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A short, paid discovery that ends with a ranked, costed backlog: what to build, what to buy, what to leave alone, and the reasoning behind each call.
A short paid discovery that ends with a ranked backlog: what to build, what to buy, what to leave alone, and the number attached to each.
Interviews across operations, finance, sales and support to find where time and money actually go, rather than where AI demos look impressive.
We check whether the data each idea needs exists, is accessible and is good enough, because that decides feasibility more often than the model does.
For each opportunity we compare off-the-shelf tools, configuration of what you already pay for and a custom build, with costs over three years.
Regulatory, data protection, reputational and operational risks are assessed per idea, including the rules in India, the Gulf and the US where relevant.
Ideas are scored on value, cost, feasibility and risk, then ordered so the first project is likely to succeed and fund the next one.
The top items become a phased plan with budgets, team needs, milestones and the measures that will show whether each phase worked.
The discovery is short and fixed in price on purpose, so the decision about what to build is made before anyone is committed to building it.
We speak with leaders and the people doing the work across functions, gathering pain points, volumes and the ideas already circulating internally.
How ai strategy & roadmap shows up across India, the Gulf and the US. Pick one to see what changes in the process.
Several departments propose AI pilots at once, each with its own vendor and no shared way to compare them.
Leadership works from one ranked backlog with costs and risks side by side, and funds the pilot most likely to pay back.
Code, documentation and the tests that prove it — yours outright — and the limits it runs inside, agreed before anything goes live.
Each stage ends in something you can hold — a document, a demo, a passing eval, a dashboard. Nothing carries over on trust alone.
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model
Two-week sprints to implement agents, integrate with your systems and run internal evals. You see working software early and often.
A working demo in your environment
We run your real use cases, measure accuracy, latency and cost, and pressure-test edge cases with your team before go-live.
Evaluation report with KPIs
We help you launch, monitor and continuously improve. You get playbooks, dashboards and regular reviews to scale safely.
Live dashboards and runbooks
Still deciding?
Thirty minutes with an engineer who builds ai strategy & roadmap. No deck, no discovery form.
Talk to an engineerDiscovery is paid and fixed-price, agreed before it starts, and usually runs two weeks. You get the long list, data readiness review, build or buy comparison, risk register and a ranked, costed roadmap. There is no hourly billing, and the documents and any prototype code are yours to keep and use.
Most engagements combine two or three of these. Discovery is where we tell you which.
Agents that take a task, use your tools and finish it.
ExploreAnswers from your own documents, with the source cited.
ExploreModels tuned and tested on your task, not a benchmark.
ExploreProcesses that run end to end, people only on exceptions.
ExploreGuardrails, tracing and access control around every model.
ExploreBring one process you think an agent could run.
We'll tell you straight whether it's worth building — and what it would cost if it is.