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Fix your underperforming RAG system and improve AI search accuracy.

Get Your RAG Assessment

Frequently asked questions:

− My current AI search is "hallucinating" or giving wrong answers. Can this be fixed without scrapping the whole project?
− We’ve already spent a fortune on development, but users don’t trust the answers. Why is that?
− Is our RAG implementation slow because the LLM is "too big"?
− We have thousands of documents. Is our indexing strategy the problem?
− Do you need access to our entire codebase to perform an assessment?

Many organizations have already deployed AI search, chat assistants, or retrieval-augmented generation (RAG) systems but continue to struggle with poor answer quality, hallucinations, low relevance, inconsistent retrieval, slow response times, or declining user adoption.

RAG Optimization, AI Search Optimization, RAG Consulting, and Enterprise RAG Support help improve these systems and ensure production-grade reliability.

Our RAG remediation services identify technical issues in existing implementations and provide practical recommendations to improve retrieval accuracy, answer quality, performance, governance, and maintainability.

Primary Goal

Fix underperforming AI search and RAG implementations without rebuilding the entire system.

Common Problems We Address

  • Hallucinated responses
  • Incorrect source citations
  • Missing relevant documents
  • Poor semantic search quality
  • Duplicate or conflicting answers
  • Excessive context windows
  • Low user trust
  • Slow response times
  • Incomplete document ingestion
  • Metadata quality issues
  • Chunking and indexing problems
  • Vector search tuning issues
  • Prompt engineering problems

Initial Assessment Requirements

For an initial remediation review, we typically request:

  • AI search or chatbot URL
  • Description of the primary issue
  • Sample questions and responses
  • Examples of inaccurate answers
  • Expected answers or source documents
  • Architecture overview if available
  • Existing retrieval or search settings
  • Access to logs or screenshots where available

Technical Review Areas

  • Document ingestion pipelines
  • Chunking strategies
  • Metadata enrichment
  • Vector database configuration
  • Hybrid search configuration
  • Retrieval tuning
  • Prompt construction
  • Citation generation
  • Model selection
  • Evaluation metrics
  • Guardrails and governance

Deliverables

  • Findings report
  • Prioritized remediation roadmap
  • Quick-win recommendations
  • Architecture recommendations
  • Retrieval optimization recommendations
  • Implementation estimates