The open-source AI builder platform you embed in your product.
Agents, apps, and skills, built on your servers with your LLM keys. Implement it yourself, or our AI-native engineers do it for you.
Arrears by building
Worst first · pulled live from the rent ledger · April fiscal year
Total arrears
$48,210
▲ $3,120 this month
Tenants overdue
31
9 over 60 days
Avg days late
23
▲ 4 vs last month
Outstanding by building
Done - arrears by building, worst first, live from the ledger.
built with speculos-harness
AI adoption was phase one. Owning the platform is phase two.
The mandate today is adoption - the questions that follow are cost, lock-in, and model choice.
The bill
Inference spend scales with adoption. Every generation here is metered - per person, per model, at provider rates.
Lock-in
Workflows built in a vendor's tool stay there. An open-source platform on your servers stays yours.
Model churn
The best model changes every few months. Here, switching is a configuration change - closed or open weights.
Open source, in three parts.
A React package, a Python backend, and a build service - working defaults out of the box, every piece swappable for your own. It runs on your servers, behind your SSO.
@speculos-harness/react
The chat, live preview, file view, and version history, dropped into your product as components.
speculos_harness
The agent: a router you mount on your FastAPI or Flask service. Storage, auth, and models plug in here.
speculos/harness-bundler
A container that rebuilds the app on every change - fast enough that there's no run button.
Instructions
set once by your adminIncluded on every build, company-wide.
Design system
your tokens, applied to every generated app
Models
a company default, plus what users may pick
SSO
your identity in front of the platform
Prompt log
every prompt recorded and replayable
Sandbox
generated code runs sealed; credentials stay on your server
Your models, at provider prices.
No inference markup - usage is billed by your provider, on your keys, and metered per person and per model. Adding a model is a configuration change.
budgets and rate limits: your LiteLLM proxy
Own the way your company builds with AI.
Our AI-native engineers set the platform up inside your infrastructure - and it stays yours to run.