Your Senior Engineer in a Box
Stop answering "How does this API work?" Find code implementations instantly, trace requirements to pull requests, search across repos and docs simultaneously, and onboard new developers 2x faster with an AI that understands your technical stack.
Why Engineering Teams Lose Velocity
Engineers spend 30% of their time answering "how does this work?" questions, searching for implementation details, and debugging issues that someone else already solved. Knowledge is trapped in Slack threads, old PRs, and engineers' heads.
💬 "How Does X Work?" Overload
Senior engineers get pinged 10+ times per day: "How does authentication work?" "Where's the payment API?" "Who owns the email service?" Each interruption costs 15 minutes of context switching. Productivity drops. Burnout increases.
📂 Docs Scattered Everywhere
API docs in Confluence. Architecture diagrams in Google Slides. Code comments in GitHub. Runbooks in Notion. Slack threads with tribal knowledge. Engineers waste hours searching across 6 tools to find how to do anything. Often easier to ask a person than find the doc.
⏰ New Hires Lost for Months
New engineers take 90+ days to become productive. README files are outdated. No one knows where to find the setup guide. New hire asks 50 questions in first week—all questions that last 5 new hires asked. Onboarding debt compounds.
🔍 "Why Was This Built This Way?"
Engineer joins team, sees confusing architecture decision, asks "why is this designed like this?" No one remembers. Original engineer left. PRs from 2 years ago have no context. Team rewrites code that was intentional. Rework and technical debt accumulate.
How Docmet Transforms Engineering Workflows
Unify code, documentation, and conversations into a single searchable knowledge base that understands your technical stack and answers questions like a senior engineer would.
🔍 Search Across Repos and Docs Simultaneously
Ask: "How does OAuth authentication work in the mobile app?" Docmet searches: (1) GitHub repos for relevant code files (auth.ts, oauth.config.js), (2) Confluence/Notion for architecture docs, (3) Jira tickets for implementation discussions. Returns synthesized answer with code snippets + doc links + related PRs.
🔗 "Which Code Implements This Requirement?"
Product manager asks: "Did we implement the 2FA feature from PRD-2024-Q3-07?" Docmet traces: PRD document → Jira tickets → GitHub PRs → Deployed code. Generates traceability matrix showing requirements mapped to implementations. Essential for compliance, audits, and stakeholder updates.
📚 "How Do I Call the Payment API?"
Developer asks natural language question. AI returns: (1) API endpoint URL, (2) Request/response schema with examples, (3) Authentication requirements, (4) Code snippet in developer's language (Python, JavaScript, cURL), (5) Rate limits and error codes. Auto-generated from OpenAPI specs, code comments, and usage examples.
🐛 "Why Is the Payment Service Timing Out?"
DevOps engineer investigating production issue. Asks Docmet: "Find all docs related to payment gateway timeout errors." AI retrieves: (1) Past incident reports, (2) Runbook for payment service debugging, (3) GitHub issues with similar symptoms, (4) Datadog dashboard links, (5) Architectural diagram showing dependencies. Accelerates troubleshooting.
🎓 New Developer Self-Service
New engineer accesses personalized onboarding checklist: (1) Set up dev environment (with links to setup scripts), (2) Clone repos and run tests, (3) Read architecture overview, (4) Complete code review training, (5) Ship first PR. Each step includes interactive guides. Progress tracked. Manager sees completion dashboard.
🏗️ "Why Did We Choose MongoDB Over PostgreSQL?"
Engineer questions past architecture decision. Docmet searches ADRs, design docs, and email threads to find original decision rationale with trade-off analysis. Preserves institutional knowledge. Prevents costly rework or uninformed changes.
Proven Engineering Workflows
Developer Onboarding Acceleration
Scenario: Fast-growing SaaS company hiring 10-15 engineers per quarter. Historical time-to-first-PR: 4 weeks. Time-to-productive: 12 weeks. Engineering velocity suffering due to senior engineers spending time onboarding.
