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Enterprise AI · India's LLM Experts

LLM & RAG Services
Built for Enterprise

Deploy production-grade Large Language Models and Retrieval-Augmented Generation pipelines that answer from your data — not hallucinations. From GPT-4o and Claude to open-source LLMs, we build, fine-tune, and manage AI systems at enterprise scale — all within India's data residency requirements.

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Data Never Leaves Your Tenant

DPDP Act Compliant

Azure Certified Partner

50+

AI Projects Delivered

95%

Accuracy on Private Docs

4–8 wk

Typical MVP Delivery

10+

LLM Platforms Supported

RAG Pipeline — Live Query

Running

What are our Q1 2026 GST liabilities across all product lines?

Query Embedding

text-embedding-3-large

✓ 12ms

Vector Retrieval

Azure AI Search · top-k=8

✓ 34ms

Re-ranking & Context

Semantic re-ranker · 3 chunks

✓ 18ms

LLM Generation

GPT-4o · grounded response

✓ 1.2s

Answer generated with 3 source citations. Total latency: 1.26s · 98% confidence · Sources: GST_Q1_Report.xlsx, Invoice_Summary.pdf, ProductLines_Master.csv

RAG

On Your Private Docs, SharePoint & Databases

100%

Data Privacy — Never Sent to Public LLM Training

10+

LLM Providers: OpenAI, Azure, Anthropic, Mistral, LLaMA

≤8 wk

From Kickoff to Production RAG MVP

RAG Pipeline Development LLM Fine-Tuning Vector Database LangChain LlamaIndex Azure OpenAI Semantic Kernel Pinecone Weaviate Azure AI Search pgvector Prompt Engineering LLMOps GPT-4o Claude 3.5 Llama 3 Mistral Embedding Models RAG Pipeline Development LLM Fine-Tuning Vector Database LangChain LlamaIndex Azure OpenAI Semantic Kernel Pinecone Weaviate Azure AI Search pgvector Prompt Engineering LLMOps GPT-4o Claude 3.5 Llama 3 Mistral Embedding Models

Our LLM & RAG Services

End-to-End LLM & RAG Implementation

From architecture design to production deployment and ongoing LLMOps — our AI engineers build Retrieval-Augmented Generation systems and fine-tuned LLMs that deliver accurate, grounded answers from your enterprise data.

RAG Pipeline Development

End-to-end Retrieval-Augmented Generation pipelines — document ingestion, chunking strategies, embedding generation, vector store indexing, semantic retrieval, re-ranking, and grounded LLM response generation. Built on LangChain, LlamaIndex, or Semantic Kernel.

LangChain LlamaIndex Vector Search

Custom LLM Fine-Tuning

Fine-tune GPT-3.5 Turbo, Mistral, LLaMA 3, and other open-source LLMs on your domain-specific data — legal documents, technical manuals, product catalogues, or customer service logs. Includes dataset curation, RLHF alignment, and evaluation.

GPT Fine-Tune LoRA / QLoRA RLHF

Vector Database Integration

Design and implement vector stores tailored to your scale and cloud — Azure AI Search, Pinecone, Weaviate, Qdrant, Chroma, or pgvector on PostgreSQL. Hybrid search combining dense vectors with BM25 keyword search for maximum retrieval accuracy.

Pinecone Weaviate pgvector

Enterprise AI Chatbot & Copilot

Intelligent chatbots and copilots powered by RAG — answering questions from your SharePoint, Confluence, PDFs, ERP data, and databases in real time. Deployed inside Teams, Dynamics 365, your website, or as a standalone app with role-based access control.

Teams Bot D365 Copilot Multi-turn

Document Intelligence & Extraction

LLM-powered extraction of structured data from unstructured documents — invoices, contracts, purchase orders, GST filings, lab reports, and forms. Combines Azure Document Intelligence OCR with GPT-4o for near-human extraction accuracy.

OCR + LLM JSON Extraction Azure DI

LLMOps & AI Observability

Production-grade LLMOps pipelines — prompt version control, A/B testing, latency and cost monitoring, hallucination detection, guardrails, and continuous evaluation using Promptflow, LangSmith, or custom dashboards on Azure Monitor.

Promptflow LangSmith Guardrails

Full Capability Coverage

Everything You Need for Production AI

We cover the complete LLM and RAG stack — from data pipelines and model selection to deployment, monitoring, and continuous improvement.

🔍 Core RAG

Advanced Retrieval Strategies

Beyond naive RAG — HyDE, multi-query, parent-child chunking, contextual compression, and agentic RAG with tool use for complex, multi-step queries.

Hybrid dense + sparse search
Cross-encoder re-ranking
Self-querying retrievers
Graph RAG for connected data

🏗️ Architecture

LLM Architecture Design

We select the right architecture for your use case — RAG vs fine-tuning vs prompt engineering — and design for latency, cost, and accuracy targets before writing a single line of code.

