I build the systems that tell you when the model is wrong.

A mail server cannot wait. Every millisecond of classification is a millisecond of delivery latency for a real customer, so the budget was single-digit milliseconds, and an LLM call is three orders of magnitude too slow to sit in that path. The answer was to stop treating classification as one decision. Cheap deterministic checks resolve the overwhelming majority, and only genuinely ambiguous mail escalates to the ML classifier and an asynchronous LLM pass, which is what keeps both latency and GPU cost viable at this volume. False-positive and false-negative reports route back as retraining samples, so the model tracks real traffic rather than a frozen dataset.

It writes a test suite, builds until the tests pass, then asks whether those tests were rigorous enough to believe: hold-out splits, mutation, property tests, reference oracles, differential fuzzing. It returns one calibrated confidence rather than a green tick, and escalates a pass it does not believe.

Meetings, Slack threads, pull request discussions and calendars reconciled into one state graph of commitments, decisions and blockers that survives across sessions. Deadline tracking, contradiction detection, auto-standups, and an MCP server that hands that state to any agent. Runs on hosted models or entirely local ones.

Every agent action is checked against deterministic policy before it executes, and each decision is signed into a SHA-256 append-only chain that an auditor can replay without the vendor's cooperation. Adapters for OpenAI, Anthropic, Gemini and OpenRouter, across HTTP, MCP and agent-to-agent.

Shows people which companies their installed apps are contacting, in real time. It reads DNS and TLS SNI through a local VpnService and attributes each hostname using an on-device classifier. Nothing leaves the phone, so the privacy claim is structural rather than a promise, and the classifier is open source so it can be checked.

A team shares one live session and watches the same thread stream token by token: presence, revision history with diffs, comments that become assignable action items, and the ability to halt a response mid-flight while keeping whatever already arrived. The SwiftUI client adds OAuth handoff and an offline send queue so a prompt written without signal still sends.