Docmet Workflow:
- Pre-Boarding: New hire receives Docmet access before day one. Reads: (1) Engineering culture guide, (2) Architecture overview, (3) Tech stack explainer
- Day 1 Checklist:
- "Set up local dev environment" (step-by-step guide + troubleshooting FAQ)
- "Clone repositories and run tests" (commands with explanations)
- "Configure IDE and debugging tools" (VSCode/IntelliJ setup guides)
- First Week Self-Service:
- "How does CI/CD pipeline work?" → Docmet explains GitHub Actions workflows
- "Where is authentication implemented?" → Docmet shows auth.service.ts with explanatory comments
- "How do I run integration tests locally?" → Docmet provides Docker Compose commands
- Shadowing & Pairing: Manager assigns small bugs to new hire. New hire self-serves context via Docmet, reducing senior engineer interruptions
- First PR: New hire ships meaningful PR in Week 2 (vs. Week 4 historically)
Results:
- Time-to-first-PR: 10 days (down from 28 days, 64% reduction)
- Senior engineer time per new hire: 8 hours (down from 40 hours)
- New hire confidence: 4.5/5 (up from 2.8/5)
- 15% increase in engineering velocity due to reduced onboarding drag
Production Incident Response
Scenario: E-commerce platform experiences payment gateway timeout spike during Black Friday. Revenue at risk. Engineering team needs to debug fast but relevant knowledge is scattered.
Docmet Workflow:
- Initial Alert: PagerDuty alerts on-call engineer: "Payment API 95th percentile latency exceeds 5 seconds"
- Context Retrieval: Engineer asks Docmet: "Payment gateway timeout troubleshooting"
- AI Response:
- Past Incidents: Links to 3 prior incidents with similar symptoms (from GitHub issues + incident reports)
- Runbook: Step-by-step debugging guide (check DB connection pool, inspect gateway logs, verify API rate limits)
- Architecture: Diagram showing payment service dependencies (API gateway → payment service → Stripe → database)
- Recent Changes: PRs merged in last 48 hours that touched payment code
- Datadog Dashboard: Direct link to relevant metrics and logs
- Root Cause Found: Database connection pool exhausted (similar to incident from 6 months ago). Solution documented in past incident report.
- Mitigation: Increase connection pool size (config change), deploy hotfix in 15 minutes
- Post-Mortem: Docmet auto-generates incident timeline using Slack messages, PagerDuty alerts, and code changes
Results:
- Time-to-resolution: 45 minutes (vs. 4+ hours typical for payment issues)
- Revenue saved: $2M+ (estimated based on Black Friday transaction volume)
- Knowledge captured: Incident added to knowledge base for future
- MTTR (Mean Time to Resolution) reduced 73% for similar incidents
API Documentation Automation
Scenario: API-first company with 200+ endpoints across 15 microservices. Manual API documentation is outdated and incomplete. Developers constantly ask "how do I call X endpoint?"
Docmet Workflow:
- OpenAPI Spec Import: Docmet ingests OpenAPI/Swagger specs from all microservices (auto-synced from GitHub)
- Code Comment Extraction: Parses JSDoc/PyDoc comments explaining business logic and edge cases
- Example Generation: Scans test files for API usage examples. Extracts sample requests/responses.
- Natural Language Interface: Developers ask: "How do I create a new user with the User API?"
- AI Response:
- Endpoint: POST /api/v2/users
- Authentication: Bearer token required (get from Auth API)
- Request Body Schema: { email: string, password: string, name: string }
- Example cURL:
curl -X POST https://api.example.com/v2/users \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"email":"[email protected]","password":"secure123","name":"John Doe"}'
- Response: 201 Created with user object
- Error Codes: 400 (invalid email), 409 (email already exists), 429 (rate limit)
- Related Endpoints: GET /users/{id}, PATCH /users/{id}, DELETE /users/{id}
- SDK Generation: Links to client libraries (Python SDK, JavaScript SDK) with usage examples
Results:
- API-related support questions: 85% reduction (from 120/month to 18/month)
- Documentation accuracy: 100% (auto-synced from code, never stale)
- Developer satisfaction with API docs: 4.7/5 (up from 2.3/5)
- Partner integration time reduced 60% (better docs = faster adoption)
Built for Engineering Workflows
💻 Semantic Code Understanding
Docmet doesn't just search code by keywords—it understands semantic meaning. Ask "Find functions that validate email addresses" and it finds relevant functions across Python, JavaScript, Java, Go codebases even if they're named differently (`validate_email`, `isValidEmail`, `checkEmailFormat`). Supports 20+ programming languages.
