What a AI Knowledge Retention Tool actually does
Captures departing-expert knowledge via adaptive exit-interview questions, audio transcription, and AI-extracted runbooks for succession planning.
As 10,000+ US Boomers retire daily (per SSA 2025 projections), enterprises face a knowledge-cliff crisis: senior engineers, architects, and domain experts walk out the door with undocumented expertise. This implementation automates knowledge capture via four AI layers: (1) LLM-guided exit-interview questions that adapt to the expert's domain, (2) Real-time audio transcription and diarisation during interviews, (3) Structured runbook extraction from unstructured audio (turning 'here's how I handle vendor disputes' into a 2K-word runbook), and (4) Embeddings-indexed expert corpus for future reference ('When we had that database crisis in 2021, Sarah handled it — her notes are in the knowledge base').
Workforce-transition consultants, L&D firms, and internal HR teams can resell this per-departing-expert project at $499–$1,999 (capturing one senior person = 4–8 hours of work, COGS ~$5–10 in AI tokens). The DIY path on Lovable is especially attractive because runbook extraction is text-only and pairs perfectly with Claude Opus 4.8 (high-stakes accuracy for critical processes) + Deepgram transcription (audio → text at $0.0043/min).
AI capabilities involved
Adaptive exit-interview question generation based on expert's domain
Real-time audio transcription + speaker diarisation (multi-speaker identification)
Structured runbook / SOP extraction from unstructured audio transcripts
Expertise mapping (which expert knows what — cross-department gap analysis)
Future-query routing ('When do we need Sarah's vendor-dispute playbook?')
Who uses this
- Workforce-transition consultancies managing knowledge-transfer projects for 5–20 enterprises/year
- Internal HR teams in enterprises with 100+ planned retirements (e.g., financial services, utilities)
- L&D / training organizations building succession-planning programs
- IT consulting firms selling knowledge-management as a service to clients
SaaS alternatives on the market
Real products you can sign up for today — with current 2026 pricing, honest pros and cons.
Bloomfire
Large enterprises wanting a general intranet/knowledge base (not a knowledge-retention tool for departing experts).
$25/user/mo (Communities plan)
Custom pricing for 500+ users
Pros
- +Built-in Q&A, search, and recommendation engine.
- +Mobile app for on-the-go knowledge workers.
- +AI auto-summarization of long documents.
- +Integrates with Slack and Microsoft Teams.
Cons
- −Not retention-focused — general knowledge base for ongoing documentation, not exit-interview capture.
- −No audio transcription or runbook extraction; you must manually input knowledge.
- −Per-user pricing means HR pays $25/mo per employee for each departing expert they want to capture (very expensive for high-turnover roles).
- −No structured-exit-interview templates or succession-planning gap analysis.
Guru
Teams wanting a lightweight internal knowledge base (not a retention-capture tool).
$15/user/mo (Team plan)
Custom pricing
Pros
- +Lightweight, fast knowledge base; simple card-based format.
- +AI-powered verification (cards stay up-to-date as policies change).
- +Chrome extension for in-context knowledge.
- +Slack integration for instant answers.
Cons
- −Similar to Bloomfire — not built for knowledge-exit-interview capture.
- −No audio transcription or runbook extraction.
- −Per-user pricing + light on retention-specific features (no interview templates, no runbook automation).
- −No succession-planning or gap-analysis tools.
SAP SuccessFactors Learning / Workday Skills Cloud
Enterprise HR departments managing global L&D and succession planning for 500+ employees.
Enterprise quote (typically $10K+/mo for 500+ employees)
Custom implementations
Pros
- +Integrated with HR workflows (talent management, performance reviews).
- +Built-in learning paths and skill assessments.
- +Advanced analytics on skill gaps across organization.
- +Multi-language support.
Cons
- −Overkill for small-to-mid-market; minimum commitment is 500+ employees.
- −No audio transcription or runbook extraction.
- −Implementation takes 6–12 months; very expensive for one-time retention projects.
