Production-grade AI Agents × Funded-stage MVPs

AI integration for funded-stage MVPs

Ship the AI feature your roadmap promised the investors, with the evals and guardrails sized to your stage, not a Series C research budget.

Book a 20-min Architecture Review

Domain context

Most funded MVPs ship an AI feature in v1 because they had to put it in the deck. The feature then becomes a quiet liability: it hallucinates in front of a key customer, the costs creep, nobody knows what to do when a model swap breaks something. We help teams ship the AI feature properly the first time. A small eval suite that catches the worst regressions before they hit production. A guardrail layer that keeps the model from saying anything the legal team will read in a screenshot on Twitter. A cost ceiling that won't surprise the CFO when usage spikes after a press hit. It's the engineering side of AI features, sized to a funded team with one engineer who's going to maintain this and a roadmap that won't slow down for a research project.

Why this combination

Specialist AI labs will quote you a six-month engagement with a model-fine-tuning phase and a research-lead title in the staffing plan. Generalist agencies will ship a feature that demos great and falls over the first time a real customer types something unexpected. We sit in the middle. We treat AI as production engineering with a few extra failure modes (evals, drift, cost, refusal), not as a research project. The output is a feature your team owns and can extend without rehiring the agency every time the model layer shifts.

“We added a RAG-backed support agent to a Series A SaaS product in five weeks, with a 312-case eval suite that has flagged seven regressions before they reached customers.”
B2B SaaS at Series A, in-house engineering team of three. Wanted a support copilot that could answer questions about the product without making things up. Real customer data, real escalation paths to humans when confidence was low. · Time from kickoff to production: 5 weeks

Frequently asked

We don't know what AI feature to build yet. Can Hotreloads help us scope it?
Yes. The first week is usually scope. We listen to the use cases, talk to a couple of your customers, and pick the AI feature that's both genuinely useful and small enough to ship in this engagement. If the right answer is 'don't ship AI yet,' we'll tell you.
What about model selection? OpenAI, Anthropic, open-weights?
We pick based on the feature, the cost ceiling, and where your data can legally live. For most funded-MVP features at this stage, a hosted commercial model is the right answer. We architect around the model interface so swapping is a config change rather than a rebuild.
How do we keep AI costs from blowing up?
Token-cost dashboarding from day one, request caps per customer, and a routing layer that uses cheap models for cheap requests. We'll show you the unit economics before the feature ships and instrument it so you'll see the cost trend long before it becomes a problem.
What if our AI feature breaks after a model update?
That's what the eval suite is for. Every model swap runs against the eval suite first; failures block the deploy. We build the suite during the engagement and leave you with the harness so your team can extend it as new failure modes show up.
Will customers know they're talking to AI?
Yes. We design the UI so the AI layer is clearly disclosed, the limits are visible, and there's a path to a human when the confidence is low. The teams who try to hide it usually end up with a customer-support problem six months later.