🕰️ "Who Changed This and Why?"
Ask about specific code: "Why was error handling removed from paymentProcessor?" Docmet analyzes Git blame, retrieves relevant commits and PRs, shows commit messages and code review discussions. Reconstructs decision context even if original engineer left.
🔗 "What Services Depend on This API?"
Critical for impact analysis. Ask: "What breaks if I change the User API schema?" Docmet analyzes: (1) Direct API callers (code analysis), (2) Services with dependencies in package.json/requirements.txt, (3) Documented integrations. Generates dependency graph visualization. Prevents breaking changes.
✅ "Are There Tests for This Code?"
Developer asks: "Is the payment processing function tested?" Docmet finds corresponding test files, shows test coverage percentage, highlights untested edge cases. Encourages better testing practices. Links to testing guidelines.
💬 Answer Questions Without Leaving Slack
Engineers live in Slack. Deploy Docmet bot in engineering channels. Ask inline: "@docmet how do I deploy to staging?" Bot responds with runbook and commands. No context switching. High adoption because it meets engineers where they work.
🏗️ Auto-Generated System Diagrams
Ask: "Show me the architecture of the payment service." Docmet parses code, Docker Compose files, Kubernetes configs, and documentation to generate architecture diagram showing: services, databases, message queues, external APIs, data flows. Keeps diagrams up-to-date as code changes.
Connects to Your Dev Stack
Seamless Developer Integration
📁 GitHub / GitLab / Bitbucket
Sync: Bi-directional sync with repos. Docmet indexes: source code, commit history, pull requests, issues, wikis. Permissions: Respects GitHub access controls (private repos only visible to authorized users). Real-Time: Webhook-based updates (new PRs indexed within minutes). Search Scope: Search specific repos, branches, or across entire org.
🎫 Jira / Linear / Asana
Link Code to Requirements: "Which Jira ticket requested this feature?" Docmet connects tickets to implementing PRs. Sprint Planning: "Show all P0 bugs assigned to my team." Status Updates: "What's the status of PROJ-1234?" retrieves ticket details, comments, assignee.
📚 Confluence / Notion / GitBook
Unified Search: Search code AND docs simultaneously. "How does SSO work?" returns both code implementation and Confluence architecture doc. Sync: Auto-sync on doc updates. Versioning: Track doc versions alongside code versions.
🚀 GitHub Actions / GitLab CI / Jenkins
Pipeline Docs: "How do I trigger production deployment?" Links to CI/CD configuration files with explanations. Build Failures: "Why did build #1234 fail?" Retrieves build logs, error messages, and similar past failures with solutions.
📊 Datadog / New Relic / Splunk
Metric Correlation: "Show dashboard for payment API latency." Direct links to relevant Datadog dashboards. Log Search: "Find errors in payment service logs from last hour." Queries log aggregator with structured search.
💬 Slack / Microsoft Teams / Discord
Bot Deployment: Deploy in engineering channels. Contextual Answers: Bot understands conversation context. If discussing a PR, bot can answer questions about that PR. Thread Archival: Important Slack decisions auto-indexed for future reference.
