RAG & Knowledge Base Systems
Accurate Answers. Cited Sources. Your Data.
Build retrieval-augmented generation systems over your internal documents, codebases, databases, and knowledge bases — delivering accurate, cited answers without hallucination.
What's Included
ROI guarantee or money back within 90 days
Supported Platforms
Industry Certified
AWS, Azure, GCP Professional
50+ Enterprise Clients
Fortune 500 to startups
Zero Breach Record
Perfect security track record
Guaranteed Results
ROI or money back
How It Works
From Document to Accurate Answer.
Every RAG system runs the same six-stage pipeline. The quality of each stage determines answer accuracy — this is where most RAG implementations fail silently.
Ingest
Documents from every connected source — PDFs, Confluence, SharePoint, databases — are fetched, normalised, and queued. Incremental sync keeps the index current within minutes of any change.
Chunk
Content is split into semantically coherent segments — not arbitrary character counts. Chunk boundaries respect sentence and paragraph structure; source metadata is attached to every segment.
Embed
Each chunk is transformed into a dense vector that captures meaning — not just keywords. Model selection is benchmarked against your specific retrieval task before commit.
Index
Vectors are stored in an HNSW-optimised index designed for sub-100ms P99 retrieval. Technology is selected — Pinecone, pgvector, Qdrant, Weaviate — based on volume and ops requirements.
Retrieve
Queries trigger hybrid search — dense vector similarity plus BM25 keyword retrieval — with results re-ranked and filtered to chunks the requesting user is authorised to see.
Generate
Retrieved chunks are injected as grounding context. The LLM is instructed to answer only from that context, attribute every claim to a source, and decline when confidence is below threshold.
Why RAG
Grounded Answers.
Not Guesses.
Base LLMs answer from training data — general, stale, and impossible to verify. RAG grounds every response in your specific documents, with citations that let you trace every claim to its source.
The difference is not incremental. Below a confidence threshold, our systems explicitly say "I don't have enough information" — a capability bare LLMs lack entirely.
Data Sources
Ingest Everything
You Already Have.
We build connectors to every source your knowledge lives in — no manual exports, no format restrictions. New documents are available to query within minutes.
Documents
- DOCX
- PPTX
- Markdown
- HTML
- TXT
Platforms
- Confluence
- Notion
- SharePoint
- Google Drive
- Box
Dev & Code
- GitHub / GitLab
- Jira
- Linear
- Readme.io
- ADRs
Structured Data
- PostgreSQL
- MySQL
- Snowflake
- BigQuery
- CSV / JSON
Comms & Support
- Slack export
- Zendesk
- Intercom tickets
Use Cases
Where RAG Delivers.
Internal Employee Assistant
Employees ask natural language questions and get answers grounded in SOPs, HR policies, and internal wikis — with source links.
— 70% fewer "who do I ask" emails
Technical Documentation Q&A
Engineering teams query code, architecture docs, and runbooks in natural language instead of searching Confluence.
— 50% faster onboarding for devs
Legal & Compliance Search
Legal teams find relevant clauses, precedents, and regulatory guidance across thousands of documents instantly.
— 10× faster document review
Customer-Facing Product AI
Embed a knowledge base assistant in your product — customers get instant, accurate answers from your docs and help center.
— 60% reduction in support volume
Deployment Options
Cloud Managed
Managed vector infrastructure with auto-scaling and 99.9% uptime SLA.
— SMB and Mid-Market
Hybrid
Vector store and LLM inference in your VPC; ingestion pipeline in cloud.
— Enterprise
On-Premises
Fully air-gapped RAG with open-source LLMs for maximum data control.
— Government / Finance
FAQ
Questions Answered.
Not covered? Schedule a call — we answer your specific architecture questions directly, same week.
Every response is generated with strict grounding — the LLM is instructed to answer only from retrieved context and explicitly state when it doesn't have enough information. We implement faithfulness scoring in the evaluation pipeline and set confidence thresholds below which the system declines to answer rather than extrapolate.
50+
Enterprise clients
99.9%
Avg uptime delivered
$22M+
Annual cost savings
300%+
Avg first-year ROI
Trusted by 50+ enterprise organisations
Ready to transform
your infrastructure?
Join industry leaders who have achieved measurable results across DevOps, AI Agents, Data Engineering, Security, and custom product development. Use the calculator below to estimate your return — then choose how to get started.
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Schedule a strategy call
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Download the transformation guide
A 40-page blueprint covering DevOps, AI, Security, Data Engineering, and LLMOps best practices.
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Response within 4 hours
4-hour response
All enquiries answered within 4 hours on business days. Emergency support available 24/7.
NDA on request
Confidentiality protection available for all technical discussions, assessments, and proposals.
10+ years production experience
Deep expertise across Fortune 500 enterprises and high-growth startups in every solution domain.
Not sure where to start?
Every organisation is unique. Our team provides personalised guidance to help you understand exactly how transformation drives measurable results for your specific environment, team, and goals — across any domain.
Response within 4 hours • Emergency support 24/7