Co-founder Mohammed Nasiruddin recognised at The Excellency Iconic Awards 2025 for a venture combining LLM-driven investor-startup matching with on-chain audit trails over committed capital.
London-based MatchPoint AI, an early-stage platform pairing AI-driven investor-to-startup matching with blockchain-anchored audit trails over committed capital, has been named Emerging AI Startup of the Year at The Excellency Iconic Awards 2025. The award, in the Data and AI category, was presented on 25 August at a panel-judged ceremony, with the certificate conferred on co-founder and product architect Mohammed Nasiruddin individually for his role in shipping the platform within five months of incorporation.
The recognition points to a growing institutional appetite for agentic AI platforms that bring algorithmic discipline to a stubbornly manual corner of capital markets. Lower mid-market deal discovery has long been flagged as the slowest, costliest stage of the venture and growth-equity cycle. Well-funded incumbents including Affinity, PitchBook and Crunchbase have built workflow tools around static datasets and CRM-style tagging. A smaller cohort of newer entrants, MatchPoint AI among them, is attempting something more ambitious: an end-to-end reasoning engine that explains its matches, paired with a ledger that records what each side actually committed.
What the platform does.
MatchPoint AI is a multi-tenant SaaS application built on a Python (FastAPI) and Node.js backend, with LangChain orchestration and a Llama 70B reasoning layer over a Pinecone vector store and a graph builder. The system ingests structured and unstructured deal data from founder forms, web scrapers, third-party APIs and document uploads through a Kafka inbound queue, normalises it into a deal graph, and produces ranked matches with explanatory rationales that can be defended in front of a deal committee. The blockchain layer underneath anchors investor commitments and follow-through milestones to a tamper-resistant audit trail, addressing a long-running complaint from limited partners and regulators about the opacity of fund flow reporting in the lower mid-market.
The company has disclosed an early set of operating figures: twenty-four startups onboarded, around thirty-five thousand investor profiles, a match-to-meeting conversion rate in the order of four percent, and approximately fourteen million United States dollars of tracked commitments. The numbers are modest in absolute terms, but they are unusual for a venture less than six months from incorporation. They are also the kind of figures the awards panel was looking at when it shortlisted the company.
Why the recognition has signal value.
The Excellency Iconic Awards is a panel-judged programme rather than a self-nominated showcase. The Data and AI category is evaluated on founder contribution to product distinctiveness and early commercial traction. The judges look for evidence of a working product, not a pitch. That is what places the recognition above the noise of the many crypto and AI awards that have proliferated in the past three years, where the threshold is often little more than a paid entry.
For Voice of Crypto’s readership, the more interesting angle is architectural. MatchPoint AI is one of the small group of platforms that has chosen to make blockchain a load-bearing component of an AI-native product rather than a marketing afterthought. The platform’s audit-trail layer uses immutability, distributed consensus, cryptographic integrity and smart-contract governed execution to record committed actions in a way that is verifiable post-hoc. That design choice is what regulators and limited partners have been pressing on since the FTX collapse: provenance, not promises.
About the founder.
Nasiruddin is a London-based product engineer with sixteen years of experience building multi-tenant SaaS and enterprise automation platforms across the United Kingdom, the Gulf and India. He currently serves on the platform engineering team at The Guardian’s Digital Publishing and Subscription Systems division, which supports infrastructure reaching more than one hundred million monthly readers, over one million paying subscribers, and around twenty-four million page views a day. He has three peer-reviewed papers in submission, covering blockchain-based crowdfunding, natural-language document classification and machine-learning-based anomaly detection. He is an active mentor through Topmate, where he sits in the top three percent of the platform’s mentors by mentee rating, and through Mentors in Tech and One Million Mentors.
Asked for comment, Nasiruddin underlined the team result and pushed back gently on the framing of the award as an individual milestone. “Awards are useful at the point at which they tell you the market has noticed,” he said. “The harder thing is to keep shipping value at the pace the panel said we have. The roadmap is the test, not the trophy.”
The broader market.
MatchPoint AI’s recognition arrives at a moment when AI-native fintech is one of the most active segments of the United Kingdom’s technology economy. The UK AI sector now includes more than five thousand eight hundred companies and generates around twenty-three point nine billion pounds in annual revenue, per the Department for Science, Innovation and Technology. The Government’s AI Opportunities Action Plan, written by Matt Clifford and published in January 2025, has been credited with catalysing approximately fourteen billion pounds in private-sector AI commitments and around thirteen thousand new AI roles in its first wave. UK fintech continues to rank second globally only to the United States for venture capital attracted, supported by an exceptionally mature regulatory framework that includes the Financial Conduct Authority’s Innovation Sandbox and the Cryptoasset Authorisation Regime.
Whether MatchPoint AI can translate the award into the next stage of scale will depend, as ever, on what the metrics look like twelve months from now. The market is not short of platforms with technical promise that never converted it. The platform’s blockchain audit-trail design is differentiated, the founder profile is unusual for an early-stage AI venture in that he has shipped multi-tenant SaaS at commercial scale before, and the first six months suggest disciplined execution. The next twelve months will tell us whether the panel’s bet was the right one.
