Industry examples — bRRAIn Docs

Working SDK examples for healthcare, finance, legal, manufacturing, and IoT/teleoperation.

Industry examples

Each example is a complete, runnable pattern. Clone the full project from github.com/Qosil/bRRAIn/sdk-examples.

Healthcare — patient records

Use case: find similar prior diagnoses and treatments for a new patient encounter.

ws, _ := client.Workspace("clinic-main")

encounter := map[string]any{
    "type":        "patient_encounter",
    "patient":     "person:p-1234",
    "seen_at":     "2026-04-16T10:12:00Z",
    "chief_complaint": "chronic migraine, 3 weeks",
    "vitals":      map[string]any{"bp": "128/82", "hr": 72},
    "meds_reviewed": []string{"ibuprofen", "sumatriptan"},
    "classification": "confidential",
}
id, _ := ws.Store(encounter, sdk.WithContext(map[string]string{
    "provider": "person:dr-alice",
    "clinic":   "org:main-clinic",
}))

similar, _ := ws.Retrieve(sdk.Query{
    Search: "chronic migraine patient responded to sumatriptan",
    Types:  []string{"patient_encounter"},
    Limit:  5,
})

The Handler's healthcare domain adapter tags PHI appropriately and respects the confidential classification at Gate 2.

Finance — transactions and compliance

Use case: log every transaction and detect anomalous patterns via the graph.

ws, _ := client.Workspace("finance-ledger")

tx := map[string]any{
    "type":     "transaction",
    "tx_id":    "tx-9012",
    "amount":   24500.00,
    "currency": "USD",
    "from":     "account:acct-1",
    "to":       "account:acct-99",
    "purpose":  "invoice settlement",
    "occurred_at": "2026-04-16T12:45:00Z",
}
ws.Store(tx)

anomalies, _ := ws.Retrieve(sdk.Query{
    Search: "unusual transfers above $20,000 in last 7 days",
    Types:  []string{"transaction"},
    Filters: map[string]any{
        "amount":       sdk.Gte(20000),
        "occurred_at":  sdk.Gte(time.Now().Add(-7 * 24 * time.Hour).Format(time.RFC3339)),
    },
})

Legal — precedent discovery

Use case: find similar prior cases when drafting a brief.

ws, _ := client.Workspace("litigation")

casefile := map[string]any{
    "type":         "case",
    "caption":      "Acme v. Widget Corp",
    "court":        "US District Court, Delaware",
    "docket":       "1:26-cv-00234",
    "parties":      []string{"org:acme", "org:widget"},
    "issues":       []string{"breach of contract", "damages"},
    "status":       "filed",
}
ws.Store(casefile)

precedents, _ := ws.Retrieve(sdk.Query{
    Search: "breach of software licensing contract damages Delaware",
    Types:  []string{"case", "opinion"},
    Limit:  10,
})

Manufacturing — supply chain lineage

Use case: trace every component back to its supplier and lot.

ws, _ := client.Workspace("factory-alpha")

part := map[string]any{
    "type":      "part",
    "part_no":   "PN-987",
    "supplier":  "org:supplier-a",
    "lot":       "lot-2026-04-15",
    "received_at": "2026-04-15T08:00:00Z",
    "material":  "aluminum-6061",
}
ws.Store(part)

assembly := map[string]any{
    "type":       "assembly",
    "product":    "product:widget-pro",
    "parts_used": []string{"part:PN-987", "part:PN-988"},
    "operator":   "person:bob",
    "assembled_at": "2026-04-15T14:00:00Z",
}
ws.Store(assembly)

// Later, when a defect is discovered:
recalls, _ := ws.Retrieve(sdk.Query{
    Filters: map[string]any{
        "parts_used": sdk.Contains("part:PN-987"),
    },
})

IoT / teleoperation — sensor telemetry

Use case: ingest millions of sensor readings per day and detect anomalies.

ws, _ := client.Workspace("mine-shaft-3")

svc, _ := ws.CreateSession(sdk.SessionTypeService,
    sdk.WithMetadata(map[string]string{"purpose": "telemetry-ingester"}))

stream, _ := svc.StoreStream(ctx)
defer stream.Close()

for reading := range sensorNet.Readings() {
    stream.Send(map[string]any{
        "type":        "telemetry",
        "sensor":      reading.SensorID,
        "metric":      reading.Metric,
        "value":       reading.Value,
        "unit":        reading.Unit,
        "captured_at": reading.Timestamp,
    })
}

// Periodically check for anomalies:
anomalies, _ := ws.Retrieve(sdk.Query{
    Search: "temperature spike above threshold in zone B",
    Types:  []string{"telemetry"},
    Filters: map[string]any{
        "sensor.zone": "B",
        "metric":      "temperature_c",
        "value":       sdk.Gte(85.0),
    },
})

Pair the Service session with Agent sessions for teleoperated equipment so operators' commands and the robot's actions are both captured with full provenance.

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