Enterprise search, conversational AI, workflow agents, document intelligence, biometrics and visual search — delivered on our own IP, maintained by our own product team, so you are not paying to invent the same plumbing twice.
Token costs for leading models fell by more than 90% in a year. Capability is becoming a utility. What is not becoming a utility is the work of aiming it at one specific business process and keeping it correct there.
That is what this practice sells. Gartner expects more than half of enterprise generative AI models to be domain-specific by 2027, and at least 30% of generative AI projects to be abandoned after proof of concept. Both numbers point the same way: the winners are narrow, embedded in an existing workflow, and instrumented from day one.
TurfAI is the product our own Product team builds — the search, retrieval, agent orchestration, document extraction and biometric engines we would otherwise rebuild on every engagement. This practice delivers client projects on top of it, which is why our timelines look short and our second engagement with a client tends to be cheaper than the first.
Retrieval over your own corpus — policies, contracts, tickets, product data — with citations back to source, access control that respects the permissions the documents already carry, and answers that decline rather than invent. Deployed as search, as an assistant, or behind an existing channel.
Agents that execute rather than answer: reading a request, checking it against systems of record, taking the action, and escalating the cases they should not decide. Scoped to one process at a time, with the human handoff defined before the build starts.
Extracting the dark data out of unstructured documents — invoices, receipts, contracts, statements, forms — into something a system can act on. Confidence scoring per field, and a review queue for what falls below the threshold.
Video face detection and verification, signature matching, and voice authentication. Built for onboarding and step-up authentication flows where the false-accept and false-reject rates are contractual rather than academic.
Multi-format search across images, video, logos and brands — for retail catalogue matching, brand compliance monitoring and media asset retrieval.
Where a process is stable, high-volume and rule-bound, an agent is the wrong tool and RPA is the right one. UiPath-based automation, delivered at national scale during the COVID programmes and in production across government and enterprise back offices since.
These are industry and analyst figures, not our claims. We publish them because they are the basis on which we scope engagements.
Not “customer service” — the specific queue, the specific document type, the specific decision.
What it costs and how long it takes today, measured, so the improvement is arguable later.
What the system is allowed to decide, and what it must escalate.
Built with Quality Engineering, so the thing is testable before it is finished.
Drift, cost per outcome, model refresh and incident response, owned by Platform Delivery & Operations.
If a proposal cannot survive those five questions, we would rather not write it.
Most of them fail on the same handful of things. Tell us where yours is stuck and we will tell you which one it is.