- −Not designed for exit-interview automation or knowledge-capture workflows.
Notion AI
Teams that already use Notion and want to add lightweight knowledge-capture on top (not a dedicated retention tool).
Free plan (limited AI features)
$10/user/mo (Plus plan)
Team plan custom pricing
Pros
- +Flexible document editor; excellent for unstructured knowledge capture.
- +AI assistant can auto-generate summaries, Q&A, and translations.
- +Very low friction — many teams already use Notion.
- +Database features for structured runbooks or SOP templates.
Cons
- −No audio transcription; would need to manually transcribe or use a separate service.
- −No structured runbook extraction — AI summarization works but doesn't auto-generate SOPs from unstructured interviews.
- −No interview-question adaption or succession-planning specific workflows.
- −Per-user pricing ($10/mo) means cost scales with team size, not project size.
The AI stack
The core pipeline captures audio from expert interviews (Deepgram Nova-3 transcription + diarisation), extracts structured runbooks and domain-expertise maps via LLM (Opus 4.8 for high-stakes accuracy), indexes the resulting text via embeddings (Voyage for high fidelity), and surfaces relevant knowledge via semantic search. The main cost driver is audio transcription (~$0.0043/min); the main complexity is runbook extraction from noisy interviews.
Speech-to-text (audio transcription + speaker diarisation)
Converts recorded interviews (WAV, MP3) into timestamped transcripts with speaker identification (e.g., '[00:00–00:45] HR Manager: Tell me about your vendor-dispute process' '[00:45–02:30] Expert: We have a 3-step playbook...').
Deepgram Nova-3
$0.0043/min batch transcription; $0.0077/min for streaming; $0.12/hr for diarisation add-onPost-interview transcription (upload WAV/MP3, get transcript next day). Standard for consultancy workflows.
OpenAI Whisper
$0.006/min via APIOrganizations already invested in OpenAI; smaller interview volumes where higher cost is acceptable.
Google Cloud Speech-to-Text
$0.024–0.048/min depending on tierEnterprises with large interview volumes and need for multi-language support.
Our pick: Default to Deepgram Nova-3 for batch transcription ($0.0043/min) + diarisation ($0.12/hr). For a 1-hour interview, cost is ~$0.26 + $0.12 = $0.38. For consultancies running 10–20 expert interviews/month, budget $50–100/mo for transcription.
Runbook extraction (structured output from unstructured interview)
Parses interview transcript and extracts a formal SOP document: title, prerequisites, step-by-step instructions, expected outcomes, edge cases, escalation paths.
Claude Opus 4.8
$5 / $25 per M tokens (input / output)Mission-critical runbooks (e.g., database-recovery procedures, security incident response, financial audit processes) where accuracy matters more than cost.
Claude Sonnet 4.6
$3 / $15 per M tokensGeneral runbook extraction for administrative, operational, or technical SOPs where 95% accuracy is acceptable.
DeepSeek V4 Flash
$0.14 / $0.28 per M tokensLow-value runbooks or bulk processing where cost is more important than accuracy.
Our pick: Use Claude Opus 4.8 for the first 1–2 expert interviews (to establish SOP quality). Once you have a template, use Sonnet 4.6 for subsequent interviews (80% quality at 40% cost). For a 1-hour interview transcript (~8K tokens), Opus costs ~$0.12 input + $0.10 output = $0.22; Sonnet costs ~$0.024 + $0.03 = $0.054. Over 20 interviews, the Sonnet savings compound (~$3.20 total vs. $4.40 for Opus).
Embeddings + RAG (expertise indexing and future retrieval)
Embeds all captured runbooks and interview transcripts into a vector space, enabling semantic search ('Show me all knowledge about database recovery') and cross-expert dependency mapping ('If Sarah leaves, who else knows vendor management?').
text-embedding-3-small
$0.02 per 1M tokensMVP and early-stage consultancies with <50 experts captured.
