GenAI Research Assistant for a Virtual Software Company
AI PRODUCT DEVELOPMENT
AI Engineering Without Pausing Your Roadmap
An AI engineering team that works in your codebase, on your stack, alongside the engineers you already have. We build the retrieval, evaluation, tenant isolation and cost control underneath AI features, so your product team keeps shipping everything else.
Built once and inherited by every feature after it. Running in your cloud, your customer's tenant, or their own hardware, with your choice of model.
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AI Product Development
We Build the AI Features Inside Your Product
Three ways to start: a single engineer, a scoped build, or a short review.
WHERE YOU ARE NOW
Three ways an AI roadmap stalls. In each one, the shortage is engineering capacity rather than AI expertise.
Planned, not started
The roadmap has AI on it for this year. Nobody has started, because the engineers who would build it are committed to everything else, and the data layer underneath would need work first.
Announced, not shipped
The release note went out. The feature is still behind a waitlist or an internal demo. The blocker is rarely the model, it is that nothing exists to feed it reliably at production quality.
Shipped, not scaling
It works for the customers whose data you prepared by hand. Widening it to the rest of the base turns out to be a different job, and it belongs to data engineering rather than to the team that built the demo.
WHAT MAKES THIS DIFFERENT
Building AI Into a Product Is Not the Same as Adopting AI Internally
It fails in public
An internal AI project that underperforms is a quiet retrospective. A feature in your product fails in front of your customers, under contract, in a renewal conversation. Accuracy targets, fallback behavior and failure modes have to be designed in, not discovered in support tickets.
Unit economics decide whether it survives
Inference cost per tenant lands on the gross margin your business is valued on. Token spend, caching, model routing and where a smaller model is sufficient are architecture decisions with a line-item consequence. We treat cost per tenant as a design constraint, not an invoice you discover later.
It has to work on every tenant's data
Enterprise AI is built against one company's data with one set of conventions. Yours has to hold across every customer you have, including the ones with fifteen years of inconsistent records. Multi-tenant isolation, per-tenant evaluation and graceful degradation are the difference between a demo and a feature.
It has to run where your customers require
One enterprise buyer will insist it runs inside their own cloud account. Another will not allow their data to leave their network at all. A third has already signed with a different model provider than the one you built on. Hard-wire one provider and one deployment shape, and each of those becomes a lost deal, a forked codebase, or a rebuild.
WHERE THE TIME GOES
BUILD THE FOUNDATIONS JUST ONCE
Most of an AI feature is not the model. It is retrieval and chunking strategy, an evaluation harness that catches regression before a customer does, prompt and model versioning, per-tenant isolation, fallback behavior for when confidence drops, cost metering and an audit trail. All of it gets built the first time, whether or not anyone planned it, and the second feature needs the same list.
We build that once, as a shared layer your team owns.
SYSTEMS WE BUILT FOR SOFTWARE COMPANIES
HOW WE WORK: THREE WAYS TO START
- Embedded engineers
AI and data engineers who join your team, your standups and your repository, contributing inside the first few weeks. Our day overlaps the US East Coast morning, so standups, reviews and pairing happen live rather than through overnight handoffs. Against a US hiring cycle that runs for months and may not close at all.
- Scoped build
A defined feature or pipeline, delivered end to end against an agreed specification, then handed over. For teams who know exactly what they need and have no capacity to build it.
- Architecture review
A short engagement that ends in a written assessment of what is blocking your AI feature and what it would take to unblock it. Where most engagements start.
THEY TRUSTED US
Talk to our engineers
Whether the AI feature is still on the roadmap, already announced, or live and struggling to scale, the first conversation is about what you are building and what sits underneath it. No commitment
