T2K Text2Knowledge

For teams who can’t afford to lose structure

Beyond text.
Toward understanding.

When you summarize a clinical note, you lose the relationships between diagnoses, medications, and gaps in care. Text2Knowledge extracts structure instead — typed entities, provenance, and connections your team can inspect, query, and build on.

Explore Ontology Studio Read the Vision

Ontology Studio is in early access. We’re onboarding design partners who work with complex documents daily.

Vision

Text is the visible trace. Knowledge is the structure beneath it.

Most tools stop at search or summaries — they compress documents into prose and lose the structure that matters. Text2Knowledge preserves it: diagnoses linked to symptoms, medications tied to evidence, gaps flagged before they become failures.

Framework

From signal to meaning.

01

Text

A discharge summary. A research paper. A care plan note. Dense, unstructured, and full of buried connections.

02

Meaning

The patient is 72. The diagnosis is CHF. The claim is patient-reported. Each fact gets a type and a source.

03

Knowledge

Dyspnea is a symptom of CHF. Furosemide is prescribed for it. The relationships become explicit and queryable.

04

Impact

No follow-up plan documented. The gap is flagged before it becomes a missed appointment or a readmission.

Same clinical note, two different outputs:

Standard AI Summary

"Patient is a 72-year-old male with CHF and COPD experiencing dyspnea and edema. He takes furosemide and metoprolol but has difficulty following his fluid restrictions."

Readable, but flat. Relationships between symptoms, diagnoses, and medications are implicit. No structured links to query or audit.

T2K Ontology

Patient(72M) + Diagnosis(CHF, COPD) + Symptom(Dyspnea, Edema bilateral LE) + Medication(Furosemide 40mg daily, Metoprolol 50mg BID) + Claim(inconsistent fluid restriction adherence, patient-reported) + Gap(no documented follow-up plan).

Facts are captured as typed, linked entities. Relationships, provenance, and gaps stay explicit and queryable.

Ontology Studio

An editable world model built from language.

Ontology Studio is an LLM-native workspace for converting unstructured language into knowledge maps that can be inspected, challenged, and extended. It's live — try it now.

  • Extract entities, concepts, claims, and value signals
  • Model relationships, hierarchies, and causal chains
  • Separate fact, interpretation, belief, and implication
  • Reveal contradictions, gaps, and downstream impact
  • Build shared understanding instead of isolated summaries
Source Clinical note

Patient is a 72-year-old male presenting with worsening dyspnea and bilateral lower extremity edema. History of CHF and COPD. Current medications include furosemide 40mg daily and metoprolol 50mg BID. Patient reports inconsistent adherence to fluid restriction.

derived from note
Extracted Ontology 7 entities
  • Confidence: 99%
    Rule: Person Entity Link
    Patient 72M
  • Confidence: 98%
    Rule: Clinical Diagnosis Match
    Diagnosis CHF
  • Confidence: 98%
    Rule: Clinical Diagnosis Match
    Diagnosis COPD
  • Confidence: 96%
    Rule: Symptom Classification
    Symptom Dyspnea
  • Confidence: 94%
    Rule: Symptom Classification
    Symptom Edema, bilateral LE
  • Confidence: 98%
    Rule: Med-Extract-V2
    Medication Furosemide 40mg daily
  • Confidence: 98%
    Rule: Med-Extract-V2
    Medication Metoprolol 50mg BID
Relationships
  • Dyspnea symptom of CHF
  • Edema symptom of CHF
  • CHF comorbid with COPD
  • Furosemide prescribed for CHF
Claim patient-reported

Inconsistent adherence to fluid restriction

Flagged: gap moderate confidence

Patient reports inconsistent fluid restriction adherence — no documented follow-up plan or monitoring note.

Now the team can query "which symptoms are linked to CHF?", spot the missing follow-up plan, and share a structured view instead of forwarding a wall of text.

Ontology Studio is live.

Pick a scenario — clinical notes, climate policy, remote work — and see structured extraction in action.

Open Ontology Studio

Background

Text2Knowledge is built on 25 years of work in NLP, machine learning, and applied data across healthcare, bioinformatics, and complex social systems. LLMs generate fluid language — we turn that language into typed, auditable structure that clinicians, researchers, and strategists can inspect and build on.

Use Cases

Start where text carries consequence.

A

Healthcare

You read a discharge summary. T2K extracts the diagnoses, medications, and gaps — then links them so your care coordination team can see what's connected, what's missing, and what needs follow-up. Compatible with FHIR and SNOMED-CT structures.

B

Research and Strategy

You have 30 papers and a thesis. T2K maps the claims, evidence chains, and contradictions across sources so your team can query the literature as a connected knowledge base instead of reading one PDF at a time.

C

Enterprise Knowledge

You have policies, SOPs, and meeting transcripts that contradict each other. T2K connects them into a queryable ontology so you can find conflicts, track how decisions evolve, and close gaps before they cause failures.

About

About Text2Knowledge.

Text2Knowledge is a solo-founded company built on 25 years of NLP and applied ML experience across healthcare, bioinformatics, and enterprise data. We're starting where language is densest: clinical knowledge, research, and high-stakes operations. Ontology Studio is our first product — in early access and built alongside our design partners.

FAQ

Common questions about Text2Knowledge.

Honest answers about where we are and where we're going.

What is Text2Knowledge?

A tool that turns documents into structured knowledge you can query. Instead of a summary paragraph, you get typed entities, relationships, claims with provenance, and flagged gaps — all inspectable and editable.

How is it different from an AI summary?

A summary compresses everything into prose. Ask yourself: can you query the output? With T2K, diagnoses stay linked to symptoms, medications stay tied to evidence, and gaps stay visible instead of being flattened into one paragraph.

Who is Ontology Studio for?

Clinical informaticists, care coordinators, research analysts, and anyone who spends hours reading dense documents and wishes the output was structured instead of another PDF.

What text can Text2Knowledge structure?

Today: clinical notes, discharge summaries, research papers, transcripts, and web pages. The parser handles PDF, DOCX, and plain text. More formats and deeper extraction are shipping every week.

Is Ontology Studio available now?

It's in early access. The core workflow — ingest, extract, review, and export — works today. We're onboarding design partners to stress-test it with real documents and shape what gets built next.

How does Text2Knowledge handle sensitive data?

We're building toward HIPAA-readiness and will support on-premise deployment. During the design partner phase, we recommend using de-identified or synthetic data. Ask us about your specific compliance needs.

Get started

Become a design partner

We’re looking for people who wrestle with complex documents every day — and want a better tool than summaries and search.

  • Test Ontology Studio with your real documents
  • Give structured feedback on extraction quality
  • Shape the roadmap for your domain’s needs
  • Get early access and priority onboarding at launch

Your domain becomes a first-class use case. You get a tool built around your workflow.

Get involved

Or email partners@text2knowledge.com

Get in touch

Text2Knowledge is pre-seed. If you work in healthcare AI, clinical informatics, or early-stage investing, we'd like to talk.

Start a conversation

Or email hello@text2knowledge.com