Voyage voyage-3-large
$0.18 per 1M tokensMature knowledge-base (50+ experts) where retrieval quality significantly impacts business value.
Our pick: Start with text-embedding-3-small ($0.02/M) to keep MVP cost low. At 20 experts × 2K tokens per transcript, cost is ~$0.80/month. Graduate to Voyage when you have 50+ experts and retrieval quality becomes critical.
Storage + vector DB (Supabase + pgvector)
Stores interview metadata (date, expert name, department), transcripts, extracted runbooks, and embeddings. Supports multi-tenant isolation (each consultant's clients' data is separate).
Supabase Pro ($25/mo)
$25/moDIY consultants with 5–20 clients, each capturing 2–5 experts/year.
Self-hosted PostgreSQL + pgvector (Render / Railway)
$12–50/mo depending on storageLarger consultancies or enterprises managing 50+ expert captures.
Our pick: Use Supabase Pro for MVP. Upgrade to Team only if you're capturing >500 experts/month (rare).
Reference architecture
The system accepts audio files (recorded exit interviews), transcribes them via Deepgram (with speaker diarisation), passes the transcript to Claude Opus 4.8 for runbook extraction, embeds the resulting text via text-embedding-3-small, and indexes into Supabase pgvector. Downstream, consultants can query by department, role, or specific process ('Show me all runbooks about financial audits') to identify knowledge gaps. The main engineering challenge is handling variable-quality audio (some interviews are recorded on iPhone Voice Memo, others in a quiet conference room) and incomplete knowledge (experts often omit edge cases or forget steps).
Consultant uploads audio file + interview metadata (expert name, department, role, date)
Lovable frontend → Supabase bucketLovable form accepts MP3/WAV file upload. Metadata (expert_name, department, tenure, critical_processes) is captured. File is stored in Supabase Storage (public bucket) with a unique interview_id.
Deepgram async transcription job
Supabase Edge Function (async) → Deepgram APISupabase Storage webhook triggers edge function on file upload. Function calls Deepgram batch endpoint with audio file + diarisation enabled. Deepgram processes asynchronously; when done, webhook posts transcript JSON back to Supabase `interviews` table (transcript, speaker_segments, confidence_scores).
Claude Opus 4.8 runbook extraction
Supabase Edge Function → Claude APIOnce transcript is complete, another edge function is triggered. Function reads the transcript, calls Claude Opus 4.8 with a structured prompt: 'Extract a runbook with these fields: [title, purpose, prerequisites, steps (array of {step_num, title, description}), expected_result, edge_cases, escalation].' Claude returns structured JSON, which is stored in `runbooks` table.
Embed runbook + transcript, index in pgvector
Fly.io Python worker (daily batch) + text-embedding-3-small APIDaily batch job queries all runbooks with embeddings = NULL. For each, calls text-embedding-3-small to embed the runbook text (title + purpose + steps). Stores embedding in `runbook_embeddings` table (pgvector type).
Dashboard: Search + gap analysis
Lovable UI + Supabase queryConsultant types a search query (e.g., 'financial audit process'). Frontend calls Supabase function to: (1) embed query with text-embedding-3-small, (2) cosine-similarity search over `runbook_embeddings`, (3) return top-10 matching runbooks with metadata (expert name, department, criticality). Also shows succession gap: 'Database recovery known by: Sarah (retiring 2025-Q2). Backup owner: Tom (but not trained on new system). RISK: Critical.'.
Estimated cost per request
~$0.26 per hour of audio transcribed (Deepgram Nova-3 batch + diarisation); ~$0.18 per runbook extracted (Claude Opus 4.8, ~3K input + 1.5K output tokens); ~$0.001 per embedding (text-embedding-3-small on 1K-token runbook). Total per expert captured: ~$0.50 (1-hour interview) + $0.18 (runbook) + $0.001 (embedding) = ~$0.68 in AI costs.
Cost calculator
Drag the sliders to model your actual usage. The numbers update in real time so you can stress-test economics before writing a single line of code.
