Enterprise RAG and knowledge AI that teams can trust.
Prometheas builds retrieval-augmented generation systems and knowledge assistants for enterprises that need grounded answers from approved documents, tickets, cases, policies, CMDB records, CRM records, and internal knowledge. The work is designed around source quality, permissions, evaluation, and adoption.
Teams find approved knowledge and operational context faster.
Answers are grounded in governed sources with visibility into source quality.
Retrieval failures reveal stale, missing, or duplicated content that can be fixed.
Pressures shaping
the buying decision.
- Knowledge is spread across too many systems
Teams search across docs, intranet pages, ticket histories, CRM records, policy folders, and tribal knowledge before they can act.
- Generic AI cannot cite the truth
A model can sound confident while missing policy nuance, data permissions, or the newest operational record.
- Source quality is uneven
Old articles, duplicate documents, stale policies, and inconsistent tags reduce trust in AI answers.
- No evaluation loop
Teams need to know which answers are good, which sources are weak, and where retrieval fails.
The plays we run
to ship safely.
- Source readiness first
We assess documents, metadata, permissions, update routines, duplication, and ownership before building retrieval.
- Permission-aware retrieval
Users should only retrieve answers and sources they are allowed to see in the underlying systems.
- Citations and escalation
Answers include source references where needed, and low-confidence or sensitive outputs route to human review.
- Evaluation and improvement
We create test sets, feedback loops, retrieval diagnostics, and quality dashboards for ongoing improvement.
Where this
creates leverage.
- Employee knowledge assistant
Answer HR, IT, finance, operations, and policy questions from approved sources.
- Service and support knowledge AI
Retrieve known fixes, case history, customer context, and knowledge articles for agents.
- Operations policy assistant
Help teams interpret process, compliance, and exception rules with source references.
Sized to the
risk and scope.
- RAG readiness review
2-3 weeks to assess sources, permissions, content quality, and target workflows.
- Knowledge assistant pilot
8-12 weeks for governed retrieval, answer generation, citations, feedback, and launch training.
- Managed knowledge AI
Monthly quality review, source improvement, retrieval tuning, and new workflow expansion.
Supporting systems, not the main story.
We choose tools around ownership, risk, integration needs, and lifecycle cost. Buyers should see this as implementation support, not the definition of the solution.
What the engagement includes.
- Knowledge-source audit and cleanup roadmap
- RAG architecture and retrieval design
- Document, ticket, case, CRM, CMDB, and policy indexing
- Permission-aware answer generation
- Source citations and answer traceability
- Evaluation sets, feedback loops, and quality dashboards
- Managed retrieval and knowledge-quality improvement
Enterprise IT services and shared services
AI-assisted knowledge and service-triage pattern for teams with large internal knowledge estates.
Grounded knowledge retrieval
B2B SaaS support operations
Support modernization pattern for SaaS teams outgrowing a generic helpdesk.
Queue and ownership redesign
“A product is not finished when it launches. It is finished when customers can use it and the business can run it.”
Leads digital product engineering for customer portals, SaaS platforms, mobile apps, cloud modernization, and AI-enabled product experiences.
Full profileEnterprise RAG / Knowledge AI on your roadmap?
30 minutes with Kabir Malhotra. No slides, no deck — a practical architecture sketch, scope estimate, and candid second opinion.
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