retry, re-run, restart, fail over. Nothing else runs without a human.
- Hard budgets per incident. When the budget runs out, the agent escalates instead of looping.
- Code changes are always pull requests, merged by a named human. Production still releases from main only.
- Low confidence means hand over, not guess: the agent ships a diagnosis pack to the on-call engineer.
- Every action lands in an audit trail written by the system, with a kill switch per client.
What it looks like in code
simplified// A durable sync: retries resume at the failed step export const syncCatalog = inngest.createFunction( { id: "sync-catalog", retries: 4, concurrency: { limit: 1, key: "event.data.clientId" }, onFailure: ({ event, error }) => inngest.send({ name: "ops/incident.opened", data: { source: "sync-catalog", error: error.message } }) }, { event: "catalog/sync.requested" }, async ({ event, step }) => { const rows = await step.run("pull", () => adapters.catalog.pull()); await step.run("upsert", () => db.upsert(rows)); }); // Fix agent: open a PR, then pause for a human const merged = await step.waitForEvent("human-review", { event: "github/pr.merged", match: "data.incidentId", timeout: "72h" });
Secure by architecture, not by promise.
Defense in depth, designed for AI systems: the model never holds the keys, never sees another client’s data, and never ships its own code. Pick a threat to see which layers stop it.
Each client starts further along than the last.
A snapshot, not a ceiling. We evaluate new models, agents, and tools continuously and adopt what proves itself against our evals. Click a layer for the reasoning.
Why it compounds
More clients, more load, same team.
Scale shows up in three ways, and each has its own mechanism. The one deliberate limit is human review, and we keep that fast with small pull requests and automated checks.
Configure, don’t rebuild
The skills library, harness adapters, and templates mean a new client is configuration and data wiring. Code becomes shared the moment a second client needs it.
LiveElastic by default
Stateless containers on Cloud Run scale out on demand. Postgres on Neon scales compute and branches per environment. The model gateway fails over across providers.
LiveCompute, not headcount
Parallel agents scale throughput with compute. Specs, tasks, and checks let new engineers and agents onboard from the written goal, not tribal knowledge.
LiveTurn up the traffic
Illustrative, not a benchmarkWe don’t claim what we haven’t shipped.
Controls that exist today, what is next, and where the system already runs in production.
Security and control
- NDA and DPA signed before any data moves.
- Separate databases per client and per environment.
- Secrets in a vault. A named human merges every change.
- Nothing trains on client data. Local embeddings keep retrieval data in our containers.
- You own the repo and the data: standard TypeScript on Postgres.