Engineering the next breakout startup

Most minimum viable products go from kickoff to live in six to twelve weeks depending on scope, and a deliberately narrow first build can be faster. We scope around the single thing that proves your model rather than the full product you eventually want, because the purpose of an MVP is to answer a commercial question quickly and cheaply. The most common reason a first build overruns is not engineering difficulty but scope that expands to include features nobody has yet validated anyone wants, which is exactly what the MVP was meant to test.

Yes. We work with bootstrapped founders, pre-seed teams and funded scale-ups, and we scope to your stage and runway rather than applying one engagement model to everyone. For pre-funding teams the priority is usually reaching something demonstrable with the least capital consumed, which sometimes means recommending a smaller build than you asked for. We also build architecture that survives later scrutiny, because a codebase assembled purely for speed becomes a problem during technical due diligence, and that is an expensive moment to discover it.

Yes. We integrate language model copilots, retrieval augmented search, automation and machine learning features into existing codebases on current model stacks including Claude. Working inside an existing product is usually more constrained than starting fresh: the model needs access to your data in a form it can use, which often means the retrieval and data preparation work is larger than the feature itself. We would rather be straightforward about that split at the estimate stage than present the model integration as the whole job and discover the data work afterwards.

Both. Conversion optimisation, SEO, performance and analytics are part of how we build rather than services added later, so the product is instrumented to acquire, activate and retain users from the start. In practice that means event tracking is designed alongside the features rather than retrofitted when someone asks why activation is flat. A product that cannot tell you where new users stop is very hard to improve, because every proposed change becomes a matter of opinion rather than something you can settle with data from your own users.

You do, completely, with no vendor lock-in. You receive clean, documented, well structured code and full ownership of everything we build, held in your own repositories from the first commit rather than transferred at the end. For a startup this is not a formality: your codebase is an asset investors will examine, and any ambiguity about who owns it is a genuine obstacle in a funding round or an acquisition. There is no proprietary framework you can only maintain through us and no arrangement where you keep paying to use what you commissioned.

By working backwards from the riskiest assumption in the business rather than forwards from a feature list. The first release should test whether people will actually do the thing your model depends on, so anything that does not contribute to that test is a candidate for cutting, including features that feel obviously necessary. We are explicit about which shortcuts are deliberate and which are debt, so the transition from MVP to a durable product is a planned piece of work rather than a gradual realisation that it cannot take more customers.

It depends on scope and stage. We size the build to your goals and runway and send a fixed proposal, usually within 24 hours of a discovery call, so you can plan with confidence rather than against a moving estimate. For early stage teams we would rather propose a smaller first phase that preserves your capital and produces something testable than a comprehensive build that consumes the runway you need to act on what you learn. Knowing what to leave out is generally the more valuable part of the conversation.

Yes, and the difference is mostly in the unglamorous parts. A demonstration works on curated inputs with someone watching; a production feature has to handle bad input, unavailable model providers, latency, cost per request and outputs that are confidently wrong. We build with evaluation against real examples, guardrails on what the model is permitted to assert, graceful degradation when the provider fails, and monitoring of both cost and quality. We also ground answers in your own data rather than allowing the model to generate plausible statements about things you do not actually offer.

Yes. You can engage a dedicated senior team that plugs into your roadmap immediately, giving you delivery velocity without the months of hiring lag, working inside your tools, rituals and code review process rather than running a separate project alongside. This suits teams with a clear roadmap who need capacity, or who need a specialism they intend to bring in house later. Where that is the intention we plan the handover deliberately rather than leaving you dependent on us, and we will say when a smaller engagement would serve you better.

That is a deliberate goal rather than a happy accident. We write documented, well architected code with automated testing and continuous integration in place, so the codebase is an asset in diligence rather than a liability that reduces your valuation or delays a round. Technical diligence typically probes security practice, dependency health, test coverage on critical paths, and whether the architecture can absorb growth. The findings that damage a round are rarely exotic: they are usually absent tests, secrets committed to the repository and undocumented systems only one person understands.

Yes. We architect for growth and stay involved to optimise, re-scale and extend as usage and your team increase, so traction becomes a planning exercise rather than a technical crisis. Scaling work usually means adding real observability, fixing queries that were fine at a thousand rows, moving slow work to background processing, and paying down the shortcuts that were reasonable at MVP stage. Doing this before you need it wastes capital, and doing it too late costs you customers during the exact period when attention is hardest to earn back.

We expect it, and the engagement is structured so a change of direction is not financially punishing. Working in short sprints with regular demos means you can redirect at sprint boundaries rather than discovering a mismatch at the end of a long build. Because you own the repository throughout, nothing is held hostage to the engagement continuing. We would rather you pivot early on evidence than continue building something the market has already told you it does not want, and we will say so plainly if we think the data points that way.