Custom RAG as a Service for Businesses

It removes the daily blockers that slow teams down — slow access to critical documents, inconsistent answers across tools, and hours lost to manual lookup. We connect to your document stores, wikis, tickets, emails and apps to build a retrieval system that delivers fast, cited answers your teams can trust, plus dashboards to track accuracy, coverage and time saved.

Fine-tuning stores knowledge inside the model — it helps with tone but goes stale and needs new training cycles. RAG keeps knowledge outside the model and fetches only the most relevant sources at query time. That means fresher answers, built-in citations, lower cost, tighter access control, and far fewer hallucinations. We often pair RAG for search and Q&A with selective fine-tuning for tone or specialized language.

Yes. Agentic RAG goes beyond simple question-and-answer. Instead of a single retrieval step, an agentic system plans a sequence of retrievals, reasons across the results, and takes action based on what it finds — ideal for multi-step research, automated compliance checks and support flows that span multiple systems. We design and deploy agentic pipelines tailored to your workflows and compliance needs.

We work with the leading vector databases and pick the right one for your scale, latency and infrastructure — including Pinecone, Weaviate, Qdrant, Chroma and pgvector for teams already on PostgreSQL. For enterprise deployments we often pair a vector store with a metadata database for hybrid search, which improves precision significantly. We don't lock you into a single vendor.

Yes. We support VPC and on-premises deployments with role-based access, document-level permissions, SSO and audit logs. Access is enforced at both index and query time, so each user sees only what they're allowed to. We integrate with your identity provider, redact PII, encrypt data in transit and at rest, and keep detailed logs for compliance reviews.

We run a focused pilot in 4 to 8 weeks covering data preparation, retrieval setup, prompt design and initial evaluation. After that, we expand by use case and department, add integrations, and harden monitoring. You get clear KPIs and a working test interface early, then a staged production rollout with dashboards and alerts for quality and uptime.