orbitone · ai software lab

We build AI productsthat survive contactwith reality.

OrbitOne is a small engineering lab for founders shipping agents, copilots, and AI-native software. Less slideware. More production paths, evals, and interfaces people finish using.

typically 6–14 week engagements · senior builders only · remote-first

build_surface · live
/agentsMulti-agent workflows that run real ops, not demo chats
/ragRetrieval stacks tuned on your data — latency, cost, evals included
/productAI-native interfaces people actually finish using
/infraObservability, guardrails, and fallbacks before you scale traffic
awaiting_scope…

// what we take on

Three engagement types. No generic “digital transformation.”

01

AI product builds

From zero to shipped: model selection, UX for uncertainty, backend orchestration, and a release path that doesn’t die in a notebook.

  • ·Next.js
  • ·Python
  • ·vLLM / API providers
  • ·Postgres + vectors
02

Agent systems

Tools, memory, routing, human-in-the-loop. We design agents for production failure modes — timeouts, tool abuse, bad plans — not just happy paths.

  • ·Tool protocols
  • ·queues
  • ·eval harnesses
  • ·HITL review
03

Applied LLM integration

Drop intelligence into existing software: copilots, document pipelines, search, support automation. Measurable quality bars, not “we added a chatbot.”

  • ·RAG
  • ·structured outputs
  • ·streaming
  • ·cost controls

// operating rules

How we decide what to build next.

AI work fails when teams optimize for demos and press. These are the constraints we hold — on every engagement.

Ship the system, not the demo

Demos impress. Systems survive traffic, weird inputs, and Friday deploys. We optimize for the second one.

Evals before vibes

Every serious AI feature needs a quality bar you can re-run. We build the harness with the product, not after the incident.

Models are interchangeable

Provider lock-in is a product risk. We design for swap-ability: adapters, structured contracts, and cost/latency knobs.

Humans stay in the loop where it matters

Autonomy is a spectrum. We place review, escalation, and override where failure is expensive — and automate the rest.

tools we actually touch

OpenAI / Anthropic / open weightsLangGraph · custom orchestratorspgvector · Qdrant · hybrid searchNext.js · FastAPI · TypeScriptTemporal · queues · event busesLangfuse · Phoenix · custom evals

// the lab

Three builders. One quality bar.

OrbitOne is intentionally small. You work with the people writing the agents, designing the product surface, and wiring the evals — not a bait-and-switch bench of contractors.

Background across product engineering, ML-adjacent systems, and AI UX. We take a few concurrent builds so context doesn’t evaporate mid-sprint.

meet the operators →
AC
Alex Chen
product eng · agents
SJ
Sarah Johnson
systems · retrieval
MR
Michael Rivera
ai ux · product

// from people who shipped with us

“They killed our ‘chatbot on the homepage’ plan in week one and replaced it with a retrieval + workflow agent that actually closed tickets. The eval suite is still running in CI.”

Jane Cooper · head of product, lin & co

“First team that treated model cost and latency as product requirements, not afterthoughts. We swapped providers twice without rewriting the app.”

Michael Chen · founder, innovatelab

// next

Have a hard AI problem — not a vague “AI strategy”?

Send scope, constraints, and timeline. If we’re a fit, you’ll get a clear yes, a rough shape of the build, and a discovery slot. If not, we’ll say so fast.