Research, screen, and diligence companies with structured data and rigorous analysis. Not opinion. Not guesswork. Evidence at depth.
From defining your thesis to post-acquisition onboarding. Each app is purpose-built, data-connected, and designed to surface evidence — not generate opinion.
Digital twin #0: the card of a ukacq app itself, one per app at app.ukacq.com/[app_id]. Served by the same process as apps.ukacq.com (:8003) — the Host header decides whether a path means the twin or the console.
Digital twin for any UK company. Financials, people, ownership, filings — all from primary sources.
Draft persona-aware outreach emails to target company stakeholders
Filter the 3.2M-company ukacq universe on any measured attribute.
One engine, four verbs over any object (talent, company, game): build predicates from census facets, compose them into scores, weight scores into ranks, run a rank or filter as a screen with export.
Eight-section live instrument for vpsm: box statistics, functionality status, liveliness, usefulness, UI status, risks, opportunities and completeness.
Orders a scope of companies by a scoring profile the user states in full — attribute, direction, normalisation, missing-value policy and weight — and records score, coverage and a per-attribute basis for every company.
The app directory. Every tool in the acquisition lifecycle, at its own address.
The verb-first view surface: view.ukacq.com/{ident} dispatches any identifier (talent slug or ind_ hex, CH number, game code, archetype) to the right card. Talent cards render here; other kinds redirect to their canonical card host.
The digital twin of an acquirer archetype: a named pattern of how a class of acquirer buys, and which companies fit it.
Define an acquisition thesis — sectors, geographies, deal size, criteria, exclusions.
Full analysis of public and private data. Evidencing, not opining. Depth commensurate with data depth.
The document twin. Every file dropped into the game-definition flow, with its identity, digest, extraction state, the game it fed and the pipeline step that consumed it.
The digital twin of an acquisition game: its thesis, the universe it enumerates, the companies it has evaluated, its criterion-by-criterion assessment, its research runs and its decision pack — each fact with its source.
The digital twin of an investment memo: its identity, the inputs it was built from, its thesis, deal terms, talent, evidence, gaps and sign-off — every line carrying the source it came from.
Run the ukacq engine and a single-pass one-shot on the same game, then have a judge compare them.
Run many games through engine and one-shot tracks in parallel and compare across versions.
Deep research on companies within a defined game. Public filings, ownership, financials, people.
Filter, rank, and shortlist companies within a game. Multiple ranking options.
The digital twin of a serial acquirer: one card per acquirer at acquirer.ukacq.com/[acquirer_id or name], served by the archetype-card process (twins #5 and #6, one service, two hosts).
Companies House filings, PSC ownership graphs, employee data, financial metrics — structured, scored, and cross-referenced. Not scraped summaries. Primary sources. Each figure below is measured, with how and when beneath it.
Every claim is traceable to a source. Every score is decomposable. Every recommendation shows its working.
Structured data processing, deterministic scoring, and rule-based filtering happen in code — not in an LLM prompt. AI enters only where judgment is genuinely needed.
Companies House bulk data, PSC registers, published accounts. Not aggregator summaries. Not web scrapes. The filings themselves.
A diligence report is as deep as the data supplied. Shallow data gets honest uncertainty, not confident hallucination.
Every assertion links to its evidence. Every score decomposes into its components. No black boxes.