Most of the real value in AI today is unglamorous — reading documents, drafting the first version, answering the same question for the hundredth time. We build those features into products that already exist and already have users, then measure whether anyone actually saved time.
We are a product studio first, and nearly all of our AI work sits inside something we already build and maintain — a Laravel API, a Next.js dashboard, a React Native app. That matters. The value is not in the model; it is in the data, the permissions and the workflow already sitting in your database, and in putting the output on a screen someone opens every morning anyway.
So we start narrow. One workflow, your real records, and a small set of test cases that says plainly whether the output is good enough. If a database query, a rules engine or a better-designed form beats a model, we will tell you — it is cheaper to run, easier to explain to your auditors, and it does not quietly change behaviour when a provider ships a new version.
Summaries, drafts, classification, tagging and natural-language search added to the screens your users already know. We put them behind feature flags, so a feature can be switched off for one customer or everyone without waiting on a release.
Support, onboarding and internal help assistants grounded in your own content rather than the open internet, with a clear escalation path to a human and a full transcript you can audit afterwards.
Ingestion, chunking, embeddings and retrieval across your PDFs, course notes, policies and contracts, so staff can ask a question in plain English and get an answer with a link to the exact source page.
Invoices, bills of lading, purchase orders and scanned forms turned into structured records with a review step before anything posts. This is where our freight work benefits most — far fewer keystrokes per shipment file.
Queued jobs, scheduled tasks, routing rules, approvals and integrations that move work between systems without anyone re-typing it. Often the biggest single win on a project, and often no model is needed at all.
A test set built from your real cases, versioned prompts, rate limits, spend caps, fallbacks for when a provider is slow, and logging of every call. You can see what it costs and whether quality moved.
We sit with the people doing the work and map the workflow as it really runs, not as the SOP describes it. You get a written scope naming the one or two steps worth automating, and a rough monthly running cost before anyone writes code.
A short spike against your real records, with a set of cases you would call right or wrong. It answers one question — is this good enough to show a user? — and gives you a genuine stop point before the build budget is committed.
Our engineers build the feature into the existing codebase with the same review and release process as everything else, and both our QA engineers test it. Permissions, edge cases, retries and a sensible fallback when the model is unavailable all get handled here.
Usage and cost dashboards, logs you can inspect, and prompts and configuration kept in your repository so your own team can change them. Then a walkthrough with whoever will own it, and written notes on what to watch.
Describe the task and roughly how often it happens. You'll get a straight answer on whether a model genuinely helps, what it would cost to run each month, and which parts a plain rule would handle for nothing.