This calculator models monthly COGS for a workforce-transition consultancy running a knowledge-capture tool. Assumptions: each expert interview is 1–2 hours; each captured expert generates one primary runbook + optional transcript index for future retrieval. Fixed costs cover Supabase and Fly.io; per-expert costs cover Deepgram transcription, Claude extraction, and embeddings.
Estimated monthly cost
$35.93
≈ $431 per year
Calculator notes
- This model assumes 1–2 hour interviews; longer interviews increase Deepgram cost linearly. A 3-hour expert capture costs ~2× more in transcription.
- Supabase Pro tier supports ~10–20 concurrent consultancy clients. At higher volumes, upgrade to Team ($599/mo).
- If using Sonnet 4.6 instead of Opus 4.8, reduce per-expert extraction cost from $0.18 to $0.054 (70% savings). Quality trade-off: Sonnet misses edge cases ~10% of the time.
- Voyage embeddings ($0.18/M) are 9× more expensive but needed only if you have 50+ experts and require high-fidelity cross-expert dependency search. Start with text-embedding-3-small.
- This excludes customer support, legal review (2-party-consent audio recording), and consultant overhead.
Build it yourself with vibe-coding tools
In 1 weekend with Lovable Pro, you'll have a prototype that accepts audio uploads, transcribes them via Deepgram, extracts runbooks with Claude, and indexes the results. By Sunday night, you'll have a working demo for your first two clients.
Time to MVP
12–16 hours (1 weekend)
Total cost to MVP
$25 Lovable Pro + $60 API credits (Deepgram batch, Claude, embeddings)
You'll need
Starter prompt
Build a knowledge-retention tool with these features: 1. Authentication: Supabase Auth. Each consultant is a separate tenant. 2. Interview upload: Form that accepts MP3/WAV file + metadata (expert_name, department, title, interview_date, critical_processes). Store file in Supabase Storage. 3. Async transcription: After upload, call Deepgram batch API. When done, save transcript to `interviews` table (interview_id, transcript_text, speaker_segments). 4. Runbook extraction: Button to 'Extract runbook from transcript.' Calls Claude Opus 4.8 with prompt: 'From this interview transcript, extract a structured runbook with fields: [title, purpose, prerequisites (list), steps (list of {step_num, title, description, expected_result}), edge_cases (list), escalation_path].' Return as JSON. Save to `runbooks` table. 5. Dashboard: Show list of captured experts (name, department, date, runbook title). Click expert to see full runbook (formatted as markdown list). 6. Search: Text input to search runbooks by title / process name. Simple substring search for MVP (no embeddings yet). 7. Styling: Clean, professional UI; blues + grays appropriate for HR / organizational context. Database schema: - consultants (id, org_name, created_at) - interviews (id, consultant_id, expert_name, department, title, interview_date, audio_file_path, transcript_text, created_at) - runbooks (id, interview_id, consultant_id, title, purpose, prerequisites (text array), steps (JSON), edge_cases (text), escalation_path, created_at) Edge Functions: - POST /transcribe: takes interview_id + audio_file_path, calls Deepgram, stores transcript in interviews table - POST /extract-runbook: takes interview_id, calls Claude Opus 4.8, stores runbook in runbooks table
Paste this into Lovable
Follow-up prompts (run in order)
- 1
Add a 'Successor training' section: for each runbook, allow the consultant to note which other employees should be trained. Track training completion (checkbox list). Show a 'Knowledge gap' report: processes with only 1 expert (risky) vs. 3+ (safe).
- 2
Integrate with Resend.com to email runbooks to team members. Add a checkbox: 'Email this runbook to my team' which generates a formatted email (markdown → HTML) and sends via Resend.
- 3
Add embeddings-based search: after extracting runbooks, embed them via text-embedding-3-small, store embeddings in Supabase pgvector, and enable semantic search (e.g., 'database disaster recovery' matches a runbook called 'Server failure restoration').
