The gap between an impressive AI demo and a product customers pay for is where most startups stall — model costs balloon, the architecture cannot scale, and the founder is stuck coordinating a dev shop, a lawyer, and a recruiter. Naraway builds the product, wires the models, sets up the IP and entity, and hires the engineers as one team, so you cross that gap once.
Modern tools make a first AI demo trivial. The hard, value-creating work is turning it into something that scales, costs less than it earns, protects its IP, and can be maintained by a team you actually own.
An AI feature that delights in a demo can lose money at scale when every request calls an expensive model. Token economics have to be designed, not discovered on the bill.
A prototype wired together to look good rarely survives the first 100 paying customers. Rebuilding under load is the most expensive and stressful path to product-market fit.
AI features can be confidently wrong. Without evaluations, guardrails, and a human-in-the-loop where it matters, a hallucination becomes a customer incident.
Code written by contractors, prompts developed ad hoc, and no assignment paperwork create an IP mess that surfaces at the worst time — during diligence.
Hiring engineers who can ship reliable AI features, not just call an API, is hard and slow. The wrong early hires set patterns that are costly to undo.
A dev shop, a CA, and a recruiter who never speak leave the founder as the integration layer — the slowest, most fragile part of a fast-moving AI startup.
The team that architects your product also chooses and integrates the models, sets up the IP so you own everything, and briefs the engineers you hire. Each pillar links to the detail.
Prototype to production build — multi-tenancy, subscription billing, RBAC, and analytics — architected to scale rather than to demo.
SaaS build →Model selection and routing, prompt engineering, retrieval, evaluations, guardrails, and cost controls — so the AI is reliable and affordable at scale.
AI integration →Incorporation, DPIIT/Startup India recognition, and IP assignment so the code, prompts, and product are cleanly owned by your company.
Legal & IP →Backend, frontend, and AI-capable engineers sourced with background verification and briefed by the team that built your product — no cold handover.
Recruitment →Anyone can call a model. Making an AI product dependable, affordable, and safe to put in front of paying customers is the real work — and these are the decisions that make or break it.
A routing layer that sends each task to the right model — cheap for simple work, powerful where it counts — and lets you switch providers without a rewrite.
Prompt caching, retrieval instead of context stuffing, streaming, and per-customer metering, so unit economics stay positive as usage grows.
Automated evals to catch regressions, plus input/output guardrails, so a model update or a bad prompt does not quietly ship a worse product.
For high-stakes actions, a review step so the model proposes and a human confirms — reliability designed in, not hoped for.
Clear boundaries on what customer data reaches a model, retention rules, and provider terms reviewed — so your AI does not become a privacy liability.
Architecture, prompts, evals, and runbooks written down, so when your own team takes over, nothing critical lives only in a contractor's head.
A structured path that validates fast, then builds the foundation before piling on features.
Scope, model strategy, cost model, and architecture decided in one working session
A clickable prototype in 2-3 weeks to validate with users and investors before full build
Auth, data layer, billing, and model routing with evals built before feature sprints
Features in 2-week sprints with staging from week one, IP assigned as code is written
Production launch with monitoring and documentation as your team takes ownership
Send a short brief and we will come back with the model strategy, the architecture, and the first 90 days across product, IP, and hiring in one session.