Multi-agent Systems Agentic Workflows Long-term Memory Tool Calling Chain-of-Thought

🔒 Security

Private & Secure Deployment

Your data never leaves your environment. Deploy on Azure with VNet isolation, private endpoints, and row-level security filters in the retrieval layer.

🌐 Multilingual

Hindi & Regional Language AI

LLMs configured for Hindi, Tamil, Telugu, and other Indian languages — multilingual embeddings, transliteration handling, and cross-lingual RAG retrieval.

⚡ Performance

Latency & Cost Optimisation

Semantic caching, response streaming, chunking optimisation, model cascading (small LLM first, large LLM only when needed), and token budget management to cut costs by up to 70%.

🤖 Models

LLM Provider Coverage

We are model-agnostic — choose the best LLM for your budget and requirements.

OpenAI GPT-4o, GPT-4o-mini, o1
Anthropic Claude 3.5 Sonnet & Haiku
Meta LLaMA 3.1 / 3.3 (self-hosted)
Mistral Large & Mistral Nemo
Google Gemini 1.5 Pro
Azure AI model catalogue (600+ models)

📊 Evaluation

RAG Evaluation & Quality Assurance

Rigorous evaluation using RAGAS — measuring faithfulness, answer relevance, context precision, and context recall before going live. Continuous evaluation in production with automated regression alerts.

RAGAS framework scoring
Human-in-the-loop review
Hallucination detection guardrails
Automated prompt regression tests

Delivery Framework

Our LLM & RAG Delivery Process

A proven 5-phase process from data audit to production rollout — typically 4–8 weeks for most enterprise RAG projects.

Phase 1 · Week 1

Discovery & Data Audit

3–5 days

Identify use cases, audit your data sources (SharePoint, PDFs, databases, ERP), define success metrics, select LLM and vector store, and design the RAG architecture.

Use Case MappingData AuditArchitecture Design

Phase 2 · Week 1–2

Data Ingestion & Indexing

5–7 days

Build document loaders, chunking pipelines, embedding generation, and vector store indexing. Set up incremental sync so the index stays fresh automatically.

ETL PipelineChunking StrategyEmbeddings

Phase 3 · Week 2–4

RAG Pipeline & LLM Integration

10–14 days

Build retrieval chains, re-rankers, prompt templates, guardrails, and the LLM integration. Iterative prompt engineering and RAGAS evaluation to hit accuracy targets.

LangChainPrompt EngineeringRAGAS Eval

Phase 4 · Week 4–6

UI Integration & UAT

7–10 days

Integrate with Teams, your website, Dynamics 365, or custom front-end. User acceptance testing with real queries from your team and stakeholder sign-off.

Teams BotAPI IntegrationUAT

Phase 5 · Week 6–8

Production Deployment & LLMOps

5–7 days

Go-live on Azure with CI/CD, LLMOps dashboards, cost and latency monitoring, hallucination alerts, and a 30-day hypercare support period.

Azure DeploymentLLMOpsMonitoring

Industry Use Cases

LLM & RAG for Every Industry

Real-world RAG and LLM applications SchwettmannTech has delivered across Indian enterprises.

🏭

Manufacturing SOPs & QA

🏦

Banking Compliance & KYC

🏥

Healthcare Clinical Notes

⚖️

Legal Document Review

🛒

Retail Product Catalogues

📞

Telecom Customer Support

🚗

Automotive Technical Manuals

🎓

EdTech Personalised Tutoring

Technology Stack

Frameworks & Platforms We Use

LangChain LlamaIndex Semantic Kernel Azure OpenAI Azure AI Search Pinecone Weaviate pgvector LangSmith Promptflow Qdrant Python FastAPI Docker / AKS RAGAS

Business Impact

Proven Results from LLM & RAG Projects

Measurable outcomes from SchwettmannTech's LLM and RAG engagements across Indian enterprises — manufacturing, banking, healthcare, and more.

90%

Reduction in time spent searching internal knowledge bases using RAG-powered assistants

5×

Faster contract review using LLM extraction — from 2 hours to 24 minutes per document

₹80L+

Annual cost savings from document automation and LLM-powered data entry elimination

97%

Answer accuracy on domain-specific queries after fine-tuning and RAG pipeline optimisation

RAG vs Fine-Tuning vs Prompt Engineering

Which AI Approach Do You Need?

We help you choose the right strategy — or the right combination — before investing in development. Here's our practical guidance:

Criteria	RAG (Retrieval-Augmented)	LLM Fine-Tuning	Prompt Engineering Only
Best for	Dynamic, frequently updated data (docs, FAQs, databases)	Consistent style, domain vocabulary, or classification tasks	Simple task reformulation with a capable base model
Data privacy	✓ Data stays in your environment	✓ Training on private data, hosted privately	✗ Queries sent to LLM provider
Up-to-date answers	✓ Index refreshes automatically	✗ Requires re-training for new data	✗ Limited to model knowledge cutoff
Source citations	✓ Grounded answers with citations	✗ No retrieval — no citations	✗ No retrieval
Implementation cost	Medium (4–8 weeks)	High (8–16 weeks + data labelling)	Low (1–2 weeks)
Hallucination risk	✓ Low — grounded in retrieved context	Medium — depends on training quality	✗ Higher — relies on model memory
SchwettmannTech recommendation	Best for most enterprise use cases	Combine with RAG for best results	Good starting point, not production-ready alone

Client Stories

What Our AI Clients Say

★★★★★

"SchwettmannTech built a RAG system on top of our 10,000+ SOP documents. Our engineers now find answers in seconds instead of hours. The accuracy is remarkable — we've stopped second-guessing the AI's answers."

