Most domain experts don't begin with a public SaaS. They begin by productizing their own workflow. That's not a detour. It's the fastest path to a durable product with real demand.
This week, we build exactly that: an internal AI system that automates your signature method, proves value on your desk first, and establishes the foundations (identity, data model, workflows, prompt discipline) for everything that comes next.
Who this week is for
- You sell expertise (legal ops, CFO advisory, compliance, creative ops, insurance adjusting, etc.).
- You're drowning in repeatable work that follows a pattern.
- You could sell this as software—but you don't want to invent a product before you own the outcomes.
If that's you, Week 1 is where the flywheel starts.
What we build (outcomes)
- A working internal tool that runs your process end-to-end on 1–2 representative client cases.
- Structured intake + slot tracking so inputs are reliable and reusable.
- A deterministic workflow graph (no prompt spaghetti) that orchestrates AI + human steps.
- Prompt versions + deployment pointer so changes are safe and reversible.
- Operational traces (who ran what, when, why) so we can measure time saved and quality.
You'll finish Week 1 with a tool you actually use tomorrow.
Business rationale (why not public SaaS yet)
- Proof before scale. Internal runs confirm value, surface edge cases, and kill bad ideas cheaply.
- No rewrites later. We build the platform as if it will become a SaaS—so when you're ready, it already is.
- Sales narrative. "We run our practice on this system" converts better than "we're building an app."
The simple architecture behind Week 1
Same bones now as later—just fewer users.
- Frontend: a private UI with a guided Interview Mode (one question at a time; required fields enforced).
- Workflow runner: a graph-driven runtime (nodes/edges) that coordinates models, tools, and approvals.
- Prompt engine: immutable versions with a pointer for safe roll-forward/rollback.
- Observability: event stream → timeline view (each step, inputs, outputs, latency, cost).
That's it. Lightweight, production-minded, and honest about how real systems work.
The Week-1 “golden path” (what the user does)
- Start a case → give the system a clear objective ("Produce a CFO health brief from these docs").
- Guided intake → SlotTracker gathers the essentials (industry, revenue band, constraints, deadlines).
- System runs → Nodes execute: extract → analyze → draft → request clarification → finalize.
- Review & edit → You adjust where necessary; edits are captured as feedback signals.
- Deliver → Output is versioned, reproducible, and tied to inputs.
If we can run that loop reliably twice by end of week, we've won Week 1.
Guardrails that matter (technical edge, plain language)
- Deterministic over “clever.” Nodes are predictable; models are replaceable.
- Prompts are code. Every change is versioned; the active pointer moves atomically.
- Circuit breakers. If a model degrades or costs spike, we can fail over without drama.
- Explainability. Every result links back to inputs and steps. No black boxes.
What we don't do yet
- Multi-tenant billing, plans, and seats
- Complex permissions and sharing
- Public marketing site and self-serve signups
We'll get those. But only after the workflow pays its own way internally.
Success criteria (end of Week 1)
- Cycle time: One case completes ≥50% faster than your manual baseline.
- Quality: Errors trend down or are explainable; reviewers trust the draft.
- Stability: Same inputs → same path → similar outputs (within a small tolerance).
- Repeatability: Two distinct cases run cleanly through the full graph.
If we miss any of these, we fix the system here—before we scale it.
Common traps (and how we avoid them)
- Trap: "Let's let the AI figure it out."
Fix: We encode the decision points explicitly. Models are instruments, not project managers. - Trap: Hard-coding prompts in code.
Fix: Immutable prompt versions + pointer with an audit trail. Roll forward or back safely. - Trap: Chasing UI polish before flow stability.
Fix: Ship a clear, friendly internal UI. Pretty helps; predictable wins. - Trap: One giant "do everything" prompt.
Fix: Small, composable steps with testable inputs/outputs.
What you'll have in hand
- A private tool you can run tomorrow for a real client.
- Measurable time saved, with logs to back it up.
- An architecture that expects to become a SaaS.
- Confidence—and a narrative your future customers will believe.
What's next (Week 2 teaser)
Next week we standardize client-facing artifacts and add a review loop that captures edits as training signals. The goal: make "good" the default and "excellent" the habit.
From Internal System → Repeatable Playbook.