AI MVP Development That Survives Real Usage

It means building everything around the model that a real product needs: retrieval (RAG) so answers are grounded in your data, usage metering and cost guardrails so it stays affordable, a UX that earns trust with citations and controls, evals so you can prove quality, and a data moat that makes it defensible. The model is a component. The product is the system around it, and that is what we build and what investors actually fund.

Cost is a design decision we make on day one, not a surprise on the invoice. We build usage metering per user and org, response caching so repeated work is not paid for twice, model routing that sends easy requests to cheap models and only hard ones to expensive models, and rate limits and budgets that cap exposure. The goal is simple: your inference cost should scale slower than your revenue.

No. We build model-agnostic by design. Whether you use OpenAI, Anthropic, or open models, we architect so you can route between them and swap providers without a rewrite. That protects you from a single vendor’s price change, outage, or policy shift, and lets us put each request on the model with the best cost-to-quality fit.

Three ways. First, retrieval: we ground answers in your data with RAG and surface citations, so users can verify. Second, guardrails: we validate inputs and outputs and filter where it matters. Third, evals: we build a test harness that measures quality so regressions are caught before users see them. And we design the UX to be honest about uncertainty rather than pretending the model is always right.

Not the model, since your competitors can call the same API. Defensibility comes from your data moat (proprietary data that compounds as people use the product), workflow and integration lock-in (you become part of how they work), evals and tuning that make your output measurably better, and the prompts and fine-tunes you own. We build these in deliberately, because "what stops someone copying this" is the first question a serious investor asks.

We fine-tune and prompt-engineer where it genuinely improves the product, and we build the data pipeline to support it. We build on proven foundation models rather than training one from scratch, which keeps your cost and timeline in MVP territory. We will tell you honestly when fine-tuning helps and when good retrieval and prompting get you there faster and cheaper.

A tightly scoped AI MVP, one core flow with login, retrieval, guardrails, and a trust-first UX, ships in 14 days. One AI workflow with roles and payments or an admin panel is AI SaaS Lite, in 30 days. Two AI workflows are the full AI SaaS Build, a custom quote from $16,500, and heavier agentic workflows or several integrations get a fixed quote too. We lock scope during discovery so the date is a commitment, and you see working code in weekly demos throughout.

All of it: 100% of the source code, the prompts, the eval harness, infrastructure configuration, and documentation, handed over at launch. There is no vendor lock-in and no licensing fee. Owning your prompts, evals, and the data moat is exactly what makes the product defensible and yours to scale without depending on us.

Book a 30-minute discovery call. We discuss your use case, your data, the cost and trust surface, timeline, and budget. You leave with a scope document, a cost and defensibility plan, a scoped quote, and a build timeline, before any commitment. If it is a good fit we begin discovery; if AI is not actually the right tool for your problem, we will tell you that too.