High-value Signals Remain Locked Within your Texts
Stop wasting months on hand-built schemas and opaque extraction loops your team can't trust.
Corpus2Frame auto-models raw text into structured, evidence-linked variables for reliable BI, ML, and agent workflows.
Best for repeated record streams: Descriptions • Forms • Reviews • Inspections • Etc..
Extract and Ground
every Signal
Uncover the structure. C2F identifies recurring themes across your corpus and lifts evidence directly from the source.
- Evidence-first: Anchoring every variable in source text segments.
- Theme Discovery: Auto-detecting the schema latent in your records.
- Transformation: Turning raw prose into machine-ready data assets.
Objective response rate improved significantly in the treatment arm, with sustained benefit at 12-month follow-up.
...the Phase III clinical trial results demonstrate significant efficacy...
Adverse events remained within expected ranges across both cohorts, and no new safety signals were identified during the observation window.
Subject enrollment for the trial was completed in Q2 2023.
Follow-up assessments are scheduled at quarterly intervals, with interim analyses reported to the steering committee.
What is the primary subject of this document?
- "...the Phase III clinical trial results demonstrate significant efficacy..."
- "Subject enrollment for the trial was completed in Q2 2023."
Result: a Frame you can build on.
Every record becomes a row of typed answers: queryable values with evidence, versioned as a data product.
Query what used to be unqueryable.
Build cohorts and dashboards on text-derived fields with consistent definitions from the catalog.
"How many job posts mention React vs Svelte, by seniority, location, and team?"
Why it's better
Why Frames beat the usual approaches
Not embeddings-only
Vectors are difficult to audit and query. A Frame is typed and evidence-linked, making every value inspectable and queryable.
Not a triplet-store KG
A Frame is the primitive underneath: it projects into a knowledge graph or feature tables on demand, without the complex graph queries a triplet store forces on you.
Not RAG
Chatbot answers are transient, not state. A Frame is governed state that agents can safely read and write with full audit trails.
“C2F is a compiler: it turns text into a stable interface contract.”
Fit check
Is your corpus a fit?
Corpus-to-Frame works best with a harmonious corpus: a repeated record stream of the same kind of documents.
You have many records of the same kind (harmonious corpus)
You want reliable data for BI, ML, or safe automation
You need full auditability and explainability
You're tired of brittle prompts and bespoke extraction per feature
If your docs are a heterogeneous mix of random PDFs, you'll get better results from RAG or search
Pilot program
Prove value in 2–6 weeks
Start with a scoped pilot. If thresholds are not met, you stop. There is no lock-in.
Deliverables
- Frame v1: catalog, values, and evidence
- One thin-slice application: BI dashboard, ML uplift, or agent workflow
Success metrics
- → Product performance: coverage, evidence-rate, sampled agreement
- → Business performance: one downstream KPI (time saved, lift, conversion)
We'll return a scoped plan within 48 hours