Private enterprise knowledge systems and RAG for companies that need their proprietary data queryable, secure, and on infrastructure they control.
By Citara Labs, a design and engineering studio. GCC, India, and USA.
The design of the system before any engineering begins. What data is ingested, how it is structured, how it is retrieved, and who has access to what.
A retrieval-augmented generation pipeline that queries your proprietary data and surfaces the right information to the right person at the right moment.
Deployed on infrastructure you control. Your data never enters a public model. The system operates entirely within your environment.
An interface your team can use to query the system, integrated into the tools they already use: Slack, Teams, your internal portal, or a custom interface.
Every company above a certain size has a knowledge retrieval problem. New hires spend months learning what experienced team members know. Support teams re-answer the same questions. Sales teams cannot find the proposal that addressed this exact objection six months ago. A private knowledge system makes that knowledge queryable.
Long onboarding time. New hires take months to reach the knowledge level of experienced team members. A knowledge system compresses that time.
Support knowledge locked in documents. Support teams have the knowledge but cannot retrieve it quickly enough. A knowledge system surfaces the right answer at the point of need.
Compliance and data privacy requirements. The business needs AI capabilities but cannot use public models with its proprietary data. A private deployment resolves the compliance question.
Knowledge leaving with people. Senior people carry institutional knowledge that is not captured anywhere. When they leave, the knowledge leaves with them.
We map the knowledge the system needs to make retrievable: where it lives, what format it is in, and how it is currently being accessed before any engineering begins.
The RAG pipeline is built and deployed on infrastructure the client controls. Data ingested, tested, and validated against real queries before go-live.
The interface the team uses to query the system is built and integrated. An operations plan defines how the system is maintained and how new knowledge is ingested over time.
The knowledge infrastructure that powers the Citara Labs studio operations is the same architecture we deploy for clients. We know what the real operational challenges are because we run into them every week.
A knowledge system built by Citara Labs is a production system with an operations plan. Not a proof of concept. Not a pilot. A system the business can rely on.
Private infrastructure. Your data never enters a public model. The system runs entirely on infrastructure you control.
Operations plan included. Every deployment includes a plan for ongoing data ingestion, system monitoring, and performance maintenance.
We run this architecture ourselves. The same system we sell is the system we operate. We know what works because we use it.
Part of a broader practice. If the engagement reveals needs for agentic workflows or AI agent deployment, those capabilities exist within the same studio.
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Read →Private enterprise knowledge systems are one of five services under the AI Deployment practice. The same senior team that builds knowledge systems also builds agentic workflows, AI agents, and AI integrations.
See the full practice →A short conversation with a senior partner is enough to map the problem and what building the right system would require.
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