Straight answers from the engineers who actually build this stuff.

A normal rule-based chatbot only answers from a fixed script — go off-script and it breaks. A RAG (Retrieval-Augmented Generation) chatbot reads your own documents, website, PDFs or database in real time, pulls the most relevant pieces, and uses an LLM like GPT-4o to write an accurate answer grounded in your content.

The practical wins: it handles open-ended questions, stays current the moment you update your data, and hallucinates far less because every answer is tied to a real source.

Yes — on the official Meta WhatsApp Cloud API, not the unofficial workarounds that risk getting your number banned. That covers template approval, interactive buttons and lists, media, payments, and clean handoff to a live agent.

For Indian businesses this is usually the highest-ROI channel, because your customers already live on WhatsApp.

Yes. Beyond Q&A chatbots, we build agentic systems that plan and execute multi-step tasks — looking up data, calling your APIs, filling forms, sending email or WhatsApp, and updating your CRM.

Common wins: lead qualification, support triage, and automated report generation, where the agent removes repetitive manual work instead of just chatting.

Yes — without rebuilding what you already have. We add LLM features to existing PHP, Django, React, WordPress and custom apps over APIs.

Typical additions: a support chatbot, document search, content generation, and automated data extraction from invoices or forms.

A focused single-knowledge-base RAG chatbot typically starts around ₹1.5–2.5 lakh. Multi-source, multilingual systems with an admin dashboard, analytics and human handoff run ₹6–10 lakh+.

Cost is driven by data volume, number of integrations (WhatsApp, CRM, website), the LLM you choose, and hosting. We give a fixed quote after a free 30-minute call — no open-ended billing.

A working prototype in 2–3 weeks, and a polished production bot with your data, integrations and testing in 4–6 weeks.

The biggest variable is how clean and ready your knowledge base is, and how many systems we connect to. Messy data is usually the real bottleneck, not the AI.

A local Delhi NCR team means same-timezone communication, in-person meetings when it matters, pricing in rupees without overseas markups, and people who understand Indian data rules and business context.

Versus a single freelancer, you get a full team for engineering, deployment and support — so the project doesn't stall if one person disappears.

Yes — across all of Delhi NCR including Noida, Gurgaon and Faridabad, plus remote clients all over India. Delivery is mostly online, with on-site meetings for NCR clients when a project needs it.

AI company in Noida · AI company in Gurgaon

There's no single winner — it depends on budget, language and privacy:

Quality default: OpenAI text-embedding-3-large. Open-source / self-hosted (data stays private): bge-large and the e5 family, with multilingual-e5 handling Hindi and other Indian languages well. Cost-sensitive: text-embedding-3-small or a smaller open model is often enough.

We benchmark 2–3 options on your actual data before committing — generic leaderboards rarely match your domain.

Yes. We can deploy on India-hosted infrastructure or your own cloud account so data stays under your control, and we support on-premise setups for sensitive use cases.

When data can't leave your environment, we use open-source / self-hosted models. We sign an NDA before any project starts.

Start with a free 30-minute consultation. We discuss your goal, look at your data and systems, and send a fixed-scope proposal with timeline and cost within a couple of days. No charge, no obligation for the first call.