- 4
Upgrade runbook extraction to use Voyage voyage-3-large embeddings for high-fidelity cross-expert dependency mapping. For each runbook, identify which other experts mentioned similar processes and flag as 'backup knowledge holders.'
- 5
Add a 'Transcript browser' UI: show the full interview transcript with speaker diarisation (color-coded by speaker). Let consultant manually highlight sections and add notes (e.g., 'This is the key vendor-dispute process'). Use highlighted sections to improve runbook extraction on next run.
Expected output
A working Lovable app where you can: (1) upload an interview audio file, (2) get back a transcript (via Deepgram), (3) click 'Extract runbook' to get a structured SOP, (4) search for runbooks by process name, (5) see a list of captured experts with their departments. No production-grade compliance or security hardening required — this is a proof-of-concept.
Known gotchas
- !Audio file upload via Lovable can timeout if the file is >100MB or the network is slow. Add a progress bar and allow pause/resume, or limit file size to 50MB.
- !Deepgram's free tier ($12.50 credits) is good for ~3 hours of audio. Test with short clips first (10–15 minutes). Upgrade to a paid plan ($30–50/mo) once you're confident in the workflow.
- !Claude Opus 4.8 is expensive for testing ($5/$25 per M tokens). During MVP, do 1–2 real extractions and review the quality before scaling. If quality is good, you're ready for production.
- !Interview transcripts can be very long (1-hour audio = ~10K tokens). Ensure your Claude prompt includes a token budget (e.g., 'Extract only the 5 most critical steps' or 'Summarize edge cases to <500 words'). Otherwise, you'll hit Claude's output token limits.
- !Deepgram diarisation ($0.12/hr add-on) is useful but adds cost. For MVP, disable diarisation and manually edit the transcript if needed. Enable it only when you have 10+ interviews and the time savings justify the cost.
- !Supabase Storage is free but has limits on file size (100MB per file) and concurrent uploads. For MVP, test with <10 interview uploads. If scaling to 50+ uploads/month, consider upgrading to Supabase Pro ($25/mo) which includes 100GB storage.
Compliance & risk reality check
Knowledge-retention tools handle sensitive employee data: recorded interviews, personal information, and proprietary processes. This implementation must respect employee privacy, recording-consent laws (vary by US state), GDPR for international experts, and intellectual-property protection (ensuring documented processes are owned by the company, not the departing employee).
Recording consent (two-party-consent state laws)
In 11 US states (California, Connecticut, Delaware, Florida, Illinois, Maryland, Michigan, Montana, New Hampshire, Pennsylvania, Washington), both parties must consent to audio recording. In 'one-party-consent' states, only one party (e.g., the HR person doing the recording) must consent. Failing to get written consent in two-party-consent states can result in criminal liability (felony wiretapping in some states) and civil liability ($1K–$5K per instance in CA, IL).
Mitigation: Before any interview, have the departing expert sign a consent form: 'I consent to this interview being recorded, transcribed, and archived for knowledge-retention purposes.' Lovable can add a checkbox: 'I consent to recording and storing this interview per [state] law.' Store consent records in Supabase `interview_consents` table (consultant_id, interview_id, expert_name, state, consent_date, consent_form_pdf_url). If recording takes place in a two-party-consent state, require explicit written consent and store the signed PDF. Offer phone + video call recording via platforms that handle consent automatically (e.g., Zoom with 'Record consent' enabled).
GDPR for EU employees / international experts
If a departing expert is based in the EU, their interview transcript and personal data (name, email, recorded voice) are subject to GDPR. Recording and processing audio without explicit consent (Art. 6(1)(a) lawful basis) is a violation. Data must be stored in EU data centers, and the person has the right to erasure (Art. 17).
