idappt lab:
how does it work?
idappt is an AI-native engineering lab. We don't just use AI to autocomplete code — we architect entire AI teams that build software end-to-end.
How idappt lab works
Context pipelines
Every useful AI agent depends on the right information arriving at the right step. We design complex context pipelines that gather, filter, version, and route the documents, data, and instructions an agent needs — so each LLM call receives the task-relevant context it needs to make better-informed decisions.
LLM orchestration
Different models are good at different things. We orchestrate Large Language Models in concert — reasoning models for planning, fast models for routine work, specialised models for code, retrieval, or critique — so the right intelligence shows up for the right task.
Autonomous coding agent networks
We experiment with networks of specialized AI coding agents that can research, plan, design, build, test and review software as coordinated teams. Individual agents can take on distinct roles, exchange context, hand off tasks and iterate on each other's work with limited human intervention.
Multi-agent software "startups"
idappt architects and simulates a complete software "startup" staffed by AI agents in specialised roles — autonomous analysts who scope the problem and set the functional requirements, architects who design the system, engineers who implement it, and testers who break it before users do. Humans set the mission, approve the direction, and own the release.
Throughput of a much larger team
As a small foundation our goal is to achieve the throughput normally associated with a much larger software team — while at the same time being thorough in documentation, testability, accessibility, and the drive to develop solutions for citizens.