Real Estate
Ship AI features your users actually rely on, not another demo that stalls before launch.
The demo dazzled, then it met production.
You have seen this movie. A model looks brilliant on a clean sample, the room nods, and then it hits real data, real edge cases, and a latency budget, and the magic drains out of it. The feature slips off the roadmap, and the story becomes "AI did not work for us."
Nine times out of ten the model was never the problem. The problem was that the AI got treated as a science project instead of a product, with none of the engineering that makes software survive contact with users. There was no way to measure whether an answer was good, no plan for the day the model was wrong, and no clean path from the notebook to the running system. That gap is where most applied AI dies, and closing it is the whole job.
A production-grade AI feature, owned end to end by the team that also builds the software around it. In practice that means four things you can count on.
We engineer the retrieval, guardrails, and fallbacks that turn a promising model into something you can put in front of a paying customer.
An evaluation harness with real test sets and scoring, so "good enough to ship" is a number you can see, not a gut feel.
Model routing, caching, and token budgets, so the unit economics still make sense at ten times the traffic.
Built into your real systems and interface by one senior team, so users can see what the AI did and correct it, instead of distrusting a black box.
Applied AI earns its place when the intelligence is the point of the feature, not a garnish. A few of the situations we build for:
The idea only works if the model works, so the feature has to prove itself on real data rather than a scripted demo.
You want search that understands intent, a drafting assistant, or a recommendation that users trust, added to what you already run without a risky rebuild.
Something already convinces people in a demo, and now it needs the evaluation, guardrails, and monitoring to hold up in front of real users every day.
Hours a week go into reading, summarizing, classifying, or drafting, and an AI feature could take the repetitive part off your team while a person keeps the judgment calls.
Before a line of code, we set the accuracy bar the feature has to clear. An AI feature without a defined bar is unshippable, because you can never prove it is ready.
We ground the model in your data, wire it into the workflow, and score it against real inputs as we go, so quality is measured, not hoped for.
With guardrails on the output, a graceful fallback for the bad days, and monitoring that catches drift after launch.
With the eval harness and docs your team needs to keep it honest as the world it sees changes.
Most AI vendors hand you a model and leave the hard part, getting it to production, to you. We do the opposite.
The same senior engineers build your product and the AI inside it, so there is no vendor handoff where a feature dies and no interface that cannot surface what the model is doing.
Retrieval and good prompting solve most problems faster and cheaper than training a model, so we reach for them first.
It is the only thing that tells a working feature from a convincing one, so we treat it as the product, not an afterthought.
If a simple rules engine does the job, we say so, because talking you into a model you do not need is the fastest way to lose your trust.
From MVP to a multi-million-dollar acquisition.
Read Success Story →
Raw data turned into decisions, with no engineering bottleneck.
Read Success Story →Tell us the AI feature that keeps slipping off the roadmap. We will scope it, set the accuracy bar it has to clear, and build it to clear that bar in production.