Artificial Intelligence
AI systems your team can run in production.
We scope AI work around one operational workflow, not demos. Typical engagements include private RAG on internal documents, approval-aware automation, and audit-ready controls for model access and outputs.
✓Internal copilots with role-based access controls
✓Retrieval-Augmented Generation (RAG) over private document sets
✓Workflow automation with approval and audit trails
✓Prompt/response filtering for sensitive data controls
✓Model routing between local and cloud inference paths
✓Evaluation sets for answer quality and citation accuracy
✓Operational monitoring for latency, cost, and drift
✓AI governance aligned with security and compliance policy
ai_orchestrator.ts — RAG execution path
// Enterprise context retrieval and governance
const context = await vectorStore.query({
tenantId: request.tenantId,
roles: request.user.roles,
vector: embedding,
threshold: 0.86,
});
const response = await aiPipeline.execute({
prompt: sanitize(request.prompt),
groundingDocuments: context.verifiedSources,
strictProvenance: true,
guardrails: { piiFilter: true, auditLog: true }
});