RAG Development & Evaluation
Build production RAG systems with retrieval evaluation, chunking optimization and cost monitoring. From proof-of-concept to scale.
What is RAG Development & Evaluation?
Retrieval-Augmented Generation (RAG) is the most practical way to put LLMs to work on your own data. But most RAG implementations fail in production — poor retrieval accuracy, runaway costs, hallucinated answers and no way to measure what’s working.
We build production RAG systems that deliver accurate, grounded answers at scale. Our approach includes systematic evaluation pipelines, chunking optimization, query rewriting and cost monitoring from day one.
Whether you’re building an internal knowledge assistant, a customer-facing Q&A system or a compliance document search — we’ve shipped these systems and know where the pitfalls are.
Why build with us
Evaluation-First Approach
We build evaluation pipelines before the RAG system. Every change is measured against golden datasets so you always know if quality improved or regressed.
Chunking Optimization
We test semantic, recursive and fixed-size chunking strategies against your data. The right chunking strategy can improve retrieval accuracy by 40%.
Cost Monitoring Built-In
Semantic caching, model routing and token optimization from day one. We track cost per query and alert before budgets are exceeded.
Hallucination Guardrails
Every response is validated against source documents. Low-confidence answers are flagged for human review, not surfaced to users.
What we build with RAG Development & Evaluation
Vector Database Setup
Pinecone, Weaviate, Qdrant or pgvector — we pick the right one for your scale and deploy it.
Query Rewriting
User queries are rewritten and expanded for better retrieval. Multi-query and HyDE strategies tested against your data.
Reranking Pipeline
Cross-encoder reranking of retrieved documents ensures the most relevant context reaches the LLM.
Evaluation Dashboard
RAGAS metrics (faithfulness, relevance, recall) tracked over time with regression alerts on every deployment.
How we deliver
Data Audit
Week 1We analyze your documents, identify chunking strategies and build evaluation datasets.
Pipeline Build
Week 2-3Build the ingestion, embedding and retrieval pipeline with cost monitoring.
Quality Tuning
Week 4-5Optimize retrieval, add reranking, tune prompts and measure accuracy improvements.
Production Deploy
Week 6Deploy with monitoring, alerting, caching and a 30-day warranty period.
How you can work with us
Fixed-Price Project
Defined scope, fixed timeline, guaranteed deliverables. Best for MVPs and well-scoped features.
- Full scope defined upfront
- Milestone-based payments
- 8-16 week delivery
- 30-day warranty
Dedicated Developer
A senior developer assigned to your team full-time. Minimum 1 month engagement.
- 160 hours/month
- Daily standups
- Weekly demos
- Flexible scaling
Team Augmentation
A full team of developers, designers and architects embedded in your organization.
- Cross-functional team
- Quarterly engagement
- Dedicated PM
- Architecture oversight
Our RAG Development & Evaluation technology stack
LLMs
Vector DB
Framework
Evaluation
Frequently asked questions
RAG (Retrieval-Augmented Generation) combines a search system with an LLM. You need it when you want AI to answer questions about your own data — documents, knowledge bases, or databases — without fine-tuning a model.
We use a combination of retrieval metrics (recall@k, MRR), generation metrics (faithfulness, relevance via RAGAS framework), and business metrics (cost per query, latency, user satisfaction). We build evaluation into the CI/CD pipeline.
We have production experience with Pinecone, Weaviate, Qdrant, pgvector and ChromaDB. The choice depends on your scale, latency requirements and existing infrastructure.
We implement semantic caching (cache similar queries), intelligent chunking (reduce token usage), model routing (use cheaper models for simple queries), and monitoring dashboards so costs never surprise you.