Generative AI Development Services | Custom AI Applications

Generative AI development company

We build AI tools that actually know your business. They read through your own files and systems, answer your team's questions, and show which document each answer came from, so nobody has to take the AI's word for it. And your data never leaves your own accounts.

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  • OpenAI
  • Claude
  • Llama
  • LangChain
  • Pinecone
  • Python
Generative AI development company
Cited answers Every reply links to its source
Model-agnostic GPT, Claude, Gemini or open models
Private options Your cloud or on-premise
PoC in weeks Proof before a big budget
Generative AI consulting and development

Enterprise generative AI solutions

We don't have a ready-made AI product to sell you. We look at where your team is losing hours, then build custom generative AI solutions for business around that. Being an AI software development company that also writes normal software helps, because the AI has to connect to your existing systems.

Conversational LLM app with retrieval-augmented generation Most requested

LLM application development

We take your idea all the way to a live product, including screens, backend, data and hosting. That's generative AI app development done end to end.

LLM integration services

LLM integration services

Your team doesn't need another app to log into. We put ChatGPT, Claude or Gemini right inside the CRM, ERP or helpdesk they already open every morning.

RAG pipeline development services

RAG pipeline development services

How your documents are split up, stored and searched decides whether the AI gives good answers or bad ones. It's fiddly work, and we spend a lot of time testing it until the answers come out right.

Enterprise AI search development

Enterprise AI search development

Just type your question like you'd ask a colleague. The answer comes back from your drives, wikis and old tickets, with a link to exactly where it was found.

AI knowledge assistant

AI knowledge assistant

Instead of pinging HR about leave rules, or asking a team lead the same thing for the third time, staff just ask the assistant and get the answer from your own documents.

Internal copilot development

Internal copilot development

It lives inside the tools your teams already use and takes care of the boring bits: first drafts, catching up on long email threads, digging up information.

Intelligent document processing

Intelligent document processing

Our document intelligence solutions read through invoices, contracts and forms for you, pick out the details that matter, double-check them, and only then put them into your system.

AI content generation solutions for business

AI content generation

It writes first drafts of product copy, proposals and reports in your brand's voice. But nothing gets sent until a real person on your team has read it and said yes.

Business process automation with generative AI

Business process automation

Some tasks are a chain of small steps: read the email, check the order, update the record. Agentic workflows can work through the whole chain for you, and they never step outside the rules you've set.

LLM guardrails and AI governance implementation

LLM guardrails & AI governance

We give the AI clear boundaries: filters, access rules, automatic hiding of personal details and a full log of everything it does. That way it follows your policies, not its own ideas.

Generative AI proof of concept (PoC) and MVP development

Proof of concept (PoC) & MVP

We try the idea on your real data for a few weeks. If the numbers look good, we turn it into an MVP.

Private LLM deployment

Private LLM deployment

Open-source foundation models installed in your own cloud or data centre. Your data stays on your network.

Quick check

Is generative AI a good fit for your business?

Read through these. If two or three sound like your office, AI is probably worth a look.

The same customer questions come in every day.

Finding one document means digging through folders and old emails.

Someone types invoice or form details into a system by hand.

New joiners take weeks to find their way around.

Reports and proposals eat up whole afternoons.

You have years of documents that nobody opens.

Retrieval-augmented generation (RAG)

How to build a RAG chatbot for internal company documents

Most AI tools only know what they picked up from the internet. Retrieval-augmented generation changes that: before answering, the AI looks through your own documents first. Think of a student who's allowed to bring the textbook into the exam. Enterprise search using LLMs and vector databases works on the same idea.

  1. 01

    Collect

    Your PDFs, Word files, wiki pages and tickets go into one knowledge base that updates itself.

  2. 02

    Chunk and embed

    Documents are cut into small sections and each one is turned into a vector the computer can compare.

  3. 03

    Store

    Those vectors sit in a vector database, labelled so people only see what they're cleared for.

  4. 04

    Retrieve

    A question triggers semantic search and keyword search together, and the best passages win.

  5. 05

    Answer

    The AI answers from those passages only and lists its sources.

  6. 06

    Evaluate

    Each week we run test questions and track accuracy, speed and cost through model monitoring.

Choosing an approach

Fine-tuning vs RAG: which is right for my business?

People ask us this a lot. RAG lets the AI read your documents. Fine-tuning changes how it writes. Start with RAG; most companies never need more.

RAG Fine-tuning
Best for Answering from your documents A fixed tone, format or task
New information Add a file, it's live Retrain the model
Sources shown Yes No
Upfront cost Lower Higher, needs training data
Hallucination reduction

How to reduce hallucinations in enterprise LLM applications

Sometimes an AI says something wrong with total confidence. That's a hallucination. You can't stop it completely, but you can make it rare and easy to catch.

