Platform

Surface the graph. Predict.
Then act.

AhinsaAI applies graph AI to read the relationships hiding in your raw tables, predicts what is next, and hands the result to governed agents that take the action. One closed loop, run inside your environment.

Book a DemoHow it works →
Under the hood

Graph AI that reads relationships, not flat rows.

AhinsaAI applies a relational graph foundation model that learns across your tables at query time, honoring foreign keys, timestamps, and the multi-hop patterns that connect entities. It predicts in-context, so there is no task-specific pipeline to build or maintain.

Because it understands time, every prediction is anchored: the model only sees what was knowable at the cutoff. That's what makes a backtest honest and a deployment safe.

How it works

From relational tables to a governed action.

One loop: sense the graph, predict, decide under policy, act, and audit. Hover a node to inspect it, or step through it yourself.

✓
SENSE▋

Hover a node to inspect it. Click a step to jump.

  1. 1
    SenseRebuild the temporal graph from your tables, in place.
  2. 2
    PredictScore what is about to happen, point-in-time safe.
  3. 3
    DecideApply your policy and thresholds to the score.
  4. 4
    ActA governed agent takes the action: freeze, hold, route.
  5. 5
    AuditHash-chained, explainable, reversible. Then repeat.
fraud.query
# Will this account see a fraudulent payment in the next week?
PREDICT payments.is_fraud
FOR EACH account.id
WITHIN NEXT 7 DAYS

# → scored predictions + held-out backtest + risk drivers + audit record
Capabilities

Six things that make Graph AI production-grade.

01

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.

02

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.

03

Runs on your data

Every use case accepts your uploaded tables and runs its real feature pipeline, backtest, and governance, never a canned demo.

04

Governance & audit

Policy checks at decision time and a hash-chained, tamper-evident audit log of every prediction and review action.

05

Enterprise security

Multi-tenant isolation, fail-closed authentication, request limits, and tenant-namespaced storage. Hardened, not bolted-on.

06

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's working.

Security & governance

Built to pass the review, not just the demo.

The difference between a research model and a production platform is everything that surrounds the prediction.

Hash-chained audit

Every prediction and review action is written to a tamper-evident, hash-chained log, so you can reconstruct exactly what was decided, when, and on what.

Multi-tenant isolation

Tenant-namespaced datasets and registries. One tenant can never see another's data. Verified, not assumed.

Fail-closed auth

The gateway and APIs reject unauthenticated requests by default. Access is explicit, scoped, and revocable.

Request & ingestion limits

Body-size, row, column, and table caps with strict validation stop malformed or oversized uploads at the door.

Point-in-time governance

Temporal safety is enforced in the pipeline, so backtests can't leak the future and scores stay trustworthy.

Explainable by default

Every score arrives with risk drivers and a held-out backtest, so a human can confidently review and sign off.

See the platform on your data.

Bring a labelled table or use a bundled sample. We'll walk the full path (connect, query, score, backtest, audit) in minutes.

We'll reach out within one business day.