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AI-powered web and mobile applications, intelligent features inside existing systems, and custom AI solutions scoped against a decision your business actually makes.
An AI feature that lives in its own tab is a demo. The ones that get used are the ones that appear at the moment a person was already going to make the decision — in the order screen, in the approval queue, on the ticket, next to the field they were about to type into.
That is an integration problem more than a modelling one, which suits us: the same team builds the portals, ERPs and POS systems the feature has to live inside. We are as likely to spend a sprint on the permissions and the audit trail around a suggestion as on the suggestion itself.
The suggestion appears in the screen where the decision is already made — not in a separate AI tab nobody opens.
The model sits behind our own interface, so changing provider is a configuration change rather than a rewrite.
Timeouts, rate limits, refusals and low confidence all have defined behaviour before launch.
Per-tenant and per-feature spend caps, with caching, so an unusual week cannot produce an unusual invoice.
Every AI-assisted decision records its input, its output and whether a person overrode it.
Each AI feature is behind an admin toggle, so your team can disable it the moment it misbehaves.
React and React Native on the front, Django and Django REST Framework behind it, PostgreSQL underneath, and whichever model provider the use case and your data-residency rules allow. Self-hosted open-weight models where the data cannot leave your infrastructure.
The sequence is deliberately unglamorous. Most of the risk in an AI build is in the first and last steps, and most of the cost overruns come from skipping them.
We do not quote accuracy figures before seeing your data, because a number produced that way is invented. We do not promise a model will not be wrong; we design the screen around the assumption that it sometimes will be. And we cost the ongoing inference spend into the proposal rather than letting it surface as a surprise in month two.
A business that already runs software, whether a portal, an ERP, a POS or a product of its own, including one another agency built, and has a specific decision inside it that a model could help with: triage, classification, drafting, search or duplicate detection.
If there is no decision to name yet, an AI consulting assessment is the better first step, and it costs less than discovering the gap halfway through a build.
Straight answers, including the ones that rule us out.
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LLM integrations, retrieval-grounded assistants, internal copilots, customer chatbots and document intelligence — built so every answer can be traced back to the source it came from.
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Read moreAn assessment of where AI would actually pay in your operation, what it would cost to run, what your data can and cannot support, and a build order you can fund one step at a time.
Read moreDescribe your operation and we will come back with a written scope, a fixed price and a delivery date.