Retrieval-Augmented Generation
Multi-stage RAG pipelines — parsing, chunking, indexing, hybrid retrieval, re-ranking and grounded generation — with evaluation built in from the first sprint, not bolted on later.
Raipur, Chhattisgarh · Applied AI Lab
AI & Retrieval
AI & Retrieval
Engineering
We design, build and teach production-grade retrieval systems — RAG pipelines, embeddings, vector databases, semantic search, re-ranking, LLM orchestration, agents, guardrails and evaluation. Engineered in Raipur for Chhattisgarh and Central India.
The Discipline
What is AI & retrieval engineering?
AI & retrieval engineering is the applied discipline of connecting large language models to an organisation's real knowledge, tools and workflows so that AI systems produce grounded, measurable, trustworthy output instead of hallucination.
It spans retrieval-augmented generation, embeddings, vector databases, semantic search, re-ranking, LLM orchestration, prompt engineering, agent and tool design, system-prompt design, persona design, guardrails and systematic evaluation. For businesses in Raipur, Bhilai and across Chhattisgarh, this is now an operational requirement rather than a future concern.
₹500 Cr
Chhattisgarh AI Mission outlay
100
AI Data Labs planned statewide
2B+
Monthly users of Google AI Overviews
800M
Weekly ChatGPT users
Capabilities
Nine layers of a production retrieval stack
Every production AI system is a retrieval system underneath. These are the nine layers we engineer, measure and hand over.
Embeddings
Dense vector representations engineered for your domain — model selection, chunking strategy, normalisation and multilingual coverage for Hindi and Chhattisgarhi corpora.
Vector Databases
Engine selection and tuning across pgvector, Qdrant, Weaviate, Elasticsearch and Pinecone — indexing strategy, quantisation, filtered search and cost modelling at scale.
Semantic Search
Hybrid retrieval that fuses dense vector similarity with BM25 lexical scoring via reciprocal rank fusion — the production default for precision and recall together.
Re-ranking
Cross-encoder and LLM-as-reranker stages that take the top 50 candidates down to the final 3–5 passages worth sending to the model.
LLM Orchestration
Query planning, retrieval sufficiency checks, model routing and fallback logic — with observability over every non-deterministic component in the chain.
Prompt & System-Prompt Design
Prompts treated as policy documents — identity, constraints, scope, escalation triggers and output schema — plus persona design calibrated to the agent's real authority.
Agent & Tool Design
Function-calling schemas, identity management, tool sandboxing, circuit breakers and semantic version governance for agents that must stay inside policy.
Guardrails & Evaluation
Context precision, context recall, answer relevance and faithfulness measurement, plus continuous online evaluation of accuracy, relevance and toxicity in live traffic.
Architecture
The eight-stage production RAG pipeline
A production RAG system is not a single model call — it is an eight-stage pipeline, and roughly 80% of RAG failures trace back to the ingestion and chunking layer, not the LLM. Most teams discover this only after weeks of fruitless prompt tuning.
Document Parsing
Clean text, tables and metadata extracted from PDFs, DOCX, HTML, scans and databases.
Chunking
400–512 token chunks with 10–20% overlap, preserving section structure and provenance metadata.
Embedding
Domain-appropriate dense vectors, including multilingual models for Hindi and Chhattisgarhi content.
Hybrid Retrieval
Dense vector similarity fused with BM25 lexical search using reciprocal rank fusion.
Re-ranking
Cross-encoder scoring narrows the top 50 candidates to the final 3–5 passages.
Context & Generation
Grounded context assembled and passed to an orchestrated LLM call under a structured system prompt.
Guardrails
Input filtering, output filtering, escalation triggers and tool sandboxing before delivery.
Evaluation
Context precision, context recall, answer relevance and faithfulness measured continuously.
Benchmarks
Which vector database should you actually run?
In a controlled 14-case benchmark spanning ingestion, semantic search, filtered search, hybrid search, batch throughput, concurrency, scaling and mutations, pgvector won all 7 local deployment categories and Qdrant won 6 of 7 cloud categories.
| Database | Tier | Ingest (rows/s) | Semantic p50 | Peak QPS |
|---|---|---|---|---|
| pgvector | Local | 1,943.7 | 5.71 ms | 1,212.3 |
| Qdrant | Cloud | 1,825.2 | 14.30 ms | 80.4 |
| Weaviate | Cloud | 1,574.6 | 44.38 ms | 85.8 |
| Pinecone | Cloud | 256.6 | 300.23 ms | 40.3 |
Start with pgvector
If your team already runs PostgreSQL, pgvector delivers competitive performance across ingestion, search and concurrency without adding infrastructure or a new operational surface.
Move to Qdrant at cloud scale
For managed cloud deployments, Qdrant leads the majority of benchmark categories and offers the strongest cost profile once binary quantisation is applied.
Model the cost early
Self-hosted Qdrant and Weaviate undercut managed alternatives by 3–10× at scale. The engine decision is a cost decision as much as a latency decision.
Control
System prompts are policy documents, not instructions
A production system prompt is best understood as a constitution: it defines identity, behavioural boundaries, decision rules and failure modes. Guardrails are then measured as a system property — and they must be measured across multi-turn conversations, not single queries.
