Hybrid retrieval · Citations · Production evals

Answers you can cite

Production-grade Retrieval-Augmented Generation systems that ground LLM responses in your proprietary data — hybrid search, cross-encoder re-ranking, graph-augmented retrieval, agentic RAG, and citation-grounded answers engineered for accuracy, scale, and compliance.

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HybridSearch
CitedAnswers
EvalsBuilt-in
ProdReady
What is RAG

Grounded in your data

Retrieval-Augmented Generation enhances LLM responses by retrieving relevant information from your proprietary sources before generating answers. RAG solves hallucination and data freshness — every response can be traced back to a specific source document for critical business decisions.

01

Accuracy with citations

Every answer references specific source documents your teams can verify.

02

No model retraining

Add new data instantly without fine-tuning — indexes update in hours, not weeks.

03

Data privacy

Your proprietary data stays in your infrastructure with full access controls.

04

Full auditability

Complete chain of evidence from query to answer for compliance reviews.

Maturity model

Three levels of RAG

We scope clearly so you know when to graduate from foundation to production to advanced patterns.

Level 01

Foundation

Validate feasibility on your corpus with semantic search and citation-grounded responses.

  • Document ingestion & chunking
  • Vector search (Qdrant, Pinecone, pgvector)
  • Basic citation grounding
Level 02

Production

Hybrid retrieval, reranking, eval harnesses, and monitoring for enterprise accuracy.

  • Hybrid dense + sparse retrieval
  • Cross-encoder reranking
  • RAGAS / DeepEval regression suites
Level 03

Advanced

Agentic RAG, graph-augmented retrieval, and multi-modal for complex question types.

  • Agentic multi-step retrieval
  • Graph RAG for entity relationships
  • Query decomposition & multi-modal
Decision guide

RAG vs fine-tuning

Many production systems use both — we help you choose the right mix for your data and use case.

RAG

Best for factual, changing data

  • Retrieve context at query time
  • Citation-backed, auditable answers
  • Update knowledge without retraining
  • Ideal for docs, policies, knowledge bases
Fine-tuning

Best for style & reasoning

  • Modify model weights with domain data
  • Adapt tone, format, and specialized reasoning
  • Higher upfront training investment
  • Combine with RAG for best results
What we build

Advanced RAG capabilities

From ingestion through evaluation — every layer of a production RAG system, engineered for your corpus and compliance requirements.

Ingestion

Document Ingestion

Automated pipelines for PDFs, Word, web pages, Confluence, SharePoint, Slack, and databases with semantic chunking.

  • Metadata preservation & PII handling
  • Custom chunk boundary detection
  • Incremental index updates
Retrieval

Hybrid Search

Dense vector + sparse BM25 with reciprocal rank fusion for maximum recall and precision.

Quality

Re-Ranking & Filtering

Cross-encoder rerankers and metadata filters so only the most relevant chunks reach the LLM.

Trust

Citation Grounding

Inline citations linking to source documents — verify claims and pass compliance audits.

Evals

Evaluation & Testing

MRR, NDCG, faithfulness, and relevance metrics with continuous regression testing.

Multi-modal

Multi-Modal RAG

Text, tables, images, and charts — extract knowledge from complex mixed-content documents.

Our process

Corpus to production

Structured delivery from data audit through retrieval tuning to monitored production deployment.

01

Data audit & architecture

Corpus analysis, chunking strategy, vector DB selection, and eval set design.

Week 1–2
02

Ingestion & indexing

Build ingestion pipelines, embed, index, and validate retrieval on golden queries.

Week 2–4
03

Retrieval tuning & evals

Hybrid search, reranking, citation grounding, and RAGAS/DeepEval regression suites.

Week 4–8
04

Deploy & monitor

Production APIs, monitoring, cost controls, and continuous eval regression in prod.

Week 8–12
Technology

RAG stack

Vector stores, embeddings, LLMs, and orchestration — selected for your data patterns and infrastructure.

Layer 01

Vector DBs

Pinecone Weaviate Qdrant pgvector ChromaDB
Layer 02

Embeddings

OpenAI Ada-3 BGE-Large Cohere Embed Custom models
Layer 03

LLMs

GPT-4o Claude 3.5 Llama 3 Gemini
Layer 04

Orchestration

LangChain LlamaIndex Custom pipelines
Proof of work

RAG case studies

Production RAG systems with measurable accuracy and business impact.

Legal Document AI

LLM Contract Analysis Platform

Fine-tuned LLM + RAG system for contract clause extraction with 96% risk identification accuracy.

87.5%Time saved
96%Accuracy
Read case study →
Fintech Hybrid RAG

RAG Compliance Review System

Hybrid RAG processing 2,800+ regulatory documents with citation-grounded compliance gap detection.

78%Faster review
$900KAnnual savings
Read case study →
4–6 wkPrototype
HybridRetrieval
10+Years experience
200+Projects shipped
01What is RAG (Retrieval-Augmented Generation)?

RAG is an AI architecture that enhances LLM responses by retrieving relevant information from your proprietary data before generating answers. Instead of relying solely on the model's training data, RAG systems search your documents, databases, and knowledge bases to provide accurate, up-to-date, citation-backed responses grounded in your actual business data.

02How much does enterprise RAG implementation cost?

Enterprise RAG implementation typically costs $50,000–$200,000+ depending on data volume, number of data sources, accuracy requirements, and compliance needs. A basic RAG prototype can be delivered in 4–6 weeks for $30,000–$50,000. Production systems with hybrid search, re-ranking, and evaluation frameworks are at the higher end.

03What is the difference between RAG and fine-tuning?

RAG retrieves relevant context at query time from external data sources, while fine-tuning modifies the LLM's weights with your domain data. RAG is better for frequently changing data, factual accuracy with citations, and when you need to audit sources. Fine-tuning is better for style/tone adaptation and specialized reasoning. Many production systems use both. Read our detailed comparison.

04Which vector database should I use for RAG?

The best vector database depends on your requirements. Pinecone offers managed simplicity and scale. Weaviate provides hybrid search out-of-the-box. pgvector is ideal if you're already on PostgreSQL. Qdrant offers excellent filtering performance. We evaluate your data patterns, query needs, and infrastructure preferences to recommend the best fit.

Next step

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