Ground every answer

Only the documents it finds are used, not the AI's general knowledge.

Show citations

Sources are listed under every answer for a quick check.

Allow "I don't know"

Through prompt engineering, the AI is told to say it doesn't know rather than guess.

Check the output

A second pass checks the answer against the sources.

Test constantly

Model evaluation runs on the same test questions after every update.

Data privacy and AI safety

Private or on-premise LLM deployment for enterprises

Work in banking, healthcare or law? You can still use AI. Data privacy and AI safety just need a different setup. Pick one of these three.

Option 1

Enterprise API, no training

Business plans from GPT, Claude or Gemini that don't train on your data. Fastest to set up.

Option 2

Your cloud account

AWS Bedrock, Azure OpenAI or Google Vertex AI inside your own cloud account.

Option 3

Fully on-premise

Llama, Mistral or another open model on your own servers, cut off from outside providers.

Industry Focus

Specialized Generative AI applications

Document processing

Generative AI for document processing and intelligence

  • Invoice and purchase order data extracted and entered into your ERP
  • Contract clauses located, compared with your standard terms and flagged
  • KYC and onboarding forms checked for missing or mismatched details
  • Long reports and case files summarised into a one-page brief
Healthcare AI

Generative AI use cases for healthcare

  • Consultation notes turned into structured summaries for doctors to review
  • Insurance claims and discharge paperwork filled in automatically
  • Clinical protocols and guidelines searchable by staff in plain language
  • Common patient questions answered, with clinicians approving sensitive replies
Getting started

What your first four weeks with us look like

Here's how a proof of concept usually goes, week by week.

Week 1

We get to know your business

We meet the people who'll use it, look at your documents and agree what success means.

Week 2

We build the first version

A first working version is connected to your documents and ready to try.

Week 3

Your team tries it out

A small group uses it on real work, and we adjust based on what they tell us.

Week 4

We share honest results

You get the numbers on accuracy, time and cost, and our honest view on next steps.

Lessons learned

Common AI mistakes we'll help you avoid

Most AI projects that stall do so for one of these reasons.

Starting too big

Trying to do everything in one go. We pick one task, get it working, then add more.

Messy documents

Old or conflicting documents lead to wrong answers. We sort the content out first.

No way to measure success

If nobody agreed on targets, nobody knows if it worked. We set them on day one.

Forgetting the people

A tool the team doesn't trust won't get used. We bring staff in early.

Pricing

How much does generative AI development cost?

Start with a small proof of concept. Spend more only after it proves itself. There's also a monthly charge for using the AI model, based on how much you use it.

Stage 1

Proof of concept

One use case, tested on your data, with accuracy measured.

Typical timeline: 3–4 weeks
Most common Stage 2

MVP

Real users, logins, safety checks and links to your systems.

Typical timeline: 6–10 weeks
Stage 3

Production

Rolled out across teams with monitoring and regular updates.

Typical timeline: 3 months +
FAQ

Frequently asked questions

How much does generative AI development cost?
It comes down to what the AI has to do, how much data it handles and how many systems it talks to. We begin with a proof of concept so the first bill is small. After launch, you pay a monthly model fee that follows your usage.

Should I use fine-tuning or RAG?
Pick RAG when answers must come from your documents and stay current. Pick fine-tuning when you need a fixed tone or format. Most companies start with RAG and stop there.

How do you stop an LLM from making things up?
It can only answer from documents it finds, it must show sources, and it's allowed to say it doesn't know. A second check catches unsupported claims, and we test regularly.

Can we run an LLM privately, without sending data outside?
Yes. Use an enterprise plan that doesn't train on your data, host the model in your own cloud account, or run an open model on your own servers. We'll suggest one based on how sensitive the data is.

Can you integrate ChatGPT into our existing software?
Yes. ChatGPT, Claude or Gemini can go into your CRM, ERP, helpdesk or custom app. Each person only sees data they're allowed to, and requests are logged.

What is an AI knowledge assistant for employees?
An internal tool where staff ask about policies, products or processes in plain language. It answers from your documents with a link to the source, so HR and IT get fewer interruptions.

Why choose a generative AI development company in India?
Good engineers at a fair price, plus a team that knows Indian languages, local data rules and the tools Indian companies use. We work from Lucknow, with clients in India and abroad.
Start a PoC

What would you like AI to do?

Tell us what slows your team down and which files or systems are involved. You don't need to be technical. We'll reply within a working day.

Email

[email protected]

Phone

+91 81890 10044

Address

1st Floor, CP/140, Viraj Khand-4, Gomti Nagar, Lucknow, Uttar Pradesh 226010

NDA on request. Your data is never shared.

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