Enterprise Solution
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
Multi-format ingestion: PDFs, Word, Confluence, Notion, code repos, databases
Hybrid search combining dense vector and BM25 keyword retrieval
Chunk-level citation with source attribution in every response
Incremental indexing — new documents available within minutes
Access control inheritance — users only retrieve what they can access
Query rewriting and multi-hop reasoning for complex questions
Evaluation pipeline measuring faithfulness, relevance, and completeness
Multi-tenant architecture for team or department isolation
ROI guarantee or money back within 90 days
Supported Platforms
P
PineconeW
WeaviateP
pgvectorO
OpenAIA
AnthropicL
LangChainU
UnstructuredF
FastAPI
S
S3Industry 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.
01
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.
Multi-format parsingConnector syncChange detectionIncremental updates
02
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.
Recursive splittingOverlap windowsSentence boundariesMetadata tagging
03
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.
OpenAI text-3Cohere embed-v3BGE / E5 open-sourceBatch processing
04
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.
HNSW indexingPinecone / pgvector / QdrantFiltered namespacesMulti-tenant isolation
05
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.
Query rewritingDense + BM25 hybridPermission-filtered retrievalCross-encoder re-ranking
06
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.
Grounded generationChunk-level citationConfidence thresholdGraceful "I don't know"
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.
Metric
Base LLM
RAG System
Hallucination Rate
15–25%
< 2%
Answer Accuracy
60–70%
95%+
Source Attribution
None
100% cited
Knowledge Freshness
Training cutoff
Minutes old
Domain Specificity
General knowledge
Your exact data
Access Control
None enforced
RBAC at retrieval
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.
Enterprise
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
Technology
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 / Finance
Legal & Compliance Search
Legal teams find relevant clauses, precedents, and regulatory guidance across thousands of documents instantly.
— 10× faster document review
SaaS / Retail
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
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your infrastructure?
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