Relational AI for enterprise decisions.
Avra pre-trains on the relational economy, fine-tunes inside your workspace, and deploys predictions into the systems that run credit, fraud, growth, and monitoring.
Book a demoRelational AI for the decisions your systems already make.
The Graph Foundation Model, fine-tuned to your workspace and shipped back into the systems you already run — with evidence on every prediction.
Pre-trained on the relational economy.
Companies, individuals, counterparties, devices, and events are modeled as a temporal graph — not flattened into rows that erase the signal.
Fine-tuned inside your workspace.
Customer data stays isolated. Downstream weights are exclusive to your workspace, adapted to the relational signal only your business can see.
Evidence on every prediction.
Every score returns ranked relational evidence, model version, and decision context — for audit, monitoring, and review.
Earns its place in production.
Run Avra in shadow beside your incumbent on real production traffic. Promote where lift is proven. Roll back where it's not.
Lives inside the tooling your platform team already runs.
No migration. No bespoke schema. Avra reads your data, returns through your decision plane, and exports the audit trail your compliance team already monitors.
Reads your data plane
Streams, batch, warehouse mirror. Avra reads through the existing operational paths without bespoke schema or migration.
Returns through your decision plane
Predictions ship back to the systems already running credit, fraud, growth, and monitoring — as a callable surface or a batch table.
Exports its audit trail to yours
Ranked relational evidence, pinned model paths, and decision logs flow into your existing observability and compliance pipeline.
From foundation to decision.
Every customer gets their own model. Five steps from the pre-trained foundation to a prediction in your decision plane. One foundation. A different model per workspace.
Start from the pre-trained Graph Foundation Model.
Trained on the relational economy. Time-aware. Sealed read.
The hard part is already done. Avra ships a foundation model that encodes how entities behave in a real economy — companies, owners, counterparties, and the relationships between them, over time. Your workspace inherits that pre-training as the substrate every downstream model is built on.
Read the brief: foundationBring the relational signal only you can see.
Counterparties, transactions, internal labels — without migration.
Connect through stream, batch, or warehouse mirror. Avra extends the foundation with the structure inside your business — accounts, contracts, devices, behavioural sync. The workspace is sealed: your data stays in your tenant, and never enters the foundation.
Read the brief: workspace- KConnectedacme.counterpartieskafka · 12 partitions · 14d retention
- PConnectedacme.transactionsparquet · daily · s3://acme-data/tx
- SConnectedacme.labelssnowflake · read-only · sealed
Define the prediction in plain terms.
Target, horizon, entity, decision surface.
Default at 90 days. Fraud ring at multi-hop depth. Conversion propensity from a cold cohort. The same model architecture serves every relational task — you specify what to predict, the horizon, and where the answer should land. No bespoke feature engineering.
Read the brief: taskSpecialize on your data alone.
Customer-specific weights. Exclusive to your tenant.
The foundation is adapted into a downstream model trained on your relational signal. Weights are workspace-exclusive — they never leave your tenant, and no customer signal flows back into the foundation. One model per task, one task per workspace.
Read the brief: tuneReturn predictions through your decision plane.
API, batch, or callable surface. Evidence on every call.
Predictions ship back into the systems your team already runs — credit, fraud, growth, monitoring. Every score returns ranked relational evidence, pinned model version, and decision context. The audit trail flows into your existing compliance pipeline.
Read the brief: serve