AI products, engineered to still work when the demo is over and real users arrive.

Yes. A significant share of our AI work is built for other agencies and consultancies under NDA. Three partnership models: white-label (your brand, our engineers, fully invisible), a dedicated AI pod working as your in-house capacity, and capacity overflow for sprint-by-sprint work. Code ownership transfers to your repositories, and we run inside your tooling as standard. AI products are exactly the kind of work clients are now asking agencies for and few agencies are staffed to deliver as real engineering.

Our entire team is based in Noida, India — 40 people in our iThum Tower B office, founded in 2015. We work with founders, product teams, and agencies across the UK, US, Ireland, Australia, the UAE, Germany, and the Netherlands. Working hours overlap with UK mornings, the full EU workday, and US afternoons. Every engagement has a named senior lead as a single point of contact, and for founders without an in-house team that lead is your direct line into the build.

Apply one test: imagine the AI removed entirely. If a working product still remains and solves a real problem, you are building an AI feature. If nothing usable is left, you are building an AI-first product. The answer matters because it changes the scope, the funding, the risk, and the moat — an AI feature isolates the AI risk, an AI-first product concentrates all of it into the core. We help you answer this honestly on the discovery call, because many teams call themselves AI-first when they have really built a strong product with an AI feature.

It depends on the scope — an AI product MVP is a very different engagement from a full AI-first product with billing, accounts, and a multi-feature surface. The cost drivers are the difficulty of the AI core, the breadth of the product around it, the accuracy and latency bar, and the eval coverage required. We scope every engagement against the specific brief, are competitive with established engineering rates internationally, and recommend starting with a costed MVP or product audit so you commit a small budget before a large one.

An AI product MVP is typically a focused 8 to 14 week engagement — a capability spike to retire the biggest risk, then one core workflow built end to end and eval-measured. A fuller AI-first product with accounts, billing, onboarding, and a multi-feature surface takes longer and is phased so the core ships and earns first. A product audit is 2 to 4 weeks. We work in two-week sprints with weekly demos and a visible quality number throughout the build.

Two things, not one. The demand risk — do people want this — which every MVP has always tested. And the capability risk — can the AI actually do the core task reliably on real, messy data — which is unique to AI products and is usually the bigger risk. We scope an AI MVP to answer both: a capability spike on real data measured against a golden set, plus one core workflow built end to end in front of real users. Success is two signals together, a measured quality number and genuine demand.

Because for an AI product, the eval set is the specification. A description like “it summarises well” is not buildable, since “well” has no definition a team can build to. A golden set of real tasks with known-good answers and a target score is buildable — it defines, concretely, what the product must do. So we write the eval set first: it is the spec, it becomes the regression net that catches quality drift in CI, and over time it becomes part of the moat. Building the feature first and the evals later means building without a specification.

Everything that is not the model call. The model is rented and available to every competitor, so it is never the moat. Defensibility comes from proprietary data the AI is grounded on, a genuinely hard workflow that took real engineering to make reliable, an eval suite and quality bar a rival would take years to match, distribution and brand, and the switching costs that build up once a customer’s work lives in your product. If you are building AI-first, the moat is the product strategy, and we treat it as a day-one design question, not a later problem.

Yes — and for most AI products that is the point. An AI product is still a product: it needs accounts, authentication, billing, onboarding, an interface, settings, and support tooling, and that unglamorous majority of the build decides whether the AI ever gets used well. We engineer the product around the AI to the same standard as the AI core, drawing on the ten-year engineering practice behind our Development archetype. A brilliant AI core inside a broken product is, to a user, a broken product.

Whichever the eval data favours for the product. We build with Claude, GPT-4o, Gemini, and open-source models such as Llama and Mistral, behind an abstraction that lets the product route between them and swap as the frontier moves. We are deliberately not locked to one provider, because models change every few months and an AI product should be able to take the gains without a rewrite. The model is a swappable component; the eval suite, the data, and the product around it are what we build to last.

Both are engineered, not left to chance. As load grows, cost per task becomes a unit-economics question, so we track it token by token, route simpler work to cheaper models where the eval data allows, cache aggressively, and review the margin. Quality is held by the eval suite running in CI on every change and by a weekly review that feeds production failures back into the golden set. We offer ongoing scaling and cost engagements precisely because an AI product that finds traction meets both pressures at once.

Yes. Adding a substantial new AI surface to an existing product is something we build to the same standard as a new AI product — eval-gated, observed, with the AI core proven before it ships. If the AI is one capability among many, our AI Integration page covers that work in depth. If the new capability is significant enough to reshape what the product is, it belongs here, on the AI product side. On the discovery call we will tell you honestly which it is and scope it accordingly.

AI Integration is adding AI features to an existing product — RAG, search, copilots. AI Workflow is automating a process with AI, including multi-step agents that take actions across systems. AI Product — this page — is building a product where AI is the core, from MVP to a scaling AI-first company. They share the same engineering foundation — retrieval, evals, observability — and many engagements touch more than one. Our AI Engineering hub page is the place that ties all three together.