The unstructured-data problem

Your teams have the information. They just can’t see how it connects.

Analysts read disconnected PDFs, articles and transcripts by hand. Vector-only search finds similar text but can’t traverse relationships, so complex “who, what and where” questions go unanswered or hallucinated. GrasPh fixes both.

The old way

Search finds documents, not the full story

  • ✕Information overload. Teams manually read thousands of disparate reports, articles and transcripts with no systematic way to store the knowledge.
  • ✕Contextual blind spots. Keyword and vector search can't map how companies, people and products entities across documents, so signals get missed.
  • ✕Hallucinations on multi-hop. Vector-only RAG fabricates answers to relationship questions because similarity can't traverse logic.
  • ✕Build bottlenecks. Custom NLP pipelines, Cypher orchestration and graph UIs take engineering months teams don't have.
With GrasPh

GrasPh turns every source into connected knowledge

  • ✓Minutes, not weeks. Upload documents, web pages and videos to instantly create a connected knowledge layer without manual preparation.
  • ✓Connects the dots. Reveal relationships across people, organizations, events and documents, then explore them using natural language.
  • ✓Grounded & cited. Every answer is backed by contextual evidence and linked to its original source for transparency and trust.
  • ✓Out of the box. Built for rapid deployment with scalable ingestion, intelligent retrieval and intuitive visualization from day one.

Engineering & business impact

Get faster answers with the
context to trust them

95

%+

Reduction in Retrieval Latency

Cypher traversals beat pure vector scans

30

%

Lower AI Token Cost

Graph pruning and precise context shrink prompts

5

Retrieval Modes

Vector, graph, full text and hybrid blends

100

%

Grounded & Traceable

Every answer cited to source chunks and graph lineage

50,000+ documents, URLs and transcripts
unified into one graph.

Asynchronous multi-source ingestion across PDFs, web pages and video automatically.
Book a Demo →

How it works

From Scattered Data to
Smart Answers

GrasPh transforms enterprise content into a connected knowledge layer
that understands relationships, answers complex questions and
reveals insights hidden across thousands of sources.

↻ Hybrid GraphRAG retrieval ● Semantically relevant chunks ● Multi-hop walk ● Combined into one grounded, cited answer
Step 01 · Ingest

Bring every source into one intelligent workspace.

Import PDFs, web pages, reports, emails, databases and other enterprise content without restructuring or manual preparation.

Why GrasPH

"Isn't this just another RAG
chatbot?" No!
And here's
exactly why.

01

See every Connection

02

Trace every Answer

03

Match Retrieval to the Question

04

Build Graphs Automatically

05

Control Model and Costs

See how people, products, and events connect

Pillar 01
The flat-RAG problem

Vector search retrieves text that looks similar to your question. It can't connect a CEO to a product to a market across three different documents, so multi-hop "who, what and where" questions fall apart.

How GrasPh does it

GrasPh transforms enterprise content into connected knowledge, revealing relationships across every source. Instead of returning isolated passages, it uncovers the connections that lead to complete, contextual answers.

Entity graphMulti-hop CypherCross-document

Trust every answer with sources you can trace

Pillar 02
The flat-RAG problem

Pure-vector pilots hallucinate on complex questions, and you can't see why. There's no lineage from the generated answer back to the facts it supposedly came from.

How GrasPh does it

Every answer is backed by contextual evidence and linked to its original sources, giving teams the transparency, traceability and confidence needed for critical decisions.

Source citationsGraph lineageNear-zero hallucination

Use the right retrieval method for every question

Pillar 03
The flat-RAG problem

One-size-fits-all vector retrieval is brittle. Some questions need semantic recall, others need exact graph traversal. A single mode can't serve both well.

How GrasPh does it

GrasPh intelligently combines multiple retrieval strategies to deliver the most accurate results for every query, balancing semantic understanding with relationship-aware reasoning.

VectorGraphGraph+VectorFulltextHybrid

Build your knowledge graph without manual ETL

Pillar 04
The flat-RAG problem

Building an enterprise Knowledge Graph traditionally requires weeks of schema design, custom NLP pipelines, and manual graph engineering—time and expertise most teams can't afford.

How GrasPh does it

Upload your content and GrasPh automatically identifies key entities, relationships and supporting evidence, transforming disconnected information into connected knowledge in minutes.

LLMGraphTransformerAuto-extractionMinutes, not weeks

Choose your models and stay in control of costs

Pillar 05
The flat-RAG problem

Single-vendor RAG ties you to one model's price and limits, with little visibility into token spend until the bill arrives.

How GrasPh does it

GrasPh optimizes every request to deliver enterprise-scale performance with efficient resource utilization, giving organizations the flexibility to scale while keeping costs predictable.

11+ LLM providers~30% lower token costPer-user tracking

What it does

Connect your knowledge.
Ask better questions.
Trust every answer.

Build a Connected Knowledge Graph Automatically

Documents in, a connected knowledge graph out. No manual modeling.

  • Ingests PDFs, Word/TXT, images, web, Wikipedia, YouTube and S3/GCS in parallel.
  • Auto-extracts entities, relationships and properties to your schema without manual modeling.

Find the Right Context for Every Question

The right retrieval strategy for every kind of question.

  • Combines semantic and relationship-aware search.
  • Multi-hop traversal search resolves deep, cross-document queries.

Give Teams Answers They Can Verify

Deliver trustworthy AI answers with complete transparency and source verification.

  • Every response is backed by contextual evidence and linked to its original sources.
  • Explore connected relationships and supporting evidence with an interactive knowledge view.

What it does

Build on Proven Graph Technology
with the Models You Choose

Technology foundation

Start with a graph foundation built to scale

GrasPh provides an enterprise-ready knowledge platform that unifies ingestion, reasoning and visualization into a single experience.

Connected Knowledge Store
Unified storage for entities, relationships and evidence. Scalable knowledge foundation.
Single source of truth
Intelligent Knowledge Pipeline
Automated knowledge extraction. Transforms unstructured content into connected knowledge.
Auto graph build
Interactive Knowledge Explorer
Enhancement, exploration & visualization
Visual graph
Model-agnostic & cost-governed

11+ providers, with spend you can see

Extraction, embeddings and chat run on the model that fits each workload and token usage is tracked deterministically to prevent surprises.

1
Pick the right model

Select the best AI model for cost, speed or accuracy.

2
Track every token

Per-user, per-day and per-month token tracking gives deterministic visibility into LLM consumption.

3
Stay grounded

Generate answers backed by your enterprise knowledge.

Ready to Turn
Scattered Content into
Connected Intelligence?

Thirty minutes. Your sources, your questions, a LIVE knowledge graph and grounded answers at the end of it. No slideware.

Book a Demo →