Measurable Engineering Velocity Gains
Results from engineering team deployments
Support Deflection
Self-service for "how does this work?" questions
Faster Onboarding
Time-to-first-PR for new engineers
Faster Incident Resolution
MTTR reduction with contextual knowledge
Velocity Increase
Team output due to reduced interruptions
*Based on engineering productivity metrics from SaaS and tech company deployments (Q3-Q4 2025)*
From Pilot to Eng-Wide in 3 Weeks
Proven deployment process for engineering teams
Platform Integration & Knowledge Setup
Connect Docmet to GitHub/GitLab, Jira, Confluence, Slack. Configure OAuth permissions and RBAC (junior devs, senior engineers, DevOps). Select 2–3 key repositories for initial indexing. Import architecture docs, ADRs, runbooks and Configure taxonomy (services, teams, tech stack tags) Train AI on company-specific terminology (internal services, acronyms) Run initial code indexing (overnight for large repos).A fully connected, permission-aware knowledge base with structured documentation and indexed code
Use Case Validation & Pilot
Validate search and answers with known queries: “How does authentication work?”, “Where is the user API?”, “How do I deploy to staging?”. Refine AI responses based on engineering feedback Validate accuracy across docs, code, and tickets Create and deploy Slack bot in engineering channels Run a pilot with 10–20 early adopters. Proven accuracy on real engineering workflows and confidence in day-to-day usage
Organization Rollout & Adoption
Announce Docmet to the full engineering organization. Run a 10-minute demo at team all-hands. Share a cheat sheet of high-value queries. Embed Docmet into new-engineer onboarding, Monitor adoption metrics and support deflection. Collect ongoing feedback. Conduct quarterly reviews with engineering leadership. Org-wide adoption, measurable productivity gains, and continuous improvement
Engineering ROI
Calculate Your Engineering ROI
Engineering Team ROI Model
Assumptions (Mid-size tech company):
- Engineering team: 50 developers (mix of junior, mid-level, senior)
- Average fully-loaded cost per engineer: $200K annually = $100/hour
- Time spent on "knowledge seeking" per engineer: 6 hours/week (30% of time)
- Searching for documentation: 2 hours/week
- Asking colleagues questions: 2 hours/week
- Code archaeology (understanding old code): 2 hours/week
- Total weekly "knowledge seeking" time: 50 engineers × 6 hours = 300 hours/week
Without Docmet:
- Weekly cost of knowledge seeking: 300 hours × $100 = $30,000
- Annual cost: $1.56M in engineering time spent searching for knowledge
With Docmet:
- Reduction in knowledge seeking time: 40% (via self-service)
- New weekly time: 180 hours (120 hours saved)
- Weekly cost: 180 hours × $100 = $18,000
- Annual cost: $936K
- Docmet subscription (Business plan): $499/month = $6K annually
Annual Savings:
- Engineering time savings: $624K annually
- Velocity increase: ~15% (engineers spend less time searching, more time building)
- Docmet cost: $6K
- Net ROI: 10,300% (103x subscription cost)
- Payback period: < 3 days
Onboarding-Specific ROI
New Engineer Onboarding:
- Historical time-to-productivity: 12 weeks
- Senior engineer mentoring time per new hire: 40 hours ($4,000)
- Company hires: 20 engineers per year
- Annual onboarding cost: 20 × $4,000 = $80,000
With Docmet:
- Time-to-productivity: 6 weeks (50% reduction)
- Senior engineer mentoring time: 8 hours ($800)
- Annual onboarding cost: 20 × $800 = $16,000
- Onboarding savings: $64K annually
Total Engineering ROI:
- Knowledge seeking savings: $624K
- Onboarding savings: $64K
- Total annual benefit: $688K
- ROI: 11,367% on $6K investment
What Engineering Leaders Say
From the CTOs and Engineering Managers using Docmet
“"Our engineering velocity increased 15% simply because we stopped interrupting each other. Docmet became the first place engineers look for answers. Instead of pinging senior engineers in Slack 50 times a day, the team self-serves from Docmet. Our senior engineers finally have time to architect instead of answering 'where's the docs?' Junior engineers ramp up 2x faster. Best ROI of any tool we've deployed."”
Accelerate Engineering Velocity
See how Docmet reduces interruptions, accelerates onboarding, and helps engineers find answers instantly. Schedule a demo with code search and your actual repositories.
Common Engineering Questions
Engineering FAQ