Metric Extraction
The numbers that matter, pulled from every document, no manual keying.
Antonine
An agent orchestration layer that mirrors your existing workflow, taking a deal from inbox to IC-ready,
with a memory that gets sharper with every one.
Standalone AI tools generate plausible answers, not auditable ones. A diligence read on a company can't be "probably right." Neither can an IC memo or a deal recommendation.
Antonine keeps the two separate: the intelligence proposes, the platform executes. Every output traces back to its source, nothing leaks between funds, and nothing reaches your team that you can't defend.
Defense-grade infrastructure, applied to investment management. Agentic intelligence on top, your judgment in the loop.
From inbox to The Vault.
CIMs, teasers, pitch decks, rent rolls, operating statements. Uploaded one at a time, in bulk, or routed automatically from a deals@yourfirm inbox. Every page is analyzed if needed, every chart parsed, every embedded exhibit extracted. The deal is now an object in your firm's ontology, with every source document attached.
The numbers that matter are pulled from every document, and the deal is weighed against your firm's mandate, sector thesis, and the deals you've passed on before. It lands in your dashboard already ranked for fit, with the reasoning attached, so your team spends its time only on what belongs there.
Agents write every figure directly into your firm's own model templates, building the LBO inside the models you already use, and generate the IC memo in your exact format. They surface comparable deals from your history and from market data, benchmark the target against industry medians, and produce meeting prep briefs with talking points and risks. Your team reviews and refines instead of starting from scratch.
Every figure linked back to source. Every claim cited. Your firm's standard memo format, populated from real deal data. The analyst's job becomes review and judgment, not drafting from scratch. What used to take eight hours takes fifteen minutes.
Every deal becomes a typed object in your firm's private knowledge graph — linked to the memos, comps, and decisions around it, and cross-referenced against every deal that came before. The next deal arrives already connected to your history, so the Assistant answers from what the firm already knows, with citations routed back to the source. Each deal makes the graph denser, and a denser graph makes every future deal sharper.
Every deal you process trains the platform on your firm. Each screening decision sharpens the next screen, each memo refines the template, each query teaches the Assistant your vocabulary. And because every task routes to the most effective model per token, the more you run, the more value you pull from every dollar of AI spend. Six months in, the platform looks nothing like the one we shipped on day one. It looks like yours.
Bring your existing files and workflows. No migration required.
Upload the teaser, the CIM, the model, and the work that used to take a week is ready in minutes.
The numbers that matter, pulled from every document, no manual keying.
Scored against your thesis, so your team only spends time on what fits.
An IC-ready read on the deal's merits and risks, written for you.
A first-draft investment-committee memo, ready to edit instead of write.
Every new deal arrives with your own prior context already written: the deals that rhyme, what you decided, and why.
Your back catalogue, loaded with years of your firm's judgment working from day one.
Most software starts empty and learns slowly.
Antonine starts knowing everything your firm already does.
A new deal lands with firm-specific prior context already written, citing the deals that matter.
“Have we seen anything like this from Firm XYZ before?” Answered instantly.
A “revisit at <10× EBITDA” note from three years ago resurfaces automatically when a comparable lands.
Ontology, isolation, routing, deployment.
Every deal, document, agent, and decision is a typed object in your firm's ontology. Links between objects compound into a knowledge graph that gets denser the more you use the platform. New analysts query the graph in natural language and get source-grounded answers.
Each customer firm gets its own deployment record, its own asset class scope, its own agent configuration. Zero cross-customer data exposure. AES-256 at rest, TLS in transit. Optional on-premise or private cloud deployment.
Every task is routed to the most effective model per token for the job, so you get maximum value for every dollar of AI spend. Heavy reasoning goes to frontier models; routine extraction goes to cheaper, faster ones. Routing happens at the agent invocation layer, with a fallback model behind each deployment.
Modules ship independently. A firm can adopt only The Vault to consolidate scattered institutional memory, or only DealFilter for screening, or the full stack. Module gating happens in the ontology layer. Adoption compounds without fragmenting infrastructure.