The data and integration foundation enterprise AI actually needs.
RAG-grade knowledge, vector and retrieval platforms, MDM alignment and governed connectors across your CRM, EHR, ERP, ITSM and CCaaS — so agents ground on trusted data, not scraped PDFs.
Model quality is capped by data quality.
Every stalled AI program traces back to the same root causes: knowledge lives in dozens of tools, entitlements aren't respected in retrieval, and integrations were built for humans, not agents. We close that gap with a reusable foundation.
- Knowledge is scatteredSharePoint, Confluence, ServiceNow, product docs, EHRs and CRMs — with no unified, permission-aware retrieval layer.
- Retrieval ignores entitlementsNaive RAG surfaces content users shouldn't see. Enterprise retrieval must respect role, region, tenant and record-level ACLs.
- Integrations aren't agent-readySystems of record expose human UIs, not scoped, idempotent, auditable tool calls for AI agents to use safely.
A reusable foundation across every AI use case.
Data readiness, retrieval, integrations and quality — engineered once, reused everywhere.
Inventory of sources, quality, lineage, sensitivity and access — with a prioritized remediation plan tied to use cases.
Ingestion, chunking, embeddings, hybrid search, reranking and evaluation — with entitlement-aware retrieval.
Managed vector databases (pgvector, OpenSearch, Pinecone, Azure AI Search) and hybrid retrieval patterns proven at enterprise scale.
Scoped, permissioned tool APIs for CRM, EHR, ERP, ITSM, CCaaS and internal systems — designed for AI agent consumption.
Golden records for customer, member, provider, patient and product — the identity backbone agents rely on.
Freshness, drift, PII, redaction and lineage monitoring — with alerting into your existing data platform.
A six-step model, from assessment to managed operations.
Every engagement follows the same rhythm — so business, IT and delivery stay aligned from opportunity to outcome.
Source inventory, data quality, entitlements, gaps.
Retrieval architecture, connectors, MDM strategy.
One flagship use case on the new foundation.
Ingestion, vector store, connectors, observability.
Onboard additional use cases, teams and domains.
Managed ops — freshness, evals, connector health.
Use cases already in production with enterprise clients.
One governed retrieval layer across every business unit instead of five parallel builds.
Scoped tool APIs for Salesforce, ServiceNow, Epic, SAP and Workday — with auth, audit and idempotency built in.
Entity-resolved golden records that ground agent decisions in the right record every time.
Ownership, review cycles and freshness SLAs for the content agents rely on.
Industries where this ships fastest
- Healthcare Providers
- Health Payers
- Financial Services
- Insurance
- Retail & Ecommerce
- BPO
Questions buyers ask us first.
- Do we need a new data platform?
- Usually no. We build the retrieval and integration layer on top of your existing Snowflake, Databricks, Fabric or cloud data warehouse — not around it.
- How is this different from a data lake project?
- This is retrieval and integration engineered for AI agents — permission-aware, latency-bound, and evaluated continuously. A lake stores data; this makes data usable by agents.
- Which vector database do you recommend?
- Choice is per workload. We routinely deploy pgvector, OpenSearch, Pinecone and Azure AI Search — selected on latency, scale, cost and existing footprint.
- How do you handle entitlements?
- Retrieval enforces the source-system ACLs at query time — user, role, region, tenant and record-level — with full audit trace.
Book a working session with our data & ai foundations services team.
30 minutes. Your architecture, your data, your KPIs. You leave with a concrete pilot outline and a business case worth defending.