GRAPH INFRASTRUCTURE · AGENTIC AI
Introducing Ather Forge, the agent framework

Build intelligent systems on connected data.

We build graph database infrastructure and agentic AI frameworks that turn complex relationships, enterprise knowledge, and real-time data into intelligent, defensible AI applications.

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Your data never leaves your laptop during the demo.
TWO PRODUCTS

A database and a framework. One graph foundation.

AtherGraph Available now

A high-performance graph database for connected data: knowledge graphs, graph analytics, vector + graph search, time-travel, and enterprise-scale relationship intelligence.

Explore AtherGraph →

Ather Forge In development

An agentic AI framework for building, orchestrating, and monitoring AI agents that reason, use tools, collaborate, and draw on graph-powered knowledge.

Explore Ather Forge →

We also deliver agentic-AI use cases as an enterprise service. Learn more.

EU AI Act high-risk AI system obligations: Active (Aug 2026) | US SR 11-7 AI Model Risk Management: Active examination standard | MiFID II / PCI-DSS AI audit trail requirements: Enforced Is your AI stack examination-ready? →
WHY GRAPH-NATIVE

Why graph-native, not vector RAG.

A vector store can find a similar paragraph. It cannot tell you why, and “why” is the question the examiner asks.

Vector RAGATHERGraph
Retrieval basis Statistical similarity of embeddings: “this text feels related” ✓ Explicit relationships: this entity is connected to that one, typed and directed
Explaining an answer Re-prompt the model and hope the story is consistent ✓ Click prediction → walk CAUSED_BY edges to source
Point-in-time truth Embeddings have no clock, so yesterday's truth contaminates today's answers ✓ Bitemporal layer: reconstruct exactly what was known, when
Multi-hop reasoning Chunk stuffing; hops beyond one document degrade fast ✓ Native k-hop traversal: account → device → counterparty → source, in one query
Audit artifact A transcript of what the model said ✓ A replayable graph path a regulator can inspect
Data residency Typically a managed cloud vector service ✓ Sovereign: on-prem or air-gapped, and data never leaves your perimeter
WHO WE ARE

A deep-tech company for connected intelligence.

We combine graph databases, agentic AI, retrieval, and reasoning workflows into one infrastructure layer, built in Rust for performance and deployed inside your perimeter.

The graph database underneath enterprise AI that has to be defended: sovereign, auditable, federated; built for the 2026–2028 wave of AI compliance.

TRUSTED BY COMPLIANCE TEAMS IN REGULATED FINANCIAL SERVICES
“For us the question wasn't ‘is graph faster than vectors?’ It was ‘can we defend the answer in a deposition?’ That's the part vector RAG quietly fails. AtherGraph was the first tool we tried where the audit was a graph walk, not a transcript.” A Head of Financial Crime Compliance at a mid-tier European bank under active BSA examination
WHAT WE DO

Three pillars, one engine.

Graph Data Infrastructure

Model, query, analyze, and scale highly connected data with a graph-native engine: traversal, analytics, vector and full-text search in one place.

Agentic AI Systems

Agents that plan, use tools, collaborate, retrieve knowledge, and complete workflows under governance, with every step recorded.

Graph AI Applications

Fraud detection, recommendations, enterprise search, cybersecurity, compliance, supply-chain intelligence, and knowledge discovery.

THE PROBLEM

Your AI is making decisions you cannot explain.

Your AI systems are making decisions you cannot currently explain to a regulator, an auditor, or a court. The EU AI Act enforcement window is open. SR 11-7 AI model risk guidance is active. That is a liability, and it is growing. ATHERGraph closes it.

Causal traceability on every prediction: not vibes, not summaries.

I'm a Compliance Officer. Show me the audit trail I'm an Engineer. Show me the architecture I'm an Executive. Show me the business case
ENGINEERED, MEASURED, PUBLISHED

Compliance-grade claims deserve benchmarks.

Every number below is reproducible from our benchmark suite, measured on official LDBC FinBench data and verified end to end.

9µs
server-side write latency
durable group-commit WAL, crash-recovery verified
13–30×
faster deep-hop traversal vs the leading in-memory graph DB
LDBC FinBench SF1, identical results
MVCC
snapshot isolation, concurrent writers
first-committer-wins, no cross-statement locks
7/7
HA failover checks pass
cross-host replication; promoted node fully durable

See the architecture & benchmarks →

THE TEAM

Engineers first.

Builders across graph systems, AI infrastructure, distributed systems, and enterprise software.

William Webster

William Webster

Executive Director / CEO

30+ years architecting compliance-first growth infrastructure for healthcare, finance & insurance. Founder, MEA Solutions. Fluent in both marketing and technology.

Pranjal

Pranjal

Chief Communication Officer

Enterprise strategy and product vision. Driving AtherGraph adoption across financial services and regulated industries.

Shrikant Kunden

Shrikant Kunden

CSO

20+ years across telecom, fintech & managed services. 6 patents, multiple national recognitions. CEO, entrepreneur & startup mentor.

Sandeep Madiwal

Sandeep Madiwal

CTO & CMO

20+ years in IT operations, program management & digital transformation. CEO of FMCIII. Building innovation ecosystems and startup infrastructure.

AI Compliance Readiness Assessment

Answer 8 questions about your current AI infrastructure. Get a free gap analysis against EU AI Act, SR 11-7, and BSA/AML audit trail requirements: something concrete to share with your leadership before a demo.

Take the free assessment (5 min) →