A product by GroundingNodes
GraphPulse: the living knowledge graph for enterprise AI.
Enterprise AI is only as reliable as the knowledge behind it. GraphPulse turns unstructured enterprise data into structured, provenance-backed knowledge that can be extracted, reviewed, versioned and continuously evolved — giving AI agents a dependable foundation for reasoning and action.
You've hit the enterprise AI ceiling. We built the ladder.
If you are deploying AI in high-stakes environments, you already know where the standard architectures break down. You aren't looking for a prototype — you are trying to solve the fundamental limits of unstructured data retrieval.
RAG is dropping the context
Vector similarity is failing your multi-hop queries. When you rely on top-K chunk retrieval, your agents pull disconnected text fragments, miss crucial structural relationships, and lose the narrative thread entirely.
Fine-tuning is the wrong tool for facts
You tried fine-tuning to inject domain knowledge, only to realize it modifies behavior, not memory. When a regulatory standard or supplier contract updates, your fine-tuned weights are instantly stale, and retraining is an expensive, slow nightmare.
SLMs need heavy scaffolding
Small language models are highly efficient, but they struggle to reason over massive, noisy contexts without strict, pre-processed guidance.
Confidentiality is a hard blocker
You cannot stream M&A due diligence, clinical trial protocols, or proprietary spec sheets to a cloud LLM provider. The data must stay safely within your egress-contained network.
You need a pipeline, not a patch. GraphPulse is an adaptive automated extraction pipeline. We process dense, multi-format corpora, enforce strict data constraints, and map the output to a formal ontology.
Engineered for scale, speed, and certainty.
GraphPulse is designed to optimize how autonomous agents and human experts consume enterprise knowledge.
- 01
Radical tokenometry and cost efficiency
Stop brute-forcing 128k context windows with raw, noisy PDFs. GraphPulse compresses complex documents into precise semantic subgraphs. By feeding your agents only the exact nodes and edges required, you achieve massive token reduction ratios — slashing inference costs while completely eliminating the “lost in the middle” recall failure.
- 02
Query-driven adaptive expansion
Your data isn't static, and your graph shouldn't be either. GraphPulse is adaptive. Based on the density and nature of the queries your agents execute, the pipeline automatically deepens its extraction focus, expanding the graph topology exactly where your users need the most resolution.
- 03
Automated decay for millisecond latency
Enterprise data grows infinitely; your retrieval latency cannot. GraphPulse implements intelligent knowledge decay. By identifying and down-ranking stale, obsolete, or unqueried edges, the active “hot” graph remains lean. Your agents get the context they need in milliseconds, so you can serve your end-users at scale.
- 04
Air-gapped provenance
No orphan facts. Every node generated by GraphPulse passes through a strict provenance gateway, tracing back to an exact coordinate in the source document. Designed from the ground up to support local inference and offline embeddings, your data never has to leave your proprietary network.
Deploy it your way.
GraphPulse runs where your data governance says it has to. Fully air-gapped inside your network with local inference and offline embeddings, or hosted and operated by us. That is a deployment decision, not a product decision — the pipeline, the ontology, and the graph it produces are the same either way.
What GraphPulse does, at a glance.
- Extract
- Turn enterprise documents into structured knowledge.
- Ground
- Preserve provenance back to the source evidence.
- Resolve
- Have humans resolve ambiguity and conflicting knowledge.
- Temporalise
- Capture when information was valid and when that validity changed.
- Version
- Track successive states of the knowledge graph.
- Adapt
- Expand the graph when evidence or usage reveals gaps.
- Decay
- Identify knowledge that is stale, superseded or no longer supported.
Product Roadmap
We believe in radical transparency. Here is exactly what is live today, what we are actively building with our design partners, and where the architecture is heading next.
Shipping today
Structure-Aware Document Ingestion
PDF, DOCX, PPTX, TXT, URLs, and scanned PDFs — extracted with the source document's structure preserved, not flattened into plain text.
OCR & Scanned Document Handling
Scanned documents are detected automatically and routed through OCR, with language selection and page-range control.
Ontology-Driven Extraction
Entities and relationships are extracted against your controlled vocabularies, hierarchies, and constraints — not a generic schema.
Multi-Pass Extraction Pipeline
Discovery, retrieval, extraction, validation, and graph materialisation run as distinct passes, each checked before the next begins.
Evidence-Backed Knowledge
Every fact the graph accepts retains a link back to its source document and the exact segment it came from.
Provenance Enforcement
Provenance isn't a courtesy field — it's enforced structurally at the moment a fact is written to the graph.
Entity Resolution
Similarity matching, transitive resolution, and controlled merging keep the same real-world entity from fragmenting across the graph.
Constraint Enforcement
Every candidate fact is validated against your ontology and graph constraints before it's allowed to materialise.
Human-in-the-Loop Review
Structured decisions, review queues, resolution workflows, and bulk adjudication — your domain experts stay in the loop without doing it row by row.
Agent-Assisted Review (MCP)
Agents can inspect decision schemas and take part in human review through MCP, not just watch from outside it.
Deterministic Graph Materialisation
LLMs propose changes; a separate, validated deterministic service is what actually applies them to the graph.
Checkpointed Extraction
Long-running extraction jobs can be paused, resumed, restarted, and recovered without starting over.
Enterprise Permissions
Tenant isolation, permission graphs, teams, and authenticated API access, built in rather than bolted on.
Graph Export
Export to Turtle, JSON-LD, graph statements, or a SHACL projection — the graph leaves in formats your stack already reads.
GraphPulse API
Programmatic access to corpus management, extraction, review, and graph operations.
In build
Graph Versioning
Tracks how the graph evolves over time and preserves the history of every knowledge change.
Temporal Extraction
Extracts, represents, and reasons about when a fact was valid, when it changed, and when it was superseded.
Table-Aware Extraction
Makes tables first-class, provenance-backed evidence in the pipeline instead of collapsing them into flat text.
Adaptive Graphs
Identifies gaps in the graph and expands it through new evidence and targeted queries, rather than waiting for a full re-ingest.
Corpus-Scale Evaluation
Domain-specific gold datasets measure extraction, provenance, and entity-resolution quality at corpus scale, not on cherry-picked examples.
Next
Graph Decay Foundations
Foundational work to identify knowledge that has gone stale, been superseded, or dropped in confidence.
Enterprise Identity
Enterprise SSO and identity integration for production deployments.
Graph Database Integrations
Direct synchronisation with graph databases such as Neo4j.
Graph Decay — Batch Reconciliation
Automated batch reconciliation that retires stale knowledge rather than flagging it for someone to clean up.
Spreadsheet Ingestion
Native structural ingestion of Excel and CSV sources.
Private Model Execution
Run extraction against locally hosted or private LLM infrastructure, planned around specific customer needs.
Air-Gapped Deployment
Fully disconnected deployment for restricted environments with no external network path.
Continuous Knowledge Maintenance
Ongoing reconciliation and controlled updates to the graph as source material changes.
Build with us
The Design Partner Program
We are currently onboarding a select group of design partners to stress-test the GraphPulse pipeline. If your engineering team is actively blocked by the limitations of RAG or the cost of fine-tuning on highly complex documents, we want to talk.
Apply for the Design Partner ProgramYour data. Your map. Yours to keep.