Rajesh Kumar

Head of Operations, Precision Parts Manufacturer, Pune

★★★★★

"We needed a loan document extraction system that could handle Hindi and English mixed PDFs. The LLM + Azure Document Intelligence solution SchwettmannTech built processes 500 applications a day with 98% field accuracy."

Priya Menon

VP Technology, Regional NBFC, Chennai

★★★★★

"The internal knowledge chatbot SchwettmannTech deployed in our Teams environment has transformed onboarding. New hires get accurate answers from 5 years of company documentation on day one. Exceptional delivery team."

Anita Sharma

CHRO, SaaS Company, Hyderabad

FAQ

Common Questions About LLM & RAG

Have more questions? Our AI engineers are happy to discuss your specific use case.

RAG is an AI architecture that combines a retrieval system (vector search) with a Large Language Model (LLM). Instead of relying on the LLM's training data — which may be outdated or never included your proprietary information — RAG first retrieves the most relevant chunks of your documents, then passes them as context to the LLM to generate a grounded, accurate answer. Your business needs RAG if employees spend time searching documents, if customer-facing teams struggle to find product or policy information quickly, or if you need an AI assistant that gives accurate, up-to-date answers from your own data.

No. We deploy LLMs within your private Azure tenant using Azure OpenAI Service — Microsoft contractually guarantees that your data and prompts are never used to train OpenAI's foundational models. Your documents are stored in your own vector database (Azure AI Search or Pinecone on your account) and never leave your cloud environment. For sensitive use cases, we can deploy fully air-gapped open-source LLMs (LLaMA 3, Mistral) on your own servers.

Virtually any text-based source — PDFs, Word documents, PowerPoint presentations, Excel spreadsheets, SharePoint sites, Confluence wikis, web pages, SQL databases, Dataverse tables, and real-time APIs. We build custom document loaders for your specific formats. We can also handle scanned documents and images using Azure Document Intelligence OCR before indexing.

RAG retrieves context at query time from a live index — so answers are always based on current data and citations are available. Fine-tuning bakes knowledge into the model's weights, which is better for learning domain-specific writing style, vocabulary, or classification patterns — but doesn't update automatically when your data changes. We typically recommend RAG for most enterprise knowledge-base and Q&A use cases, and fine-tuning for structured output tasks, classification, or specialised generation. Often the best results come from combining both.

A focused MVP — ingesting your documents, building the RAG pipeline, and deploying a chat interface — typically takes 4–6 weeks. More complex projects involving multiple data sources, fine-tuning, multi-agent workflows, or integration with Dynamics 365 and Teams take 8–12 weeks. We always start with a 2-week proof of concept so you can see the system working on your actual data before committing to full development.

Yes. Modern LLMs like GPT-4o and multilingual embedding models like text-multilingual-embedding-3 handle Hindi, Tamil, Telugu, Kannada, Marathi, Bengali, and other Indian languages natively. We configure the retrieval and generation pipeline for multilingual content, including handling mixed Hindi-English (Hinglish) queries and transliterated text. We have built production RAG systems for Indian NBFCs and manufacturers where both documents and user queries are in Hindi.

LLMOps (LLM Operations) is the practice of running LLM-powered applications reliably in production — monitoring answer quality, tracking latency and token costs, detecting hallucinations, running A/B tests on prompts, and updating the knowledge index. Without LLMOps, an initially well-performing RAG system will degrade over time as data changes and usage patterns evolve. We set up LLMOps dashboards using Azure Monitor, LangSmith, or Promptflow as part of every production deployment.

Ready to Build Your LLM & RAG System?

Book a free 45-minute discovery call. We'll audit your data sources, identify the best use cases, and give you a realistic scope and timeline — no commitment required.

Request a Proposal

LLM & RAG ServicesBuilt for Enterprise

End-to-End LLM & RAG Implementation

Everything You Need for Production AI

Advanced Retrieval Strategies

LLM Architecture Design

Private & Secure Deployment

Hindi & Regional Language AI

Latency & Cost Optimisation

LLM Provider Coverage

RAG Evaluation & Quality Assurance

Our LLM & RAG Delivery Process

LLM & RAG for Every Industry

Frameworks & Platforms We Use

Proven Results from LLM & RAG Projects

Which AI Approach Do You Need?

What Our AI Clients Say

Common Questions About LLM & RAG

Ready to Build Your LLM & RAG System?

LLM & RAG Services
Built for Enterprise