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COMOVA / RAG & AI search

Find the right information and keep its context.

Company information is often spread across disconnected systems. A retrieval architecture needs to respect source access, keep information current, and let people inspect supporting material.

Discuss your projectHow we build
01 / WHAT WE BUILD

From the problem
to a useful system.

Start with a defined scope, review the existing workflow, and agree how the result will be evaluated.

  • Permission-aware document retrieval
  • Source-linked knowledge interfaces
  • Indexing and refresh pipelines
02 / ENGINEERING APPROACH

A connected foundation.

ILLUSTRATIVE ARCHITECTURE
  1. 01Business requirements
  2. 02Information & access
  3. 03Application capabilities
  4. 04Connected workflows
  5. 05Review & operation
INDIVIDUAL CAPABILITIESONE DIRECTION

Make the decisions explicit.

Define the source of truth, the user journey, the integration boundaries, and the behavior when something needs human attention. The final architecture follows your project requirements.

PostgreSQLVector searchPythonDocument processing
Explore the delivery process
03 / RELATED SOLUTIONS

See the approach in context.

AI + AUTOMATION01

Customer support automation

Give routine questions a clear path to useful answers.

Support channelsApproved knowledgeAssisted response
EXPLORE THE ARCHITECTURE
INTELLIGENCE02

Internal AI knowledge

Make company knowledge accessible, with context.

Approved documentsAccess-aware retrievalLanguage model
EXPLORE THE ARCHITECTURE
AI + DATA03

AI document processing

Turn incoming documents into reviewable data.

Document intakeExtractionValidation & review
EXPLORE THE ARCHITECTURE
04 / BEFORE WE BEGIN

A few useful questions.

THE NEXT SYSTEM STARTS WITH A CONVERSATION

Ready to build
the system?

Tell us what you’re trying to build, automate or scale.

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