The Problem
- 5 products, 5 systems → a 3-minute answer takes 10 minutes
- No self-service → every simple question becomes a support ticket
- AI can't act → chatbots talk but don't execute — no reschedule, no lookup, no fixes
One API and one data model — the single layer everything runs through. Agents, customers, AI — same data, same rules, one source of truth. Build it once, everything else plugs in.
How It Works
Retool, AI agents, production code — everything reads and writes through a single backend.
One API for everything
flip ↻No-code team prototypes in days. AI handles tickets. Production code runs auth and payments. Every consumer goes through the same door.
In practice
flip ↻Update contracts in one place. Retool, AI, and the production app get the change automatically. No syncing, no patching.
Data isolation per customer
flip ↻Customer A's agent sees Customer A's data — and nothing else. Every query scoped by customer ID from day one. GDPR built in, not bolted on.
I've shipped this
flip ↻I built this pattern in Odys — every query scoped by professional ID, row-level isolation from day one. Same approach here with customer ID. It already works.
AI layer — what it enables
AI doesn't just respond — it executes. Reschedule an appointment, look up a contract, restart a device. All through the same system agents use.
Keep It Solid
Speed is the advantage — my job is to protect it.
Graduation rule
flip ↻Someone builds a prototype in Retool in two days. It works. People use it. A month later, 200 agents depend on it. That's when it graduates to real code — before it breaks.
The handoff
flip ↻I sit with the builder, understand what they built, and rebuild it with proper auth, tests, and deployment — keeping the same API so nothing breaks downstream.
Test at boundaries
flip ↻No-code tool sends wrong data to the API? Automated tests catch it before any user sees a problem.
How it works
flip ↻Contract tests at every API boundary. If any consumer sends unexpected data, the test suite flags it.
Monitor everything
flip ↻Feature goes live Monday. By Tuesday we know: usage, speed, errors. If something breaks, we know in minutes, not days.
In practice
flip ↻Sentry for errors, analytics for usage. Every feature ships with monitoring. No blind spots.
Cleanup cycles
flip ↻Every few sprints: stop features, fix foundations. Scheduled, not reactive. This prevents speed from turning into debt.
The habit
flip ↻Predictable maintenance beats emergency firefighting. Build the habit of looking back into the team's rhythm.
What I'd Build
Learn first, build second, measure third.
Weeks 1–4
Learn the systems. Build the customer portfolio view — one screen, all products.
Weeks 5–8
Agent cockpit in daily use. Weekly feedback loops. Fix what's annoying.
Weeks 9–12
AI agents handle simple tickets. Measure if CSAT actually improves.
This is how it looks in practice. Two views on the same system. One action updates everything instantly.
Maria Schmidt
Customer since 2024 · Berlin · 5 products
Contracts
Need help?
My Queue · 4 open
Maria Schmidt
5 products · Berlin · Since 2024
Current Ticket
“I need to change my wallbox installation date — I won't be home on April 28”
via WhatsApp · 12 min ago
⚡ AI Suggestion
Next available date: May 2. Offer to reschedule? The customer has rescheduled once before (Mar → Apr).
What I'd Ask First
- What tools do agents use today? What's the biggest daily frustration?
- Which no-code prototypes exist and which are closest to needing real code?
- What data is already available and what's missing?
- How does the no-code/AI team work? What's the handoff process?
- Who are the key stakeholders in Operations and Service?
This is a framework — not a final answer. The real strategy starts by listening.