Product Teams Adding AI Q&A Features
Ground your in-app assistant in your own docs, help centre, and data — so it gives accurate, on-brand answers instead of generic ones.
RAG Pipeline Development
We design and build retrieval-augmented generation (RAG) pipelines that ground AI responses in your own documents, knowledge bases, and data — so answers stay accurate, current, and traceable as your content grows.
Ingestion & chunking pipelinesVector & hybrid searchCitation-ready responses
Who we build for
Who We Build RAG Pipelines For
An AI assistant is only as good as what it can find. We build retrieval pipelines that connect your documents, wikis, and databases to your AI — so every answer is grounded in your actual content, with sources you can check.
Data-Rich Organisations
Turn dormant docs, tickets, and logs into knowledge bases your team can query in plain language — no more digging through folders.
Enterprises with Compliance Requirements
Citation-ready retrieval so every AI answer can be traced back to a source document — built for industries where audit trails matter.
Teams Already Using LLMs Who Need Better Accuracy
If your AI feature 'sounds right but is wrong', RAG is usually the fix. We diagnose and rebuild retrieval to close the gap.
Why Chayaniq for RAG
RAG Pipeline Development Benefits
Retrieval-augmented generation only works when the retrieval half is done right. Here is what we bring to make your AI's answers accurate and traceable.
Document Ingestion & Chunking
Pipelines that ingest PDFs, wikis, tickets, and databases — chunked and structured for high-quality retrieval.
Vector & Hybrid Search
Vector similarity combined with keyword search — so retrieval handles both 'find similar meaning' and 'find this exact term'.
Citation-Ready Responses
Every answer links back to its source documents — critical for trust, audits, and compliance.
Continuous Re-Indexing
As your content changes, the index updates automatically — so answers never go stale.
Retrieval Quality Evaluation
Eval suites measure retrieval accuracy and relevance — so you know the pipeline is actually working, not just running.
Vector Store Selection & Tuning
The right vector database for your scale and budget — pgvector, Pinecone, Weaviate, or Qdrant — tuned for your workload.
Industries we serve
RAG Pipelines Across Industries
What counts as a 'document' differs by industry — clinical notes, financial filings, engineering specs, policy wordings. We design retrieval pipelines around your industry's content.
Remote patient monitoring
- Real-time collection of wearables data
- Enhanced doctor-patient communication
- Comprehensive view of patient health data
- Data-driven report generation
- Seamless integration with existing systems
- Improved patient outcomes
Electronic medical records (EMR)
- Interoperability of medical data across departments
- HIPAA / GDPR compliant solutions
- Improved patient care
- Quick access to patient records, vitals, and lab results
- Enhanced security and privacy of patient health data
Document management
- Compliant documentation workflow
- Streamlined management of health information
- Integrations with existing systems for a 360° data view
- Secure file sharing and access management
Insurance verification
- Automated medical insurance eligibility
- Proactive identification of coverage issues
- API-ready and HIPAA-compliant
How we work
Our Process for RAG Pipeline Delivery
Retrieval quality is the foundation everything else depends on. Our process measures retrieval before tuning generation — so improvements are real, not anecdotal.
Discovery & Source Mapping
We catalogue your content sources — documents, wikis, tickets, databases — and identify access controls and update frequency.
Ingestion & Chunking Design
We design ingestion pipelines and chunking strategies tuned to your content types and query patterns.
Retrieval Architecture
Vector store selection, hybrid search, and re-ranking — chosen and configured for your scale and budget.
Retrieval Evaluation
We build eval suites measuring retrieval accuracy and relevance against real questions before tuning generation.
Deployment
Pipeline deployed with citation-ready responses and confidence thresholds for 'I don't know' handling.
Re-Indexing & Monitoring
Continuous re-indexing as content changes, with ongoing query analysis to catch drift.
How we build RAG pipelines
Retrieval-augmented generation only works when the retrieval half is done right. We design ingestion, indexing, and retrieval so responses stay accurate, current, and traceable.
Pipelines that ingest PDFs, wikis, tickets, and databases — chunked and structured for high-quality retrieval.
- Connectors for documents, wikis, tickets, and databases
- Chunking strategies tuned to your content types
- Metadata enrichment for filtering and access control
Our stack
Our RAG Pipeline Technology Stack
Ingestion, retrieval, and evaluation tooling — selected to keep answers grounded, current, and traceable as your content grows.
Models & Providers
- Anthropic Claude
- OpenAI GPT
- Google Gemini
- Open-source embeddings
Vector Stores
- pgvector
- Pinecone
- Weaviate
- Qdrant
Hybrid Search
- Elasticsearch
- OpenSearch
- BM25 + vector re-ranking
AI Frameworks
- LangChain
- LlamaIndex
- Custom pipelines
Evaluation
- LangSmith
- Promptfoo
- Custom retrieval eval suites
Compute & Serving
- AWS Lambda
- SageMaker
- Docker
Perspectives
Ideas that pair with RAG Pipeline Development
Short reads from how we ship—architecture, product, and ops. Same themes as this service, different angles.
March 2026
Vector Databases Compared: pgvector vs Pinecone vs Weaviate vs Qdrant
Cost, latency, and operational overhead — a practical comparison for teams choosing a vector store for production RAG.
Continue readingFebruary 2026
Why Your RAG Pipeline Gives Wrong Answers (And How to Fix Retrieval)
Most 'hallucination' problems are actually retrieval problems. Chunking, hybrid search, and re-ranking fixes that move the needle.
Continue reading
