Surface the graph
Rebuild the relationships your warehouse flattened away, directly from your raw tables. No feature engineering, no data leaving your environment.
Agentic Graph AI
Your data hides a graph.Our agents act on it.
Warehouses and lakes flatten the relationships in your data into rows and columns, and the signal disappears. AhinsaAI rebuilds that graph, predicts what is about to happen, and deploys governed agents that take the action. Prediction is where graph AI stops. It is where we start.
Surfaces the hidden graph · Predicts what's next · Acts autonomously · Governed and fully audited
Built for the enterprises that run on relational data
- Financial Services
- Retail
- Media & Social
- Insurance
- Healthcare
- Telecom
- Supply Chain
The hidden graph
Your warehouse flattened the most valuable signal you have.
The relationships between customers, accounts, devices, and transactions are what predict fraud, churn, and intent. Stored as flat rows in a warehouse or lake, those connections go dark, and the signal goes with them.
AhinsaAI reconstructs the temporal graph hiding in your tables, honoring foreign keys, timestamps, and multi-hop paths. No feature engineering. Nothing leaves your environment. The signal comes back, and so does what you can do with it.
Tabular models predict on flattened rows. We rebuild the graph between them, then act on it.
The loop, in 90 seconds.
Sense the graph, predict what is next, decide under your policy, act, and audit. Then do it again. Watch it run, or step through it yourself.
SENSE
Hover a node to inspect it. Click a step to jump.
- 1SenseRebuild the temporal graph from your tables, in place.
- 2PredictScore what is about to happen, point-in-time safe.
- 3DecideApply your policy and thresholds to the score.
- 4ActA governed agent takes the action: freeze, hold, route.
- 5AuditHash-chained, explainable, reversible. Then repeat.
We are not a predictive analytics company.
Dashboards hand you a number and a chart, then leave the work to a human. AhinsaAI takes the step the rest of the market skips: the action itself, taken autonomously and under your control.
Predict what is next
A production-grade graph foundation model scores the outcome with point-in-time safety and the drivers behind every score.
Act autonomously
Governed agents take the action the prediction implies, in the moment, with humans in the loop wherever you choose.
The prediction, the action the agent took, and the audit. In one view.
Not a chart to interpret. A decision made, executed, and accountable.
PREDICT payments.is_fraud FOR EACH account.id WITHIN NEXT 7 DAYS THEN ACT
| Account | Score | Agent action |
|---|---|---|
| ACC-48213 | 0.94 | Frozen |
| ACC-30817 | 0.88 | Held + step-up |
| ACC-11097 | 0.61 | Routed to review |
| ACC-52244 | 0.43 | Watch |
| ACC-77310 | 0.08 | Cleared |
Why the agent froze ACC-48213
Rapid pass-through ring
New device, three accounts
Velocity vs. 90-day norm
Counterparty risk
Audit record
agent froze account · 12:04:21Z · policy fraud-v3 · hash 7f3a…e1c9 · reversible · human override available
A score on a screen changes nothing.
Predictive analytics ends at the insight. The value is in the action, and the action is where we live.
Predictive analytics & dashboards
- ✕ Hand you a score and a chart
- ✕ Predict or recommend, then leave a human to act
- ✕ The action happens in another tool, days later
- ✕ Relationships flattened into hand-built features
- ✕ Locked inside one vendor's data warehouse
- ✕ No record of why an action was taken
AhinsaAI
Autonomous graph agents
- ✓ Surface the hidden graph in your data
- ✓ Predict and decide under your own policy
- ✓ Governed agents take the action in the moment
- ✓ Multi-hop and temporal signal preserved
- ✓ Runs in your environment, on any warehouse
- ✓ Every action explained, audited, and reversible
0.91ROC-AUC on RelBench rel-f1*
30+agentic use cases
15industry verticals
100%runs in your environment
* rel-f1 / driver-top3, reproducible on the public RelBench benchmark (published GraphSAGE baseline 0.755). The prediction is one stage of the loop; all other figures are product facts, and we never relabel synthetic-data results as client outcomes.
Six things that make autonomous action safe.
Surfaces the hidden graph
Rebuilds the relationships your warehouse flattened, straight from your raw tables. No feature engineering, no manual joins.
Predicts, point-in-time safe
Anchored labels mean the model only sees what was knowable. No forward leakage, so the decision behind an action is sound.
Acts through governed agents
Agents execute the action the prediction implies: freeze, hold, route, step-up, reprice, notify. Inside your policy, every time.
Human in the loop, by choice
Autonomy is a setting per action. Gate anything behind a human checkpoint, and reverse any action that was taken.
Governance & hash-chained audit
Every prediction, decision, and action is written to a tamper-evident log. Reconstruct exactly what happened, and why.
Runs in your environment
On-prem, private cloud, or air-gapped, with multi-tenant isolation and fail-closed auth. The graph and the agents stay with you.
Not reports. Actions.
Each use case predicts an outcome and takes the action it implies, governed and audited. A few favorites; the full catalog has 30+.
Payment fraud
Freeze or hold suspect transactions before they settle, on cross-account and device graph signal.
Mule-ring detection
Surface rapid pass-through rings and quarantine the accounts in the moment.
Churn & retention
Predict attrition and launch the retention play automatically, before the customer leaves.
Next best action
Serve the action most likely to convert or retain, not just a ranked list to read.
Account takeover
Step up authentication the moment relationship and behavior signals spike.
Credit underwriting
Decision the application with explainable, point-in-time risk and a full audit trail.
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
Autonomy you can put your name on.
Agents only earn the right to act when the controls are airtight. Ours are built in, not bolted on.
Deploy anywhere
- On-premises
- Private cloud and VPC
- Air-gapped
- Your data never leaves your environment
Control
- Per-action autonomy settings
- Human-in-the-loop checkpoints
- Reversible actions
- Policy and threshold engine
Governance
- Hash-chained, tamper-evident audit
- Multi-tenant isolation
- Point-in-time data safety
- Explainable decisions and actions
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
The action is only as good as the prediction behind it.
So we hold the prediction to a high bar. We validate on the public RelBench suite, where the work is reproducible, and report synthetic-data results as exactly that. Vendor and client claims, when shown, are cited and attributed.
- 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.
No. We are not predictive analytics or business intelligence. AhinsaAI surfaces the graph, predicts what is next, and takes the governed action. Reporting is a byproduct, not the product.
They act within the policy and thresholds you set. You decide which actions run fully autonomously and which require human approval, and every action is reversible.
Yes. Autonomy is a setting per action, not all or nothing. Any action can be gated behind a human checkpoint, and the full context is presented for the reviewer to approve or reject.
In your environment. AhinsaAI deploys on-premises, in your private cloud, or air-gapped, and rebuilds the graph from your tables in place. Your data does not leave your perimeter.
Whatever your systems expose: freeze or hold a transaction, step up authentication, route a case, reprice an offer, launch a retention play, open a ticket. Actions are connectors you approve, governed by policy.
Connect a labelled table and you can surface the graph, predict, and run a governed action in a sandbox in minutes, then promote it to production on your own data.
See an agent take the action, end to end.
Bring a labelled table or use a bundled sample. We will surface the graph, predict, and take a governed action, with the full audit in front of you.
We will reach out within one business day.