Why we built Appnalysis, and the problem we set out to solve. Mobile application development has never been more accessible, but making the right decisions has never been harder. Even experienced teams struggled to confidently answer a simple question: what does "good" actually look like?
Appnalysis originated from real-world client work at Coderus, a UK software and mobile app development agency with more than 20 years of hands-on delivery for start-ups, SMEs and blue-chip enterprises across iOS, Android, automotive, IoT and embedded. Designing, building and shipping real apps for real clients taught us what "good" looks like, what breaks under load, what app store reviewers flag, and what users actually do once an app is installed. To support delivery, teams carried out manual analysis of competitor apps, a process that was time-consuming, difficult to scale, and repeated across projects.
Over time, a pattern became clear. The problem was not a lack of data, it was a lack of structure. Data existed across the ecosystem but was fragmented, inconsistent, and difficult to translate into decisions, while AI, no-code tools, and global distribution kept increasing both speed and complexity.
2018, first manual research for client projects. 2019, market validation through WWDC research and client surveys. 2020, alpha platform released and the Innovate UK KTP for ML and LLM research programme starts. 2021 to 2022, continued validation. 2023, Appnalysis Ltd formed and beta platform released. 2024, growth, user adoption, and industry recognition. 2025, platform maturity across iOS and Android app intelligence. 2026, Appnalysis goes Agentic, with mobile app intelligence delivered as an AI agent ready to plug into the AI assistants teams already use.
Internal tools developed to standardise analysis, structure data, and generate repeatable insights for client delivery evolved into a standalone platform.
Years of shipping apps across iOS, Android, automotive, IoT and embedded shaped how we read app evidence today. When we look at permissions, SDKs, store metadata, review patterns, or release cadence, we know what each signal implies for a roadmap, a pitch, an architecture choice, a copycat response, or a compliance review, because we have made those calls ourselves on live client work. We don't just see the signal; we know what it costs to build, what it costs to ignore, and what it means for your next decision. That accumulated judgement is encoded into Appnalysis, which is why an AI assistant calling Appnalysis via MCP gets back a reasoned answer rather than a data dump.
Three principles guide how we research, reason and communicate.
Be curious. We look beyond the obvious answer. We research what peers, competitors, platforms and markets are doing, question assumptions, and explore what else might matter. Sometimes the most useful insight comes from something nobody thought to ask.
Find the evidence. Good decisions need more than opinions. We look for the data, signals and sources behind an answer, distinguishing what we know from what we infer. Evidence should be understandable, traceable and useful.
Do the right thing. We represent what the evidence tells us, not what we hoped it would say. We recognise that the decisions our intelligence supports can affect organisations, their customers and ultimately their users. When something is uncertain, we say so. When the evidence isn't sufficient, we don't manufacture an answer.
Curiosity makes us look. Evidence helps us understand. Integrity guides what we do with it.
As software becomes easier to build, decision quality becomes the differentiator. Teams no longer need more dashboards, they need clarity, context, and confidence. Today, Appnalysis is delivered as an AI agent, so agencies, product teams, enterprises, and ecosystem stakeholders can ask questions in plain language and get citation-backed answers about how apps are actually built, and how they should respond. Every answer comes with the evidence behind it, ready to plug into the AI assistants your team already uses.
We've built Appnalysis through real-world delivery, sustained investment, and external validation. The platform has evolved over several years, supported by hands-on client work and continuous iteration, and we've largely bootstrapped it through delivery work. It is further supported by Innovate UK programmes and KTP involvement, the Innovate UK Intensive Investment Programme, the Innovate Product Fund, Anglia Capital Halo, the Breakthrough Founders programme, Essex Angels, and participation in Slush and Mobile World Congress.