How to tune agents trained on public data to work better with your private code

I was working in a team of 10 people on a project. We kept running into inconsistency across the codebase because everyone prompted differently and got different results. We tried maintaining a skills.md file to fix it, but it wasn’t great.

So I built Decispher (decispher.com). It's main goal is to automatically capture engineering context from across different platforms and feed it to AI agents. It also records the decisions and assumptions agents make while coding, then surfaces them back to humans. Thereby acting as shared context layer.

I’ve been running it on our own repo. It’s been genuinely helpfulcu tting token usage by 35% on a well-documented repository (ADRs and all) and by 65% on a rougher one. The models now perform much more consistently and in line with our actual decisions.

Decispher currently connects to Slack, Jira, GitHub, GitLab, AI agents, and VS Code (or any fork). It also lets you copy context from one agent chat and paste it into another chat, agent, or machine. (there are more cool and useful features)

We have made the product live. 94 seats are remaining. 50 free credits to start.

Thanks