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Pinecone Β· pgvector Β· Weaviate Β· Hybrid Search

πŸ” RAG Systems
Knowledge That Stays True.

Give your AI accurate, grounded answers from your own documents and databases. We build production-grade Retrieval-Augmented Generation pipelines that cite every source and consistently stay below a 2% hallucination rate.

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94%
Avg Accuracy
β˜…β˜…β˜…β˜…β˜…
4.9 / 5.0
<2%
Hallucination Rate
On-Prem
Option
services/ai/rag-pipeline.py
from langchain_community.vectorstores import PGVector
from langchain_openai import OpenAIEmbeddings
# Production RAG β€” hybrid retrieval
vectorstore = PGVector.from_existing_index(
embedding=OpenAIEmbeddings(model="text-embedding-3-large"),
connection_string=os.getenv("PGVECTOR_URL")
)
retriever = vectorstore.as_retriever(
search_type="mmr", search_kwargs={"k": 6, "fetch_k": 20}
)
# Accuracy: 94.2% on domain eval set
What We Build

Every RAG pattern,
built to production grade

From simple document Q&A to multi-source enterprise knowledge bases with role-based access β€” we cover every production RAG architecture with proven accuracy targets.

Discuss Your Project β†’
  • β†’PDF, Word, HTML, CSV, JSON, and PPTX document ingestion pipelines
  • β†’Semantic chunking strategy optimisation (sentence, paragraph, parent-child)
  • β†’OpenAI Ada-3, Cohere Embed, and BGE-M3 embedding models
  • β†’Pinecone, Weaviate, pgvector, and Qdrant vector store configuration
  • β†’Hybrid semantic + BM25 keyword search for maximum retrieval precision
  • β†’Cross-encoder re-ranking (Cohere reranker, bge-reranker) for top results
  • β†’Source attribution and citation on every generated answer
  • β†’Role-based document access control for enterprise deployments
Services Breakdown

Full-spectrum
RAG engineering

Every layer of the RAG stack β€” from document ingestion to answer generation β€” owned by one team, delivered end-to-end.

πŸ“„
Document Ingestion
PDF Β· Word Β· HTML Β· CSV Β· JSON

Robust ETL pipelines that ingest your documents, apply intelligent chunking, extract metadata, and keep the index in sync.

  • PDF, Word, HTML, CSV, JSON, PPTX ingestion
  • Adaptive chunking (semantic and parent-child)
  • Metadata extraction and structured tagging
  • Incremental update pipelines via webhooks
πŸ—„
Vector Database Setup
Pinecone Β· pgvector Β· Weaviate Β· Qdrant

The right vector store and embedding model for your scale and latency β€” evaluated and configured for your exact use case.

  • Pinecone serverless and pod-based setup
  • pgvector on PostgreSQL (zero new infra)
  • Weaviate and Qdrant self-hosted options
  • Namespace and collection architecture design
πŸ”Ž
Retrieval Optimisation
Hybrid Β· Re-ranking Β· HyDE Β· MMR

Naive cosine similarity gets 70% accuracy. Hybrid search, re-ranking, and query expansion get you to 90%+ reliably.

  • Hybrid semantic + BM25 keyword retrieval
  • Cross-encoder re-ranking for precision
  • HyDE hypothetical document embeddings
  • MMR diversity-optimised retrieval
βœ…
Hallucination Prevention
Source attribution Β· Confidence Β· Fallbacks

Source citation on every answer, confidence thresholds, and "I don't know" fallbacks β€” hallucination is a hard failure criterion.

  • Source attribution with document references
  • Answer confidence scoring and thresholds
  • Graceful fallback to "I don't know"
  • RAGAS and TruLens evaluation frameworks
πŸ”
Enterprise RAG
Multi-tenant Β· RBAC Β· Audit logs

Role-based document access, multi-tenant isolation, and full audit logging for sensitive enterprise deployments.

  • Role-based access control on documents
  • Multi-tenant knowledge base isolation
  • Complete audit log of every retrieval
  • On-premise or private cloud deployment
πŸ”„
Continuous Improvement
Analytics Β· Gap detection Β· Retraining

RAG systems degrade over time. We build feedback loops and monitoring to keep accuracy high as your content evolves.

  • Query analytics and unanswered detection
  • Embedding model upgrade path planning
  • Weekly retrieval quality reports
  • Automated index refresh pipelines
Technology

The stack behind every RAG project

Best-in-class tools chosen for performance, reliability, and team expertise β€” not hype.

PineconepgvectorWeaviateQdrantOpenAI Ada-3BGE-M3Cohere EmbedLangChainLlamaIndexRAGASTruLensFastAPIAWS S3Apache Tika
Our Process

Brief to deployed β€” how we work

A clear, collaborative process with no surprises and working demos at every milestone.

01
Knowledge Audit
Week 1

Map document sources, formats, update frequency, access controls. Define retrieval accuracy targets and build evaluation dataset.

02
Ingestion Pipeline
Week 1–3

Build ETL pipeline for all document types with chunking, metadata, embedding generation, and incremental update triggers.

03
Vector Store & Retrieval
Week 2–4

Deploy and tune vector store, implement hybrid search, optimise chunk size and embedding model against your eval dataset.

