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AI-Driven Knowledge Transfer Platform

Capture tacit knowledge before employees leave. AI-guided offboarding interviews, searchable handoff packs, and onboarding briefs for teams losing institutional know-how.

When a ten-year sales engineer gives notice, the company loses more than a headcount. They lose the workaround for the legacy billing API, the three enterprise accounts that only trust her, and the mental map of which procurement contacts actually respond on Fridays. HR schedules an exit interview about culture. IT revokes access on day ten. Nobody captures the tacit layer—the stuff that never made it into Confluence because it lived in Slack DMs and hallway conversations. People leaders know the cost. SHRM pegs replacement cost at 50–200% of salary for specialized roles, and a large share of that is ramp time, not recruiting fees. Yet most offboarding still means a PDF checklist and a forwarded email thread. L&D teams buy Guru or Trainual for documented SOPs, but those tools assume someone already wrote the knowledge down. They do not interview the person who knows which vendor will expedite a PO if you cc finance.

Built for Developers, Solo Founders.

Suggested stack: Next.js 14 + Supabase, Claude + Whisper, Vercel + Inngest, Stripe Billing. Weekend scope: about 10 hours.

The Problem

When a ten-year sales engineer gives notice, the company loses more than a headcount. They lose the workaround for the legacy billing API, the three enterprise accounts that only…

The Solution

Offboarding project — owner, departing employee, last day, role template

Market Research

Voluntary turnover in US professional services remains elevated post-2022; teams plan for 15–20% annual movement in high-growth SaaS.

Competitive Landscape

Guru — Card-based knowledge base inside Slack/browser. Great for documented answers; weak for generating tacit knowledge from leavers who never wrote cards. Custom; typically…

Business Model

Starter ($299/mo) — 3 active projects, 500 AI minutes, PDF export

Recommended Tech Stack

Next.js 14 + Supabase — Tables: organizations, projects, interviews, answers, handoff_packs. RLS per org. Store audio in Supabase Storage; transcripts in Postgres.

AI Prompts to Build This

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  1. 1. Offboarding Schema + Interview Flow

    Scaffold a Next.js 14 App Router B2B app for AI offboarding knowledge transfer. Supabase auth (magic link), orgs, projects, interviews.

Schema:
- projects(id, org_id, departing_name, role_template, last_day, status)
- interview_sessions(id, project_id, channel enum[text,voice], transcript, completed_at)
- answers(id, session_id, question_key, body, embedding vector(1536))
- handoff_packs(id, project_id, markdown, published_at)

Flow: manager creates project → system emails departing employee daily question batches (5 questions) → Claude generates next questions based on gaps in answers table → on last day compile handoff pack sections: Stakeholders, Open loops, Tooling tricks, Landmines, First 30 days advice.

Include RLS and a minimal admin review UI.

  2. 2. Adaptive Follow-Up Generator

    Write a server action that takes prior answers for an offboarding project and returns the next 3 interview questions.

Input: role_template (sales_engineer), answers[], days_until_last_day.
Use Claude with JSON schema: { questions: [{ key, text, priority, rationale }] }.
Rules: never repeat answered keys; prioritize accounts/revenue/risk if sales; prioritize on-call/runbooks if engineering; if <7 days left, only high-priority gaps.
Store embeddings on each answer for duplicate detection.

  3. 3. Handoff Pack Assembler

    Given all answers for project_id, generate a handoff pack markdown with sections and inline citations [Q12].

Use Claude Opus/Sonnet with strict instructions: no invented policies; mark unknowns explicitly; include stakeholder table; export HTML + PDF via @react-pdf/renderer.
Add "successor brief" variant: 1-page TL;DR for incoming hire.
Webhook to post summary to Slack channel #team-handoff.