<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/F426511D-C08B-42EB-BFDF-5C8D42E1F731" ns1:id="F426511D-C08B-42EB-BFDF-5C8D42E1F731"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/2BFB5E62-6784-416D-81C5-97CC0D9B9DFC" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A52DF872-A4AE-4C98-95AA-0A585DF95588" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A52DF872-A4AE-4C98-95AA-0A585DF95588" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2023-07-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/9EA0179D-914A-4FAC-AB96-8A9B66900BFB" ns1:rel="FUND" ns1:start="2023-04-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10064969</ns2:identifier></ns2:identifiers><ns2:title>Thema</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Recent advancements in vector embeddings and large language models (LLM) have opened-up huge new opportunities to power next-generation market discovery and search. Thema are developing game-changing trustworthy AI to unearth deep-insights from online text sources, enabling SME private market investors to significantly improve market discovery/company evaluation, ROI and productivity.

Current market solutions have not evolved in the last decade. Market-mapping currently relies on expensive/unscalable/static consulting services (c&amp;pound;500k for one-off 6-week project), is inherently human biased/constrained, and leads to missed opportunities ?as themes evolve rapidly. Existing software/tooling (analyst-driven company intelligence tools) is built on legacy search technology which reinforces bias/lacks contextual understanding, thus stifles discovery. Public search engines (Google/Bing) serve only general information discovery, whilst the nearest state of the art have poor results and rely heavily on augmenting their tools with biased consulting services.

Thema have built a highly innovative MVP that offers novel market discovery engine, proving the core technological assumptions leveraging pre-trained open-sourced large language model and tested the MVP with top PE/VC investors, validating the need/demand for the innovation. In order to develop a fully transparent and trustworthy scalable solution, Thema need to increase the breath of data and advance the technology to (1) Add additional company description sources to ingest a variety of lenses that describe what a company does (2) train and evaluate a proprietary custom embedding model instead of using black-box pre-trained models to improve transparency of results to end-users (3) create and evaluate active learning pipeline to optimise unsupervised learning results and capture subjectivity of individual customers views on markets and competition.

Success with the project will give us first-to-market advantage with a groundbreaking AI market intelligence tool. PE/VC/Hedge Funds will adopt the innovation due to its unique value-proposition: enables investors to invest earlier (increased returns); find better quality companies (higher exit rate/returns on exit); increased productivity: 35% in time and deliver 25% increase in productivity: (Cipher 2020, study on competitor intelligence tooling); deliver ROI of 1.5x better exits based on 4x discovery and 2x evaluation process improvement.

Innovation is the leading driver of economic-growth (_OECD-2017_). By transforming the market and deal discovery we will enable private market investors to discover a broader pool of entrepreneurs/companies, directly impacting overall economic-growth at every industry-scale.</ns2:abstractText></ns2:project>