From scattered data to structured answers
Turn your <data.csv> into Actionable Knowledge
Documents, PDFs, APIs, spreadsheets — Graffold connects them into a single knowledge graph you can query in plain language. Every answer traces back to its source, with stepwise logic applied along the way.
The Problem
Your knowledge is scattered across thousands of documents, databases, and file formats. Standard search returns text snippets with no context. Teams waste months manually connecting findings.
The Solution
Graffold ingests your documents, extracts entities and relationships with LLMs, consolidates duplicates, and builds a knowledge graph you can query in natural language — with every answer traced to its source.
How It Works
Bring any database (Neo4j, Neptune, DuckDB), any LLM (Bedrock, Ollama, OpenAI), and any data source. Graffold is the scaffold — it connects them into one queryable knowledge layer.
Documents ingested in a single pipeline run
P50 query latency with multi-hop graph reasoning
Database and LLM backends — swap without rewriting
See It In Action
Ask a question. Get a cited answer.
"Which proteins are associated with both cardiovascular disease and type 2 diabetes?"
Answer 250ms · hybrid · 4 sources ▸
Several proteins show cross-disease associations. TNF-α is linked to cardiovascular inflammation and insulin resistance [PMID:31245678]. IL-6 mediates inflammatory pathways in atherosclerosis and T2D [PMID:29876543]. CRP serves as a shared biomarker [PMID:30123456].
"Which of these are detectable in blood plasma?"
Answer 170ms · local · session context ▸
All three are plasma-detectable. CRP is the standard clinical marker (hs-CRP assay) [PMID:28654321]. IL-6 and TNF-α require specialized immunoassays.
KNN Expansion +6 entities ▸
The Evolution of RAG
From Chunks to Connected Knowledge
Standard RAG retrieves text chunks. GraphRAG understands relationships. Semantic KGs add temporal awareness and contradiction detection.
Standard RAG
- ✗ Flat text chunks — no structure
- ✗ Loses context across documents
- ✗ Can't answer multi-hop questions
- ✗ No provenance or source tracing
- ✗ Duplicates treated as separate
GraphRAG
- ✓ Typed entities and relationships
- ✓ Cross-document reasoning
- ✓ Multi-hop graph traversal
- ✓ Full provenance chains
- ✓ Entity consolidation & dedup
Semantic KG
- ◇ Temporal validity windows on edges
- ◇ Contradiction detection across teams
- ◇ User context as graph structure
- ◇ Statistical edges from analysis
- ◇ Context-aware query routing
Use Cases
One Platform, Any Domain
The same platform works across industries. Bring your data, define your entities, and start querying.
Life Sciences
One example — bio connectors includedIngest 50,000+ research abstracts and full-text papers. Extract protein-disease relationships with ontology-backed entity resolution. Query multi-hop pathways researchers would take months to find manually.
- ✓ Protein-disease association mining
- ✓ Biomarker discovery across publications
- ✓ Ontology alignment (disease & protein taxonomies)
Legal & Compliance
Map regulatory documents, case law, and internal policies into a connected graph. Surface contradictions between jurisdictions, trace obligation chains, and answer compliance questions with full citation provenance.
- ✓ Regulation-to-obligation mapping
- ✓ Cross-jurisdictional contradiction detection
- ✓ Audit-ready provenance trails
Supply Chain & Logistics
Connect supplier records, shipping manifests, and risk reports into a unified graph. Identify single points of failure, trace component provenance, and model disruption cascades across multi-tier supply networks.
- ✓ Multi-tier supplier dependency mapping
- ✓ Disruption cascade modeling
- ✓ Component provenance tracking
Financial Services
Build entity graphs from filings, earnings calls, and news. Map corporate ownership structures, detect hidden risk exposures, and answer due diligence questions that span hundreds of documents.
- ✓ Corporate ownership & UBO mapping
- ✓ Risk exposure network analysis
- ✓ Multi-document due diligence
Your Domain
Graffold is domain-agnostic. Any source with an API plugs in as a connector. The PDF pipeline ingests local files. Bring your documents, define your entities, and start querying — the platform adapts to your schema.
- ✓ Custom entity types and relationship schemas
- ✓ Any API becomes a data connector
- ✓ PDF, CSV, Excel, Parquet — all supported