Vector & Hybrid Search
Dense embeddings fused with keyword and metadata filters to retrieve the right passage every time.

We build retrieval systems that let AI answer from your real documents — accurately, with citations, and always up to date.
Production-grade rag systems, engineered and shipped by one accountable team.
Dense embeddings fused with keyword and metadata filters to retrieve the right passage every time.
Structure-aware chunking, hierarchical indexes, and re-ranking that preserve meaning across long documents.
Every answer links back to source passages so users can trust and verify what the AI tells them.
Incremental ingestion pipelines keep your knowledge base fresh as documents and records change.
Row and document-level permissions ensure users only retrieve what they are authorized to see.
Recall, precision, and faithfulness metrics catch drift before it reaches your users.
A transparent, low-risk path — validated on your data before you commit.
We inventory your data sources, formats, and access rules to design the right ingestion architecture.
We build parsing, chunking, embedding, and indexing pipelines tuned to your content and query patterns.
We optimize search, re-ranking, and prompt assembly against a labeled eval set for maximum accuracy.
We deploy with usage analytics and quality dashboards, then refine retrieval from real user questions.
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.
Cost depends on document volume, source count, and how strict the permission model needs to be. We scope a fixed quote after seeing your actual document set, not a generic package price.
Cost scopes to the problem, not a headcount rate card. After a free AI audit we return a fixed, itemized quote tied to clear milestones, so you know the number before any work starts.
Fixed price, tied to outcomes and scope. We don't bill open-ended hours — every engagement has a defined plan and cost agreed upfront.
Most engagements produce a working, data-validated prototype in 2–4 weeks, with full production rollout typically inside one quarter depending on scope and integration complexity.
Yes — we scope a proof of concept against your real data first, so you see measurable value before signing off on the full production build.
Security is built in by default: HIPAA, SOC 2 and GDPR-aware architecture, encryption in transit and at rest, role-based access control, and full audit logging on every deployment.
No, not without your explicit consent. Your data is used to serve your deployment — it is never used to train models for other clients.
Yes. We support private-cloud and on-premise deployments hosted within the UAE and wider GCC, so data-residency requirements are met without sending your data offshore.
Yes — for teams with strict compliance or residency requirements, we deploy self-hosted open models or private cloud infrastructure instead of public model APIs.
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.
120+ teams shipped across 6 industries
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