Delhi NCR · Generative AI

Generative AI
Development
Company Delhi NCR

Q: What generative AI development services does XPndAI offer in Delhi NCR?

XPndAI builds six generative AI service lines for Delhi NCR enterprises: (1) RAG systems — retrieval-augmented generation over your internal documents, policies, and contracts with source citations; (2) LLM application development — custom GPT-4o and Claude applications for customer service, internal copilots, and email drafting; (3) AI content automation — bulk content generation engines for e-commerce, marketing, and reporting; (4) Generative AI for BFSI — credit memo generation, regulatory reporting drafting, loan appraisal summaries; (5) AI copilots — embedded GenAI assistants in your existing ERP, CRM, or web portal; (6) Custom LLM fine-tuning — domain-adapted models fine-tuned on your industry data for higher accuracy on your specific tasks.

Q: How much does generative AI development cost in Delhi NCR?

Generative AI project costs in Delhi NCR depend on scope. A RAG system or single-use-case LLM application typically costs $12K–$30K (6–10 weeks). A multi-module GenAI platform with ERP integrations and custom fine-tuning costs $40K–$100K (14–20 weeks). Enterprise custom LLM deployment with on-premise infrastructure costs $100K–$250K+. All projects are fixed-scope with milestone payments. Working prototype delivered in 2 weeks before full commitment.

We build production generative AI systems for Delhi NCR enterprises — RAG over your documents, custom LLM applications, AI copilots in your ERP, and content automation pipelines. Based locally. Working prototype in 2 weeks.

GPT-4oClaude SonnetRAG SystemsLLM Fine-TuningAI CopilotsContent Automation

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Free Generative AI Consultation

Tell us what you want to build — we'll design the generative AI architecture and estimate it for free within 24 hours.

Gen AI Services

Generative AI Development Services for Delhi NCR

From a RAG system over your internal knowledge base to a custom LLM application — we build it, deploy it on AWS Mumbai, and support it in production.

📚

RAG Systems

Ask-your-documents AI over your internal knowledge base — policies, contracts, manuals, compliance documents — with source citation in every answer. No hallucinations on your own data.

PDF, Word, Excel, Confluence, SharePoint
pgvector + Pinecone hybrid search
Hindi document support
WhatsApp / Slack / web chat interface
Department-level access control

💬

LLM Applications

Custom applications built on GPT-4o or Claude — customer service chatbots, internal copilots, email drafting tools, call summary generators, contract review assistants.

OpenAI GPT-4o, Anthropic Claude
Meta Llama 3 (on-premise option)
Guardrails + content moderation
Multi-turn conversation memory
WhatsApp / web / Slack deployment

🖥️

AI Copilots

Embed a generative AI assistant directly inside your ERP, CRM, or web portal — search, summarise, draft, and act on data without leaving the tool your team already uses.

SAP India / Tally ERP copilot
Salesforce / Zoho CRM copilot
Browser extension or embedded widget
Tool-use: read and write ERP data
Role-based prompt context

✍️

Content Automation

Bulk content generation pipelines — product descriptions, regulatory reports, financial summaries, customer emails — generated at scale with your brand voice and data.

E-commerce product description engine
Financial report narrative generation
Customer email personalisation at scale
Multi-language (Hindi + English)
Human review + approval queue

🔬

LLM Fine-Tuning

Domain-adapt a foundation model (GPT-4o, Llama 3, Mistral) on your proprietary data — for higher accuracy on industry-specific tasks your base model struggles with.

LoRA / QLoRA fine-tuning (Llama 3, Mistral)
OpenAI fine-tuning API for GPT-4o Mini
Training data curation + annotation
Evaluation benchmarking before/after
On-premise deployment option

🏦

Gen AI for BFSI

Generative AI built for Delhi NCR's dense financial services sector — credit appraisal report drafting, loan underwriting summaries, regulatory filing generation, AML narrative.

Loan appraisal report auto-draft
Bank statement analysis narrative
RBI reporting automation
AML/CFT risk narrative generation
NBFC portfolio commentary

Foundation Models

Which LLM Do We Use For Your Project?

We're model-agnostic. We pick the right foundation model for your use case, latency requirements, and whether you need on-premise deployment.

GPT-4o

OpenAI

Best all-round · customer service · copilots

Claude Sonnet

Anthropic

Long documents · contract review · RAG

Gemini 1.5 Flash

Google

Cost-efficient · high-volume content generation

Llama 3.1 70B

Meta (open-source)

On-premise · data-sensitive use cases

Mistral 7B

Mistral AI

Fine-tuning · low-latency · on-device

Sarvam AI

Sarvam (India)

Hindi / Indic languages · regional content

Pricing

Gen AI Project Pricing

Fixed scope. Milestone payments. Working prototype in 2 weeks before you commit to the full build.

Pilot / Single Module

$12K – $30K

One Gen AI system (RAG or LLM app)

1 RAG system or LLM application
2 integrations (ERP / CRM / API)
6–10 week delivery
AWS Mumbai deployment
60-day post-launch support

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Common Questions

What generative AI development services does XPndAI offer in Delhi NCR?

Six service lines: RAG systems (ask-your-documents over internal knowledge bases), LLM applications (GPT-4o and Claude custom apps), AI copilots (embedded GenAI in ERP/CRM), content automation (product descriptions, reports, emails at scale), LLM fine-tuning (domain-adapted models on your data), and Gen AI for BFSI (credit appraisal drafting, RBI reporting, AML narrative). Based in Delhi NCR, serving Gurgaon, Noida, and Delhi enterprises.

How much does generative AI development cost in Delhi NCR?

A single RAG system or LLM application: $12K–$30K (6–10 weeks). Multi-module Gen AI platform with ERP integrations and copilot: $40K–$100K (14–20 weeks). Enterprise custom LLM fine-tuning with on-premise deployment: $100K–$250K. All projects are fixed-scope with milestone payments. Working prototype delivered in 2 weeks before you commit to the full build.

What is the difference between "generative AI" and "AI agents" — do I need one or both?

Generative AI refers to the capability to produce text, code, or content from a prompt — the GPT-4o or Claude model layer. AI agents use that generative capability as the "brain" but add the ability to take actions: call APIs, read databases, update ERPs, make decisions across multiple steps. Most enterprise projects need both: a generative AI foundation (RAG or fine-tuned LLM) and an agent layer (LangGraph) to make the AI actually do something in your systems, not just answer questions.

Can you fine-tune an LLM on our company's proprietary data without sending it to OpenAI?

Yes. We use Llama 3.1 or Mistral for on-premise fine-tuning — the training runs on your infrastructure (or a private AWS VPC in ap-south-1) and the data never leaves your environment. For non-sensitive data, we also use OpenAI's fine-tuning API for GPT-4o Mini, which is cheaper and faster. We'll recommend the right approach based on your data sensitivity and accuracy requirements.

Generative AIDevelopmentCompany Delhi NCR