Vector & Hybrid Search
Dense embeddings fused with keyword and metadata filters to retrieve the right passage every time.
← All servicesKnowledge AI
Give AI perfect recall of everything your company knows
We build retrieval systems that let AI answer from your real documents — accurately, with citations, and always up to date.
- Internal knowledge assistant across wikis, docs, and tickets
- Customer support bot grounded in help center content
- Legal and compliance research over policy libraries
- Sales enablement search across decks, specs, and case studies
90%+answer accuracy with source citations
Capabilities
Vector & Hybrid Search
Smart Chunking & Indexing
Citations & Grounding
Real-Time Sync
What RAG Systems delivers
Production-grade rag systems, engineered and shipped by one accountable team.
Smart Chunking & Indexing
Structure-aware chunking, hierarchical indexes, and re-ranking that preserve meaning across long documents.
Citations & Grounding
Every answer links back to source passages so users can trust and verify what the AI tells them.
Real-Time Sync
Incremental ingestion pipelines keep your knowledge base fresh as documents and records change.
Access Control
Row and document-level permissions ensure users only retrieve what they are authorized to see.
Retrieval Evaluation
Recall, precision, and faithfulness metrics catch drift before it reaches your users.
From idea to production
A transparent, low-risk path — validated on your data before you commit.
Knowledge Audit
We inventory your data sources, formats, and access rules to design the right ingestion architecture.
Pipeline Engineering
We build parsing, chunking, embedding, and indexing pipelines tuned to your content and query patterns.
Retrieval Tuning
We optimize search, re-ranking, and prompt assembly against a labeled eval set for maximum accuracy.
Launch & Monitor
We deploy with usage analytics and quality dashboards, then refine retrieval from real user questions.
Where teams put it to work
- Internal knowledge assistant across wikis, docs, and tickets
- Customer support bot grounded in help center content
- Legal and compliance research over policy libraries
- Sales enablement search across decks, specs, and case studies
Outcomes teams actually see
90%+grounded, citable answers
–70%time spent searching for information
5xfaster support and research responses
24/7instant access to institutional knowledge
Questions, answered
Context windows are limited and expensive at scale. Retrieval fetches only the most relevant passages, which improves accuracy while cutting cost and latency.
PDFs, Office files, wikis, databases, SharePoint, Google Drive, Slack, ticketing systems, and custom APIs — we build connectors for whatever you use.
Our ingestion pipelines sync incrementally and version content, so retrieved answers always reflect the latest approved sources.
Yes. We enforce document and row-level access at retrieval time so answers never leak content a user cannot access.
◆ Let's build
Ready to put AI to work in your industry?
Tell us your challenge. We'll come back with a concrete, no-obligation plan and a live demo of what's possible for your team.
- Free AI auditWe map the highest-ROI AI opportunities across your workflows.
- Prototype in weeksA working proof-of-concept on your real data before you commit.
- One accountable teamStrategy, models, data and deployment — end to end.
120+ teams shipped across 6 industries
Book a free demo
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