Discover & Scope
Align on problems, data reality, and success metrics. Opportunity brief, KPI model, phased roadmap, effort/cost ranges.
AI Strategy &
Use-Case Mapping
Identify the high-ROI use cases, size them honestly, and leave with a roadmap you can actually fund — not a slide deck of possibilities.
Strategy that survives the budget meeting
Most AI programmes fail before a line of code is written. They fail because the use case was chosen for how impressive it sounded rather than what it was worth, because nobody checked whether the data existed, or because success was never defined tightly enough to prove. Our AI Strategy & Use-Case Mapping engagement is designed to kill those failures early and cheaply. We start with your operators and your stakeholders, not your technology — what work is slow, what decisions are guessed, where the money leaks. Then we map each candidate against three axes: business value, data readiness and engineering feasibility.
What comes out is a ranked, costed roadmap: which use case to build first and why, what it will realistically take, what it should return, and which ideas to drop before they eat a quarter. You get a value model with real KPIs, a data-readiness assessment that tells you where the gaps are, and phased effort and cost ranges you can take to a board. It is not a plan — it is a decision, backed by evidence.
What We Do?
We run structured discovery with the people who do the work and the people who fund it, then translate what we hear into a portfolio of AI opportunities scored on value, feasibility and data reality. We audit what data you actually hold — not what the schema says you hold — and flag the gaps that would sink a build.
We define the success metrics up front, so that six months later there is no argument about whether it worked. And we sequence the roadmap so the first win lands fast enough to buy political capital for the second.
Key Deliverables
A costed, sequenced AI roadmap grounded in your data reality and your commercial goals — plus the honest answer on any use case where AI is the wrong tool.
- A ranked portfolio of AI use cases scored on value, feasibility and data readiness
- A KPI model that defines success before the build starts
- A data-readiness assessment with the gaps named and sized
- Phased effort and cost ranges you can put in front of a board
What's included ?
- + Stakeholder discovery workshops
- +Opportunity brief and value model
- + Data readiness and gap assessment
- + Phased roadmap with effort and cost ranges
Process
From Idea
to Production
to Production
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.