Vector Search
Disconnected text chunks. Context lost at retrieval.
- No relationships preserved between sources
- Hallucinations and shallow synthesis
- Provenance lost at the chunk boundary
Knowledge Graph Platform
/
Graph analytics · AI · Explainable intelligence
Turn fragmented data into queryable intelligence.
A knowledge graph platform for the modern enterprise.
GraphEdge Analytics transforms unstructured and fragmented data into explainable, queryable intelligence using graph analytics and AI. Stop chasing dashboards. Start asking your business a question and getting an answer it can defend.
Deploys on
AWS
·
Azure
·
Google Cloud
·
On-prem
·
Your VPC, your choice
01 / Why Graphs
Your data is connected. Your insights should be, too.
Most enterprises sit on millions of unstructured documents, emails, tickets, contracts, transactions, and logs — scattered across warehouses, lakes, SaaS apps, and shared drives. Vector search retrieves chunks of text. It loses the relationships. GraphEdge captures the relationships between every entity — people, processes, products, customers, contracts — and gives AI the context it needs to be genuinely useful.
80%+
of enterprise data is unstructured
documents · emails · tickets · transcripts · logs
12+
disconnected systems per Fortune 500
CRM · ERP · HRIS · ticketing · DMS · email · data lake
1
unified, queryable knowledge graph
every entity · every relationship · every source cited
GraphEdge
Customers, contracts, products, risks — typed and linked.
- Relationships are first-class context
- Every answer cites the source record
- Synthesis across the entire enterprise
02 / The Platform
Three capabilities. One graph-native engine.
GraphEdge Analytics is a domain-agnostic platform. Connect your structured, semi-structured, and unstructured data — the engine builds a queryable knowledge graph and exposes it through plain English, with full provenance.
01
Graph-native retrieval
We don't just retrieve text — we traverse relationships to find deep, synthesis-level answers across your entire enterprise.
02
Explainable AI
Stop trusting black boxes. Every AI-generated insight links back to the source record, paragraph, and graph path that produced it.
03
Enterprise-ready scalability
Production-grade architecture that handles growth from pilot to global rollout. Deploy on any cloud — AWS, Azure, GCP — on-prem, or inside your own VPC.
03 / For Corporates
Where graph-native intelligence changes the game.
The same engine, applied to the questions that move the needle for enterprise leaders — finance, operations, customer, and risk.
FINANCE
CASE / 01
Financial Integrity
Detect fraud rings by analyzing transactional flows, not just single transactions. Surface money-laundering, kickbacks, and conflict-of-interest patterns hidden in entity relationships.
finds
multi-hop fraud rings
OPERATIONS
CASE / 02
Operational Intelligence
Map your entire supply chain — vendors, parts, logistics, contracts — to identify bottlenecks and concentration risk before they hit the P&L.
predicts
disruptions early
CUSTOMER
CASE / 03
Customer 360
Link every interaction across CRM, support, billing, and product into a single graph. Deliver hyper-personalized experiences and spot churn before it happens.
unifies
every touchpoint
LEGAL & COMPLIANCE
CASE / 04
Contract & Obligation Intelligence
Every clause, party, deadline, and dependency — connected. Answer "which contracts expose us to X?" in seconds instead of weeks of manual review.
tracks
obligations & risk
RISK
CASE / 05
Enterprise Risk Mapping
Concentration, counterparty, regulatory, and cyber risk — modeled as a single typed graph. Run "what-if" traversals across the dependency network.
models
interconnected risk
KNOWLEDGE
CASE / 06
Internal Knowledge Search
Wikis, decks, tickets, emails, and transcripts unified into one graph. Employees ask plain-English questions and get cited, traceable answers.
answers
with provenance
04 / Why Now
Three curves just crossed.
Knowledge graphs aren't new — but the cost of building one over a live enterprise data set collapsed in the last 24 months. Three independent curves crossed at the same time, and graph-native AI went from research project to production-ready.
01
LLMs cleared natural-language graph queries
Plain-English questions now compile to graph traversals at >90% accuracy on the shapes business users actually ask.
60% → 90%+
02
Graph infrastructure got cheap
What used to be six-figure enterprise contracts is now consumption-priced. The cost floor for a live, queryable graph collapsed.
$100K/yr → <$500/mo
03
Enterprise data went machine-first
SaaS APIs, data lakes, and document stores ship structured JSON now. Ready for ingestion at scale — no more screen-scraping or brittle ETL.
PDF/HTML → JSON
Three years ago this stack cost $300K/yr. Today: $4K/mo. That's the unlock.
05 / How it works
From scattered data to graph-native answers.
-
01 Ingest
Connect your existing data sources.
SQL, NoSQL, APIs, data lakes, document stores, email, chat, ticketing — structured or unstructured. We meet your data where it already lives.
SQLNoSQLS3SharePointSalesforceJiraREST APIs -
02 Model
The engine builds your knowledge graph.
LLM-driven entity and relationship extraction. Identity resolution across systems. Schema inference tuned to your business — not a generic ontology.
Entity ResolutionEdge ClassificationSchema GroundingGraph Embeddings -
03 Ask
Question your business in plain English.
Plain-English questions return answers backed by the exact graph paths that produced them. Synthesis across the whole enterprise — with full provenance.
Natural languageGraph traversalProvenance trailsSub-second p95
06 / Get in touch
Let's map your knowledge graph.
Pilots on enterprise data. Demos of the platform. Conversations with anyone serious about graph-native intelligence.
GENERAL
info@graphedgeanalytics.com
FOUNDER
smaitra1@graphedgeanalytics.com
FIRST PRODUCT
silentfacts.com — now live
STATUS
Building in public · Pilots open now