We talk to them once on the team's behalf, so nobody has to interrupt them again.
For engineering teams at 50-500 person companies where 2-3 people hold all the system knowledge.
See Your Knowledge Map map
Every engineering org hits these breakpoints. The problems are predictable. The cost is not.
The same 2-3 senior engineers answer every architecture question. Their deep work disappears into Slack threads.
On-call escalates to the same person every time. New hires take 3-6 months to make a meaningful PR.
Knowledge lives in hundreds of Slack threads and undocumented decisions. Nobody knows what's captured and what isn't.
We don't extract knowledge from your engineers. We amplify what they already want to share.
Connect your tools. See where knowledge concentrates. Fill the gaps through conversation.
Read-only access to Slack, GitHub, and your docs. We analyze question patterns, code ownership, and documentation coverage.
A report showing where knowledge concentrates, which systems have single points of failure, and where your documentation misses the reasoning behind decisions.
We surface the risks. Your managers decide which systems, which experts, and what's urgent. No AI decides what's valuable. Humans set the agenda.
AI asks specific questions about the gaps your team identified. Targeted questions the expert is tired of repeating, not open-ended probing.
Attributed answers in Slack or search. Every response cites the source engineer. Knowledge stays linked to code versions and gets flagged when the underlying system changes.
The difference between documentation and actual engineering expertise is the reasoning behind it.
You probably do. Here's what they're good at and where they stop.
Good for storing documentation your team writes.
Nobody writes it. And when they do, they document the what, not the why. Six months later the page is stale and nobody updates it.
Good for finding documents that already exist.
They search what's written. They can't surface what was never documented: the reasoning, the judgment calls, the "it depends" that lives in one person's head.
Good for summarizing and querying existing content.
Only as good as what's in the docs. If the answer to "why did we choose Kafka?" was never written down, no amount of RAG will find it.
Good for real-time knowledge transfer, one person at a time.
Doesn't scale. The same engineer answers the same question 10 times. The answer lives in a DM thread nobody else can find.
Most knowledge capture fails because engineers hate being interrogated and runbooks go stale in weeks. We built around both problems.
Engineers clam up when they feel interrogated. Our AI adapts conversational style in real time, adjusting pause tolerance, question depth, and follow-up intensity so the expert stays in flow, not on guard.
Static runbooks rot. Our knowledge graph tracks what's true, when we learned it, who said it, and links captured reasoning to git state. When the code changes, stale knowledge gets flagged automatically.
Your new hire doesn't need a 40-page runbook. They need the answer to "why do we do it this way?" Query-time synthesis pulls from every source and shows its work.
"Our bus factor is 1 on three critical systems. I can't see where knowledge concentrates until someone leaves."
A knowledge map that shows exactly which systems are at risk, which engineers hold concentrated expertise, and where documentation misses the reasoning.
"I lose two senior engineers to Slack questions every sprint. They're answering instead of building."
Capture the answers to the questions your team asks most, so your seniors can get back to deep work.
"I'm tired of being the answer to every deployment, incident, and architecture question."
Your knowledge becomes accessible without you, everything attributed to you by name. You build influence that outlasts your current role.
Every "quick question" costs your senior engineer over 23 minutes of deep work. At two interruptions per hour, a 60-minute deep-work block becomes statistically improbable.
Gloria Mark, UC Irvine ("The Cost of Interrupted Work," CHI 2008)
Critical system knowledge that isn't documented anywhere: architecture decisions, debugging heuristics, the operational judgment that keeps production running. All of it gone when the engineer leaves.
Engineers lose over a day per week searching across Slack threads, outdated wikis, and asking colleagues (5.3 hrs), plus duplicating work that was never findable (7 hrs).
When interruptions are frequent, only 7% of developers report a good day. The gap between productive and unproductive teams is driven by how well they protect engineering focus.
The problem isn't that knowledge doesn't exist.
It's that it's trapped in Slack threads, undocumented decisions, and one person's head.
Engineers are rightfully skeptical of tools that analyze their communication. Here's how we handle it.
Read-only access to public channels only, no DMs ever, and no individual productivity tracking. We analyze question patterns at the team level, not individual behavior.
The opposite: it makes your expertise available when you're heads-down or on vacation, all attributed to you. You become more valuable to your team, not less.
You control what enters the knowledge base. Insights are team-level by default, focused on system resilience, not which individuals are "problems." Named attribution only where engineers opt in.
We capture reasoning through conversation, not forms. And we detect when knowledge goes stale instead of letting it rot in a wiki nobody reads.
15+ years building AI/ML, mobile, and SaaS products as a technical product manager at IBM, Samsung, and Nike.
M.S. in Computer Science / B.S. in Applied Mathematics.
15+ years building data privacy and enterprise platforms as a developer and engineering manager at Airbnb and Outreach.
M.S. in Computer Science / B.S. in Applied Mathematics.
Strategy manager at Bank of America, 2x founder who achieved profitability in first year at both companies.
M.S. in International Economics and MBA from Duke Fuqua.
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