How I'd build MSP AI
I'd reach for FastAPI as the core API, Next.js for the frontend, and Postgres for the database, with Stripe handling billing and Twilio for SMS notifications. The real work is integrating with 6+ PSA and RMM platforms - that's 60-80% of the project. Rough estimate: 320-360 hours, call it 4-5 weeks of full-time work. The integration layer is where the schedule slips or holds, so I'd front-load that risk.
Day-by-day plan
Day 1-2: Auth schema using Clerk, multi-tenant org model in Postgres, Stripe product and customer creation, basic JWT flow. Day 3-4: Next.js app shell with tenant routing, layout components, protected routes. Day 5-6: First PSA integration - likely ConnectWise, since most of your target MSPs use it. I'd use their API client to sync tickets and contacts, handle API rate limits with a job queue. Day 7: Draft generation endpoints wired to Claude API with basic prompt templates. Day 8-9: Ticket triage agent - scoring tickets by urgency, extracting client context, routing to the right tech. Day 10: SLA monitoring logic and alert hooks. Day 11: Monthly report generation (templates, PDF export via Tailwind CSS). Day 12-13: Customer onboarding - API key provisioning, PSA credential encryption, test sync. Day 14: Production hardening, database backups, error logging with Sentry, staging deploy on a single Caddy reverse proxy.
What's hard about this build
The integration layer will kill you if you're not careful. ConnectWise, Autotask, and Kaseya all expose tickets differently - custom fields, status mappings, client hierarchies diverge. One MSP's "Waiting on Customer" is another's "On Hold," and if your draft references a field that doesn't exist on their platform, you look stupid. You also need to handle authentication per-tenant: each MSP will give you credentials to their PSA instance, and you're now storing encrypted secrets at scale. When ConnectWise ships a breaking API change - and they will - you need a rollout plan that doesn't break 60 customers simultaneously. Rate limiting is silent death: sync too aggressively, hit the API limit, and your scheduled jobs start missing tickets. Testing this requires fixtures from real MSPs or you'll ship bugs that only surface in production.
What's fast because of AI
Scaffolding FastAPI routes and generating Postgres migrations compressed into hours instead of a day per endpoint. Claude writes the boilerplate; I review and adjust. Test case generation is the same way - Claude enumerates edge cases I'd miss solo, like "what if a ticket has no assigned technician" or "what if the sync API returns a 429." Prompt engineering for the triage agent happens in an afternoon instead of a week of iteration; Claude helps me reason through scoring logic, handle ambiguous urgencies, and structure the context window. UI copy and onboarding flows come fast too - Claude drafts microcopy, error messages, and the help text so I'm not inventing tone. Debugging production errors is quicker when I ask Claude to enumerate what could cause a timeout in the PSA API layer, then I test those hypotheses instead of guessing.
How I'd hand it off
I'd record three Loom videos: one walking the admin dashboard and settings, one showing how to add a new PSA platform, and one covering the on-call playbook. You'd get a runbook in Markdown covering deployment steps, database migrations, and credential rotation. Linear board transfer with all known tech debt flagged. I'd do a 30-day pager rotation - any production incident gets a Slack notification to my number, and I'm response-time SLA'd at 2 hours for critical issues. All credentials and API keys go into a shared password manager with a handoff document. Last thing: I'd pull a clean staging environment snapshot so you can onboard new customers or test integrations without touching production.