Identity & Role
Defines who the agent is and what it exists to do — preventing role confusion and scope drift.
Behavioural Constraints
Explicit "will not" rules. Absolute language is essential; conditional language creates loopholes.
Scope Limitations
Valid topics and decision boundaries that stop out-of-scope responses before they are generated.
Escalation Triggers
Conditions that halt processing and require human review — the safety valve of the system.
Output Format
Structured response schemas that make every answer validatable, auditable and machine-checkable.
89.5% → 35.5%
Guardrail pass rates across five enterprise systems and 1,200 test cases fell from 89.5% to 35.5% during multi-turn conversations — with no adversarial attack involved.
Guardrails tested only on single-turn queries are not guardrails. They are a demo.
Raipur AI Hub · Chhattisgarh
Chhattisgarh’s Self-Sufficient AI Ecosystem
Chhattisgarh is building its own AI infrastructure, with Raipur at the centre—creating a self-sufficient AI ecosystem driven by local talent, cost advantages, and direct access to state AI programmes. Raipur offers a strong independent foundation for AI growth while complementing India’s established tech hubs.
Chhattisgarh AI Infrastructure
Anchored by the state's ₹500 crore initiative, establishing 100 AI Data Labs, five AI Centres of Excellence, support for 300+ AI startups, and 50+ AI-powered citizen services.
Local AI Talent & Ecosystem
IIIT Naya Raipur and regional tech institutions produce retrieval-literate engineers, data architects, and ML specialists—creating a sustainable local pipeline for applied AI systems.
Cost Advantage for AI Businesses
Raipur delivers 40–60% operational and development cost advantages compared to tier-1 metro hubs, combined with materially lower attrition volatility.
Central India AI Corridor
Raipur builds its own independent applied AI capabilities while collaborating seamlessly with major tech hubs like Bengaluru and Hyderabad, expanding India’s national AI corridor.
Local Advantage
Built in Raipur. Benchmarked against the world.
We are an applied AI lab, not a reseller. Every architecture we recommend is one we have benchmarked, deployed and evaluated ourselves.
Services
Technology, talent and consulting
Three engagements, one standard: everything we ship comes with evaluation harnesses, guardrails and observability from day one.
Applied AI Development
We design and build production retrieval systems: RAG pipelines, hybrid semantic search, vector database implementations, re-ranking architectures and agentic workflows — each delivered with evaluation harnesses, guardrails and observability.
AI Talent Development
We train engineering teams and individuals across the full retrieval engineering stack — embeddings, vector databases, orchestration, prompt architecture and production evaluation. Project-based: participants leave with deployed systems, not certificates.
AI Strategy & Consulting
AI readiness assessment, retrieval architecture selection, compliance-aware deployment design, governance frameworks and internal evaluation capability building — including support for organisations engaging with the Chhattisgarh AI Mission.
FAQ
Frequently asked questions
Direct answers to the questions engineering and business leaders ask us most often.
What makes Raipur a strong AI destination?
Raipur anchors Chhattisgarh’s AI infrastructure, offering local talent, cost advantages, and direct access to state AI programmes, creating a self-sufficient AI ecosystem.
How does Raipur complement India’s major tech hubs?
Raipur builds its own AI strengths and can collaborate with established hubs like Bengaluru and Hyderabad, adding a central India AI corridor.
What is retrieval-augmented generation (RAG)?
RAG is an architecture that connects a large language model to external knowledge sources. Instead of relying only on training data, the system retrieves relevant documents at query time and uses them to ground its response — reducing hallucination and improving factual accuracy.
Which vector database is best for production RAG in 2026?
For local deployments, pgvector delivers the strongest overall performance across ingestion, search and concurrency, winning all 7 local categories in a 14-case benchmark. For cloud-managed deployments, Qdrant leads with 6 of 7 cloud category wins.
How do you stop an AI agent from going off-script?
Use a five-part system prompt framework — identity, behavioural constraints, scope limitations, escalation triggers and output format. Then test guardrails across multi-turn conversations, because pass rates drop from 89.5% to 35.5% in extended interactions without any adversarial attack.
What is the Chhattisgarh AI Mission?
A ₹500 crore, five-year state initiative establishing 100 AI Data Labs, five Centres of Excellence, support for 300+ AI startups, training for six lakh students and 1.5 lakh government employees, and more than 50 AI-powered government services.
Can RAG systems work with Hindi and Chhattisgarhi content?
Yes, but embedding model selection is critical. General-purpose models often underperform on regional-language content. Multilingual embedding models or domain-specific fine-tuning are required for production-quality retrieval.
Why work with a Raipur-based AI engineering partner?
Raipur offers a growing AI talent pipeline backed by the Chhattisgarh AI Mission and IIIT Naya Raipur, 40–60% cost savings versus metro teams, proximity to state AI programmes, and lower attrition — with the same production engineering standards.
Contact
Build with us in Raipur
The applied AI advantage is not about having the largest model. It is about having the right retrieval architecture, the right evaluation discipline and the right talent to build systems that hold up under real-world conditions.
Tell us what you are building — a production RAG system, a vector database implementation, a regional-language knowledge assistant, an evaluation harness, or an AI capability programme for your team.
Pure Applied Labs
Raipur, Chhattisgarh 492001, India
contact@pureappliedlabs.com
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