Onboarding new engineers with AI that reads your project graph and answers their questions in real time.

New engineers spend their first month searching Slack history for 'why does this work this way?' and asking senior engineers who'd rather be coding. Stride lets the AI answer those questions from the actual project graph — ADRs, stories, dependencies, and decisions — instead of from your senior engineers' time.

The problem

First-month engineering ramp is brutal: every question requires a senior engineer's attention; institutional memory lives in Slack threads from 2023 nobody can find; and the org chart doesn't tell you who to ask. The work isn't intellectually hard; it's discovery-hard. Most teams accept 4-6 weeks of low productivity per new hire as the cost of doing business.

How Stride solves it

Stride's AI answers questions by traversing the project graph. 'Why is the auth service split from the user service?' → AI finds the ADR, shows the rationale, links to the diagram and the affected stories. New engineers ask Stride before they ask a human, and the human gets ~70% fewer ramp-time interruptions.

AI Q&A over the project graph (ADRs, stories, diagrams, defects, runbooks)
Curated onboarding paths per role (engineer / PM / designer / QA)
Architecture tours: AI walks new engineers through the system from entry points
Code-area ownership lookup: who owns this module right now?
Glossary of internal terms (your domain vocabulary, team-specific)
First-30-days checklist generated from your onboarding template

Best for

Teams hiring frequently (5+ engineers per year) at organisations where institutional memory exists in some form (ADRs, runbooks, prior tickets).

Not for

Pre-PMF startups under 10 people where everything is in everyone's head. The AI needs documented signal to traverse; verbal-only orgs need to write things down first (which Stride helps with elsewhere).

Frequently asked

How does the AI handle questions about things not in the graph?

It says so. 'I don't have information about that — try asking Alice (last person who worked on this).' The owner lookup is also part of the graph. The AI doesn't hallucinate answers; absent context, it says absent.

What about security-sensitive context?

AI queries respect your existing role-based access. A new engineer can't read content they don't have permission for, even via the AI. The same auth boundaries that protect your existing UI protect the AI surface.

Is this just RAG over our docs?

Conceptually similar but graph-aware. RAG over docs returns the best-matching snippet. Stride's AI traverses typed relationships — 'this story affects this ADR which affects this service which is owned by this team' — so multi-hop questions work. RAG can't answer 'who should I ask about the cache layer?'; the graph can.

What does the team need to do before this is useful?

Have some ADRs (10+ is useful, 30+ is great). Have a populated story graph (most teams already do). Have ownership defined per module (Stride helps surface this from commit history if not explicit). The AI quality scales with graph density.

See team onboarding in Stride

14 days of Stride Pro, no credit card. The sample project includes every module so you can explore end-to-end in five minutes.

Start free