Same power, zero feature engineering
A relational foundation model reads your raw tables directly (foreign keys, timestamps, multi-hop paths) and predicts in-context. No pipelines to hand-build.
Production-Grade Graph AI
Predict anything on the datayou already have.
Graph foundation models proved you can predict anything on relational data. AhinsaAI makes that production grade: governed, audited, secured, and running inside your own environment. Your tables become accurate predictions, with the reasoning attached.
Runs on your data · Governance and hash-chained audit · Point-in-time safe
Built for the enterprises that run on relational data
- Financial Services
- Retail
- Media & Social
- Insurance
- Healthcare
- Telecom
- Supply Chain
The research breakthrough, made production-grade.
Graph foundation models are extraordinary in a notebook. AhinsaAI is what it takes to run them in your business: every day, on your data, under your controls.
Production from day one
Governance checks, hash-chained audit, multi-tenant isolation, fail-closed auth, request limits, and point-in-time backtests. The parts a notebook leaves out.
Every use case they promise
Fraud, churn, recommendations, lead scoring, forecasting, entity resolution and more. The full breadth of the graph-AI frontier, shipped and running on your data.
The prediction, the reasoning, and the audit trail. In one view.
Every score arrives explainable and accountable, ready for a person to review and sign off.
PREDICT payments.is_fraud FOR EACH account.id WITHIN NEXT 7 DAYS
| Account | Score | Decision |
|---|---|---|
| ACC-48213 | 0.94 | Review |
| ACC-30817 | 0.88 | Review |
| ACC-11097 | 0.61 | Watch |
| ACC-52244 | 0.43 | Watch |
| ACC-77310 | 0.08 | Clear |
Why ACC-48213 scored 0.94
Rapid pass-through ring
New device, three accounts
Velocity vs. 90-day norm
Counterparty risk
Audit record
scored 12:04:21Z · model v2.3 · hash 7f3a…e1c9 · point-in-time T-0 · routed to review queue
The idea
Traditional ML flattens your data. Graph AI reads the relationships.
Classic pipelines spend weeks hand-crafting features and, in the process, throw away the most predictive signal you have: how entities connect across tables, and how those connections move over time.
AhinsaAI reads your raw relational tables as a temporal graph, honoring foreign keys, timestamps, and the multi-hop patterns that link entities. No feature engineering. No leakage. Just predictions that arrive with their reasoning, a backtest, and an audit record attached.
The same data. A different ceiling.
Hand-built ML pipelines hit a wall the moment the signal lives between your tables. Graph AI is built for exactly that.
Traditional ML pipelines
- ✕ Weeks of manual feature engineering per task
- ✕ Relational & multi-hop signal flattened away
- ✕ Brittle pipelines that rot in production
- ✕ Leakage-prone, hard to backtest honestly
- ✕ No native governance or audit trail
AhinsaAI
Production-grade Graph AI
- ✓ Reads raw relational tables directly, with no feature engineering
- ✓ Learns multi-hop & temporal patterns across tables
- ✓ Point-in-time labels, with no forward leakage
- ✓ Built-in backtests on your own temporal split
- ✓ Governance and hash-chained audit on every decision
0.91ROC-AUC on RelBench rel-f1*
30+use cases
15industry verticals
100%runs on your data
* rel-f1 / driver-top3, reproducible on the public RelBench benchmark (published GraphSAGE baseline 0.755). All other figures are product facts; we never relabel synthetic-data results as client outcomes.
Six things that make Graph AI production-grade.
Reads relational data directly
Point it at your tables. The graph engine learns from foreign keys and multi-hop paths, with no feature engineering and no manual joins.
Temporal-safe by construction
Point-in-time labels and anchor timestamps mean the model only sees what it could have known. No forward leakage, ever.
Runs on your data
Every use case accepts your uploaded tables and runs its real feature pipeline, backtest, and governance. Never a canned demo.
Governance & audit
Policy checks at decision time and a hash-chained, tamper-evident audit log of every prediction and review action.
Enterprise security
Multi-tenant isolation, fail-closed authentication, request limits, and tenant-namespaced storage. Hardened, not bolted on.
Pluggable model engine
A local model and a graph prong engine score every example and blend, with per-prong held-out backtests so you see what is working.
Every use case the graph-AI frontier promises, shipped.
Built feature pipelines, governance, and backtests, not blank templates. A few favorites; the full catalog has 30+.
Payment fraud
Flag fraudulent transactions before they settle, using cross-account and device graph signal.
Mule-ring detection
Surface rapid pass-through rings via score-gated, amount-matched flow discovery across accounts.
Churn & LTV
Forecast attrition and lifetime value from transactional and engagement graphs.
Recommendations
Next-best-item and next-best-action from the interaction graph, with no hand-tuned features.
Credit underwriting
Score default risk on relational history with point-in-time-safe labels and explainable drivers.
Demand forecasting
Forecast demand and inventory across the supply-chain graph, at the horizon of your choosing.
One platform, fifteen industries.
Financial Services
Retail & E-commerce
Media & Entertainment
Insurance
Healthcare
Ad Tech
Telecom
B2B SaaS
Supply Chain
Manufacturing
Gaming
Travel
Real Estate
Education
Energy
Enterprise-ready from day one.
The controls, deployments, and integrations your security and platform teams ask for, built in rather than bolted on.
Deploy anywhere
- On-premises
- Private cloud and VPC
- Air-gapped
- Your data never leaves your environment
Security
- SSO and SAML
- Role-based access control
- Encryption in transit and at rest
- Fail-closed authentication
Governance
- Hash-chained, tamper-evident audit
- Multi-tenant isolation
- Point-in-time data safety
- Explainable, reversible decisions
Compliance
- SOC 2 Type II ready
- ISO 27001 aligned
- GDPR ready
- HIPAA-ready deployments
Connects to the stack you already run
Snowflake
Databricks
BigQuery
Amazon Redshift
PostgreSQL
Amazon S3
Azure
Research & benchmarks
Honest numbers. Reproducible benchmarks.
We validate on the public RelBench suite, where the work is reproducible, and we report synthetic-data results as exactly that. Vendor and client claims, when shown, are cited and attributed, never relabelled as our own.
- Point-in-time temporal safety, with no forward leakage at the anchor
- Per-prong held-out backtests on your own temporal split
- Bring-your-own-data uplift, measured local vs. local plus graph engine
Questions enterprise teams ask first.
In your environment. AhinsaAI deploys on-premises, in your private cloud, or air-gapped, and reads your tables in place. Your data does not leave your perimeter.
As a single-tenant service inside infrastructure you control: on-premises, in your VPC, or fully air-gapped. It connects to your existing warehouse and runs where your data already lives.
Single-tenant isolation, fail-closed authentication, role-based access, and encryption in transit and at rest. Deployments are SOC 2 Type II ready and ISO 27001 aligned, with GDPR and HIPAA-ready options.
No. You define the predictive question in PQL, and the graph foundation model handles feature engineering, training, and backtesting. Analysts can ship predictions without a pipeline to maintain.
Connect a labelled table and you can score, backtest, and audit a use case in minutes, then move it to production on your own data.
Your existing relational tables. Each use case declares the tables and columns it needs and validates them against a schema contract before it runs.
See it run on your data.
Bring a labelled table or use a bundled sample. We will score, backtest, and audit a use case end to end, in minutes.
We will reach out within one business day.