Mitigation: Add a 'Data location' field to the interview form: select 'EU' / 'US' / 'Other'. If EU, require explicit GDPR consent: 'I give permission for my interview to be recorded and processed under GDPR Art. 6(1)(a) consent.' Store in an EU-hosted Supabase region (if available). Add a 'Request deletion' button that lets the expert request erasure; comply within 30 days by deleting interview transcript, audio file, runbook, and embeddings from the database. Keep an immutable audit log that deletion was requested and executed.
Intellectual property / trade-secret protection
Runbooks extracted from departing experts document company processes that may constitute trade secrets or proprietary know-how. If the expert leaves and the extracted runbooks are not contractually owned by the company, the expert could claim IP rights or steal the documented process.
Mitigation: Require all departing experts to sign an IP-assignment addendum: 'All knowledge, processes, and SOPs documented in this interview are the property of [Company] and are not my personal intellectual property.' Store this addendum in Supabase (one per interview). If the company is using the tool to capture knowledge from contractors (not full-time employees), IP assignment is critical — ensure legal review before capture. Mark runbooks as 'Proprietary — Internal Use Only' in the database and never export without redaction.
HR data sensitivity and access control
Interview transcripts and runbooks contain sensitive HR data (employee departure, compensation-related context, performance issues). Access must be limited to authorized HR / management staff.
Mitigation: Implement role-based access control (RBAC) in Supabase RLS: only HR Manager / Director roles can view interview transcripts. Regular employees see only the final runbook (without the expert's name). Add an audit log (immutable table) of who accessed which interview / runbook and when. Include a note in Supabase security audit: 'Interview data is HR-sensitive; monitor access logs for anomalies.'
Data residency and backup retention
Some companies (especially in finance, healthcare, utilities) have data-residency requirements: interview data must stay within the company's country or region. Supabase backups and pgvector indexes must also respect residency.
Mitigation: In Lovable form, add a 'Data residency requirement' dropdown: 'US-only' / 'EU' / 'None'. If US-only, ensure Supabase is deployed in a US region (Supabase Pro supports regional selection). If EU, use an EU-hosted region. Document in a data-processing agreement (DPA) that Supabase backups are retained in the same region. Inform customers that US-based Supabase instances are subject to US government data requests (ECPA / SCA); for sensitive data, recommend self-hosted PostgreSQL + pgvector.
Build vs buy: the real math
6–9 weeks
Custom build time
$18,000–$32,000
One-time investment
10–20 expert-capture projects
Breakeven vs buying
A DIY Lovable build costs $25 Lovable Pro + ~$40–60/mo infra, allowing you to charge $499–$1,999 per departing-expert project (4–8 hours of work, COGS ~$5–15 in AI tokens) with 95%+ gross margin. At $750/project average, you break even on the $18K–$32K custom build after 24–64 projects. Given that the Boomer-retirement wave projects 10K+ daily US retirements through 2030, early-to-market consultancies will see consistent project flow. The custom build makes financial sense only if you've already validated demand with 5+ DIY projects and foresee 100+ projects/year. For bootstrapping, DIY is the clear path.
Skip the DIY — RapidDev builds the production version
A Lovable MVP gets you a demo. Production needs auth that doesn't leak data, AI calls that don't bankrupt you, observability when models drift, and code you can audit. That's what we ship.
Discovery call (free)
30 minWe map your exact AI Knowledge Retention Tool use case: who uses it, target volume, AI model choice, integrations, compliance scope. You get a detailed scope document and fixed-price quote within 48 hours.
AI-accelerated build
6–9 weeksOur engineers use Claude Code, Lovable, and custom tooling to ship 3–5x faster than agencies. You see weekly progress in a staging environment — not a black box.
Launch + handoff
1 weekWe deploy to your infrastructure, transfer the GitHub repo, set up CI/CD and monitoring, and train your team. You own 100% of the source code, prompts, and model configurations.
What you get
Timeline
6–9 weeks
Investment
$18,000–$32,000
vs SaaS
ROI in 10–20 expert-capture projects
30-min call. Fixed-price quote within 48 hours. No commitment.
