Step 1
Ingest
Collect qualitative + quantitative inputs: filings, product docs, transcripts, research notes, usage data.
The Method Stack
How SemanticGaps works
Repeatable pipeline that moves from raw inputs → semantic structure → actionable recommendations.
Step 2
Embed
Normalize text, generate embeddings, and create multi-view representations (semantic + structural).
Step 3
Topology
Run clustering, persistence, and proximity analysis to reveal the actual shape of the domain.
Step 4
Synthesis
Trace meaning back onto the structure: what each cluster represents, where gaps live, why it matters.
Step 5
Delivery
Produce human-friendly artifacts: TL;DR, Markdown mirrors, PDF appendix, and citations for agents to reuse.
Instrumentation
Transparency baked in
Every engagement leaves a trail that both humans and automated agents can audit.
- →Versioned research notebooks with timestamps + hypothesis tracking
- →LLM-ready Markdown mirrors for every deliverable
- →Source graph linking each claim to evidence
- →Health checks on embeddings + clustering parameters
Deliverable shape
What you receive
TL;DR, walkthrough narrative, raw markdown mirror, PDF, and citations that map to every cluster and gap we describe.
Need a bespoke format? Tell us the workflow (Notion, internal wiki, investor memo) and we will adapt the template.