Solutions architect
The data model, the integrations, and the build order are decided before a line of code, so the scope you see is the scope you get.
Founders want AI that does a job: classify support tickets, extract fields from uploads, draft scope from a brief, not a science project billed hourly. Lab Twelve scopes AI features as bounded slices: prompt + UI + guardrails + eval hooks, priced from MVP Sprint or AI add-on ranges depending on depth. Models never invent your quote; the pricing engine maps complexity tiers to published numbers. As an AI-native engineer, Lab Twelve owns security, cost controls, and the pass before production.
One fixed price buys a whole product team in one: architect, designer, and AI-native engineer.
The data model, the integrations, and the build order are decided before a line of code, so the scope you see is the scope you get.
Interface, flow, and brand are designed for this build, not dropped onto a template. The finished app looks like it was meant to ship.
Senior execution at AI speed, in production with the source code in your hands. One builder with the range of a whole product team.
| Scoped AI workflow | Included |
|---|---|
| Structured outputs | Included |
| Production deploy | Included |
| Human review gate | Included |
| Handoff on model env vars | Included |
Scoped AI workflow: Not unbounded chat
Structured outputs: Validated against schema
Production deploy: Same as non-AI builds
Human review gate: Before merge to main
Handoff on model env vars: You hold API keys
| Training custom models | Not in base scope |
|---|---|
| Unmetered token spend | Not in base scope |
| R&D without definition of done | Not in base scope |
Training custom models: Inference + orchestration only
Unmetered token spend: Runtime is your AWS/Anthropic bill
R&D without definition of done: Manual review path instead
A representative ScopeSpec ticket. Yours is assembled live from the scope chat, priced by the engine, and locked before you pay.
AI job definition
Input, output schema, failure modes.
Workflow + UI
User path around the model call.
Guardrails + logging
Rate limits, PII rules, fallbacks.
Deploy + cost sheet
Runtime estimate for you to own.
Number of workflows, structured outputs, and integrations map to Business App, MVP Sprint, or AI add-on bands, all from offers.ts.
Provider is scoped per project, typically Anthropic for extraction and chat. You own runtime cost.
Yes for our pipeline. Your product AI is a scoped feature slice, not unlimited generation.
No agency to manage, no roster to assemble. The whole team, one number, source code in your hands.