Frequently asked questions
How much does it cost to build a knowledge-retention platform?
A full custom build via RapidDev costs $18K–$32K (6–9 weeks). A DIY Lovable MVP costs $25 Lovable Pro + ~$60 API credits (1 weekend). Monthly infra is $30–60 (Deepgram, Supabase, Fly.io worker). If charging $499–$1,999 per departing-expert project, your COGS is $5–15 per expert (transcription + runbook extraction + embedding), yielding 95%+ gross margin. Breakeven on custom build is after 24–64 projects.
How long does it take to ship a knowledge-retention platform?
DIY with Lovable: 1 weekend to a working MVP (audio upload → transcription → runbook extraction → search). Custom build with RapidDev: 6–9 weeks to production with multitenancy, compliance (GDPR, 2-party-consent recording laws), audit logging, and integration with Voyage embeddings for high-fidelity expertise mapping.
Can RapidDev build this for my company?
Yes. RapidDev has shipped 600+ applications and multi-tenant SaaS platforms. For a knowledge-retention platform, typical scope is $18K–$32K over 6–9 weeks, covering Supabase schema, Deepgram + Claude integration, runbook extraction, and compliance (GDPR, 2-party-consent recording consent, IP assignment). Email seopartner@rapidevelopers.com for a free 30-min consultation.
Should I use Opus 4.8 or Sonnet 4.6 for runbook extraction?
Use Opus 4.8 for your first 1–2 expert interviews to establish quality baseline (~$0.18 per expert). Once you're confident in the structure, switch to Sonnet 4.6 for subsequent interviews (cost drops to ~$0.054, 70% savings). Quality trade-off: Sonnet misses edge cases ~10% of the time, but for straightforward processes, it's sufficient. For mission-critical procedures (database recovery, security incident response), stick with Opus.
How do I handle recording consent in two-party-consent states?
Before recording, have the expert sign a consent form: 'I consent to this interview being recorded and stored per [state] law.' In California, Illinois, and other two-party-consent states, failing to get written consent is a criminal offense (felony wiretapping). Lovable can add a checkbox + PDF signature capture. Store the signed consent in Supabase for audit purposes. Offer Zoom recording as an alternative (Zoom handles consent prompts automatically).
How do I ensure GDPR compliance for EU experts?
Add a 'Data location' field: select 'EU' / 'US' / 'Other'. If EU, require explicit GDPR consent (Art. 6(1)(a)). Store data in EU-hosted Supabase regions. Implement a 'Request deletion' feature that lets experts request erasure; comply within 30 days. Include a Data Processing Agreement (DPA) with your customers explaining how expert data is processed.
Can I integrate with Zoom or other recording platforms?
Yes. Zoom's API allows you to pull recorded meetings and transcripts. For DIY, you can: (1) Schedule interview in Zoom, (2) Zoom auto-records and transcribes, (3) Use Zoom API to fetch transcript, (4) Pass transcript to Claude for runbook extraction. Slack integration: after recording, post the transcript to a Slack channel; Lovable bot reads the message and triggers runbook extraction. This avoids separate file uploads.
How many experts can one Supabase instance support?
Supabase Pro ($25/mo) supports ~20–50 experts with full interview transcripts before hitting query limits. For 100+ experts, upgrade to Supabase Team ($599/mo) or use pgvector clustering to shard experts across multiple projects. Most consultancies start with Pro and upgrade only after 30+ captures.
How do I prevent departing experts from stealing documented runbooks?
Require the expert to sign an IP-assignment addendum: 'All knowledge documented in this interview is the property of [Company].' Store the signed IP assignment in Supabase audit log. Mark all runbooks as 'Proprietary — Internal Use Only' in the database. Never export runbooks without redacting the expert's name and company-identifying information. If the expert leaves and joins a competitor, the documented runbook remains company IP.
Want the production version?
- Delivered in 6–9 weeks
- You own 100% of the code
- AI cost monitoring built in
30-min call. No commitment.