Capability
RAG & Knowledge Retrieval across India
Retrieval-augmented generation over your own documents, with citations, access control and measured answer quality.
- Industries
- 30
- Stack options
- 10
- Typical first release
- 6 weeks
What rag & knowledge retrieval means when we build it
Your documents do not arrive as clean markdown. They are scanned PDFs, merged cells, ten-year-old templates. The ingestion pipeline is most of the work, and we build it for the corpus you actually have.
Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.
What is included
- Ingestion pipeline for your real document formats
- Chunking and embedding strategy tuned to your corpus
- Hybrid keyword plus vector retrieval with reranking
- Citations on every answer, traceable to the source page
- Permission-aware retrieval that respects existing access rules
- Retrieval quality benchmarked against a labelled question set
Who this is for
We usually work with knowledge managers, CIOs, support leaders and research teams, the people who own the outcome rather than the tooling decision.
RAG & Knowledge Retrieval by industry
Each sector changes the constraints, regulation, systems of record, and what a wrong answer costs.
- RAG & Knowledge Retrieval for Healthcare & HospitalsDPDP Act 2023
- RAG & Knowledge Retrieval for Pharmaceuticals & Life SciencesCDSCO
- RAG & Knowledge Retrieval for Financial ServicesRBI guidelines
- RAG & Knowledge Retrieval for InsuranceIRDAI regulations
- RAG & Knowledge Retrieval for Legal ServicesBar Council rules
- RAG & Knowledge Retrieval for ManufacturingISO 9001
- RAG & Knowledge Retrieval for Logistics & Supply Chaine-way bill compliance
- RAG & Knowledge Retrieval for Retailconsumer protection rules
- RAG & Knowledge Retrieval for Education & EdTechDPDP Act 2023
- RAG & Knowledge Retrieval for Government & Public SectorDPDP Act 2023
- RAG & Knowledge Retrieval for Energy & UtilitiesCEA regulations
- RAG & Knowledge Retrieval for SaaS & TechnologySOC 2
- RAG & Knowledge Retrieval for Media & Entertainmentcopyright law
- RAG & Knowledge Retrieval for Real Estate & Construction TechRERA compliance
- RAG & Knowledge Retrieval for TelecommunicationsTRAI regulations
- RAG & Knowledge Retrieval for Agriculture & AgritechFSSAI standards
- RAG & Knowledge Retrieval for HospitalityFSSAI for food service
- RAG & Knowledge Retrieval for AutomotiveAIS standards
- RAG & Knowledge Retrieval for Construction & Infrastructurebuilding codes
- RAG & Knowledge Retrieval for Professional Servicesprofessional body standards
- RAG & Knowledge Retrieval for Nonprofit & DevelopmentFCRA compliance
- RAG & Knowledge Retrieval for Travel & Tourismtourism ministry guidelines
- RAG & Knowledge Retrieval for BankingRBI master directions
- RAG & Knowledge Retrieval for E-commerceconsumer protection e-commerce rules
- RAG & Knowledge Retrieval for Recruitment & HR Techlabour laws
- RAG & Knowledge Retrieval for Mining & MetalsDGMS safety regulations
- RAG & Knowledge Retrieval for Textiles & Apparelexport documentation requirements
- RAG & Knowledge Retrieval for Chemicals & Process IndustryPESO licensing
- RAG & Knowledge Retrieval for AviationDGCA regulations
- RAG & Knowledge Retrieval for Defence & Aerospacesecurity clearance requirements
RAG & Knowledge Retrieval, stack options
We pick per workload. Each page states the honest trade-off.
- RAG & Knowledge Retrieval with Claudemodel
- RAG & Knowledge Retrieval with OpenAI GPTmodel
- RAG & Knowledge Retrieval with pgvectordata
- RAG & Knowledge Retrieval with Pineconedata
- RAG & Knowledge Retrieval with PostgreSQLdata
- RAG & Knowledge Retrieval with Supabaseplatform
- RAG & Knowledge Retrieval with Pythonframework
- RAG & Knowledge Retrieval with TypeScriptframework
- RAG & Knowledge Retrieval with Elasticsearchdata
- RAG & Knowledge Retrieval with Azure OpenAIplatform
RAG & Knowledge Retrieval across India
Delivered remotely from our hubs, the city page tells you the nearest one and the realistic kickoff time.
Questions we get asked
RAG or fine-tuning?
RAG for knowledge that changes and must be cited; fine-tuning for style, format and task behaviour. Most production systems use RAG for the facts and light fine-tuning or few-shot prompting for the form.
How accurate will it be?
We build a labelled question set from your domain and report retrieval precision and answer accuracy against it. That number is the deliverable. We do not ship a system whose quality nobody has measured.
Can it respect our existing permissions?
Yes. Retrieval is filtered by the user's actual entitlements, so the assistant can never surface a document the user could not already open.
Considering rag & knowledge retrieval?
Tell us the workflow and the constraint. We will tell you honestly whether it is worth building.
Or email bd@dtrasglobal.com · call +91 74118 77878