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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
+1 (210) 920-1680

ROI guarantee or money back within 90 days

Supported Platforms

P
Pinecone
W
Weaviate
P
pgvector
O
OpenAI
A
Anthropic
L
LangChain
U
Unstructured
F
FastAPI
PostgreSQL
PostgreSQL
S
S3

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.

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

  • PDF
  • 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
  • Email
  • 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

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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Free 30-minute session, zero obligation
Custom infrastructure & AI assessment
ROI projections for your environment
Prioritised next-steps roadmap

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Download the transformation guide

A 40-page blueprint covering DevOps, AI, Security, Data Engineering, and LLMOps best practices.

ROI calculation templates
Tool-selection frameworks
Implementation checklists
Industry benchmark data

Instant PDF — no form required

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Any domain — DevOps, AI, Data, Security
NDA available on request
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All enquiries answered within 4 hours on business days. Emergency support available 24/7.

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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.

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RAG & Knowledge Base Systems | Enterprise AI Search