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Method

Biotica runs AI-assisted, human-reviewed diligence and translates it into a citation-backed authority atlas with public and private layers. The same system supports fund-level thesis authority and deal-level underwriting communication.

How it works

Step 1

Define the scope

Pick one category thesis, one live deal, or both. We align on decision questions, audience, and target outputs before work starts.

Step 2

Build and review the evidence base

We assemble papers, patents, trials, companies, and market signals, then run human review to keep claims auditable and decision-relevant.

Step 3

Structure the diligence

Evidence is organized into claims, risks, comparables, and investment implications so teams can evaluate quickly and consistently.

Step 4

Publish public and private layers

Outputs are published as branded, crawlable authority pages plus private deal-support views for IC and co-investor workflows.

Step 5

Run as an operating system

Use the atlas in founder, LP, and syndication conversations while reusing the same evidence spine for ongoing diligence and updates.

Service modules

Diligence Engine

AI-assisted and human-reviewed research across science, patents, trials, companies, and markets.

Produces decision-ready evidence that can be audited and reused.

Evidence Graph

A structured map of claims, sources, companies, mechanisms, risks, and investment implications.

Gives teams one searchable source of truth instead of fragmented notes.

Authority Interface

A branded research interface with public and private views tailored to each audience.

Makes diligence usable in real workflows: IC, LP updates, founder discussions, and syndication.

SEO and GEO Layer

Crawlable pages, structured data, internal links, citations, and AI-readable summaries.

Turns diligence output into discoverable authority that compounds over time.

Deliverables

  • Branded Authority Atlas with public and private views
  • Fund-thesis and/or deal-specific authority pages
  • Structured claim ledger with source citations
  • Risk matrix and key underwriting questions
  • Patent, trial, and competitor landscape summary
  • Investment implications summary for internal and external communication
  • Searchable source library
  • SEO/GEO-ready structured page architecture

GEO/SEO mechanism (without hype)

Structured data is not a direct ranking factor; it is an eligibility and interpretation layer. In this model, GEO/SEO is information architecture for investment credibility.

  • Claims mapped to sources
  • Entities linked to evidence
  • Public and private layers with consistent semantics
  • Crawlable pages for search and AI interpretation

Quantitative evaluation

  • Time from first interaction to IC-ready decision memo
  • Time from first investor interaction to next-step diligence call
  • Percent of major claims with citation coverage
  • Reuse rate of diligence modules across related deals/theses
  • Qualified inbound conversations from authority pages
  • Follow-on conversation conversion rate from authority-page traffic

Qualitative evaluation

Run structured interviews with investment teams, founders, LP operators, and external co-investors or partners.

  • Which parts of the authority asset reduced confusion fastest?
  • Which claims gained trust because of evidence traceability?
  • Where did the artifact improve or fail to improve decision speed?
  • Did it change perceived expertise before live calls?
  • What objections remained unresolved after review?

Hypothesis and falsification standard

Hypothesis: when diligence is transformed into structured authority assets, teams will see improved decision velocity, stronger stakeholder trust, and better financing interactions.

Falsification: if citation-backed assets do not improve either decision-speed metrics or stakeholder confidence and conversion in a defined pilot window, the model is not creating practical advantage for that segment.

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