04
Week 3–5

Connect retrieval to your LLM with system prompt, citation formatting, confidence scoring, and fallback patterns.

05
RAGAS Evaluation
Week 4–6

Measure faithfulness, answer relevance, context precision, and recall. Iterate until accuracy targets are met.

06
Production & Monitoring
Week 6–8

Deploy with monitoring, quality degradation alerts, and a continuous improvement feedback loop.

Why Nexcode

What sets our RAG work apart

πŸ—
Senior Engineers Only

No juniors, no mid-weight delegation. Every engineer on your project is 5+ years experience, senior by any measure.

⚑
Performance as Pass/Fail

We set Lighthouse 90+ as a non-negotiable acceptance criterion β€” not a target, a requirement. Deployments fail if CWV regress.

πŸ§ͺ
Test Coverage Standard

Unit, integration, and E2E tests as standard deliverable. We don't ship without coverage. No exceptions under deadline pressure.

πŸ“
Architecture Before Code

Full system design β€” schema, API contracts, auth, deployment β€” documented and approved before any code is written.

β™Ώ
Accessibility Built-in

WCAG 2.1 AA from component 1, not added at the end. Keyboard navigation, screen readers, colour contrast β€” non-negotiable.

πŸ”
Weekly Working Demos

End of every sprint, you get a live staging URL to click through. Not a Loom recording β€” a real deployed demo.

πŸ”’
Zero Lock-in Guarantee

100% IP & code transfer. Your repo, your infra, your AWS account. Full documentation so your team can own it the day we hand over.

πŸ“Š
Analytics-Ready Launch

GA4, Mixpanel or Amplitude wired in before go-live. You launch with data, not waiting weeks to set up tracking after.

How we compare
Criteria ✦ Nexcode Typical Agency Offshore Dev Shop Freelancer
Full IP & code ownershipβœ“βœ“βœ“βœ“
Client Reviews

What clients say about our RAG work

β˜…β˜…β˜…β˜…β˜…
4.9 / 5.0 Β· 50+ AI projects
"

Nexcode rebuilt our entire frontend in Next.js App Router in 12 weeks. Lighthouse score went from 41 to 97. The code quality, test coverage, and documentation are unlike anything I've ever received from an external team. We extended the engagement twice.

SM
Sarah Mitchell
CTO, Apex Financial Β· SaaS Platform Rebuild
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Upwork Verified
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"They architected and built our entire web platform from scratch β€” real-time collaboration, complex permissions, WebSockets. Every edge case handled, zero bugs at launch."

JK
James Kowalski
CEO, NovaBrain AI
AI Web Platform
β˜…β˜…β˜…β˜…β˜…

"Our new storefront loads in 0.8s and converts at 3.2x our old Magento site. Every detail considered β€” mobile-first, accessibility, structured data. The results speak."

RP
Rachel Patel
Director, LuxeCommerce
Headless eCommerce
β˜…β˜…β˜…β˜…β˜…

"From Figma to deployed in 8 weeks. Their React architecture thinking sets them apart from every agency I\"

TN
Thomas Nguyen
Founder, WanderGo
Travel Booking Platform
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"200K concurrent users on launch day β€” not a single outage. The infrastructure and caching strategy Nexcode built handled load I didn\"

LM
Laura MΓΌller
VP Product, EduPath
EdTech LMS
β˜…β˜…β˜…β˜…β˜…

"The real-time dashboard processes 1M+ events/day without a hiccup. Clean code, exceptional docs, and they explained every architectural decision. Extended the team afterward."

AK
Amir Khan
CTO, SwiftFreight
Logistics Dashboard
FAQ

RAG Systems questions
answered

Have a question not covered here? Book a free 30-min call β†’

What is RAG and why is it better than fine-tuning for Q&A?↕
RAG retrieves relevant documents at query time and passes them as context to the LLM. Unlike fine-tuning, RAG keeps your knowledge current without expensive retraining, provides source citations for every answer, and dramatically reduces hallucination. Fine-tuning is better for changing how a model behaves β€” RAG is better for giving it accurate access to your knowledge.
How accurate can a RAG system realistically be?↕
With well-designed chunking, embedding selection, hybrid retrieval, and re-ranking, our production RAG systems consistently achieve 87–95% accuracy on domain-specific knowledge bases. We measure with RAGAS throughout development and target below 2% hallucination before every go-live.
Can RAG connect to live databases and APIs, not just documents?↕
Yes. We build multi-source RAG that retrieves from PDFs, Word documents, internal wikis, SQL databases (via Text-to-SQL), REST APIs, and real-time data. The retrieval layer is fully source-agnostic.
What does a RAG system cost?↕
Single-source RAG system from Β£12,000. Multi-source enterprise RAG with RBAC and monitoring from Β£22,000. Fixed-price proposals after a free scoping call.
Related Services

Often paired with RAG Systems

🧠
Model backbone for RAG
β†’
πŸ’¬
Conversational RAG layer
β†’
πŸ€–
Agentic retrieval flows
β†’
βš™οΈ
Deployment and scaling
β†’
πŸ”

Build AI that knows your business inside out.

Free RAG scoping call. We review your knowledge base, define accuracy targets, and provide a fixed-price proposal within 48 hours.

Get a Free AI Scoping Call β†’ View All AI Services