AI copilots
Assistants inside your tools that draft emails, proposals, reports and code using your templates and data.
Generative AI Development
Generative AI Development Company building copilots on your data
We turn large language models and image models into products your team and customers use every day: copilots, content engines, smart search and document automation, built securely on your own data.
- Copilots, content and smart search
- Private, secure and on your cloud
- Proof of concept in 2-3 weeks
450+Projects launched
7+Years of experience
20+Dedicated developers
96%Client retention
In short
Dinoustech is a generative AI development company that builds custom GenAI products: AI copilots for teams, content and image generation tools, retrieval-augmented (RAG) search over company documents, and LLM features inside web and mobile apps. We work with GPT, Claude, Gemini, Llama and Stable Diffusion, fine-tune models when needed, and deploy on your cloud. A proof of concept usually takes 2 to 3 weeks.
The basics
What generative AI can do for your business
Generative AI creates new content, such as text, images, code, summaries and answers, based on patterns learned from huge amounts of data. For a business, its real value appears when it is connected to your own knowledge: your products, contracts, tickets, reports and style.
That is done in two main ways. Retrieval-augmented generation (RAG) lets a model look up the right documents before it answers, so it stays accurate and up to date without retraining. Fine-tuning teaches a model your format, tone or domain language when prompting alone is not enough. Most products use RAG first and fine-tune only where it clearly helps.
We focus on use cases with a clear return: hours of writing and searching saved, faster proposals and reports, better self-service for customers, and new AI features that make your app stand out from competitors.
What we build
Generative AI Development Services
End-to-end GenAI work, from picking the first use case to running it securely at scale.
RAG and enterprise search
Ask questions across PDFs, wikis, drives and databases and get exact answers with links to the source.
Content generation
Product descriptions, SEO drafts, ad copy and social posts at scale, in your brand voice, with human review.
Image and video generation
Product visuals, creatives and avatars with Stable Diffusion, DALL-E and video models, inside your app or workflow.
Document AI
Read invoices, contracts, KYC and medical records; extract data, summarise and flag risks automatically.
LLM fine-tuning
Fine-tune open or hosted models on your data for better accuracy, lower cost and private deployment.
Use cases
Generative AI Use Cases
Popular GenAI projects we build, with the teams that benefit most.
| Use case | How it works | Who benefits |
|---|---|---|
| Sales proposal copilot | Pulls client data, past proposals and pricing to draft a tailored proposal in minutes | Sales and pre-sales teams |
| Knowledge assistant | Answers staff questions from HR policies, SOPs and manuals with sources | HR, operations, new joiners |
| Product content engine | Writes titles, descriptions and specs for thousands of SKUs in your style | eCommerce and marketplaces |
| Report and summary generator | Turns data, calls and meetings into clear summaries and action points | Managers and leadership |
| AI tutor | Explains topics, creates quizzes and adapts to each student's level | EdTech and coaching institutes |
| Financial document AI | Reads statements and invoices, extracts data and writes plain-language insights | Fintech, lending and accounting |
How we work
Our Generative AI Development Process
A clear, step-by-step process with a working demo early, so you always know what you are getting.
- 01
Use-case discovery
We list ideas, score them by value and feasibility, and pick the one with the fastest clear return.
- 02
Data readiness
We check the documents and data the model needs, clean them and set access rules.
- 03
Proof of concept
In 2-3 weeks you test a working GenAI feature on your real data and see the quality yourself.
- 04
Production build
We add security, evaluation tests, cost controls, UI and integrations for real users.
- 05
Evaluate and scale
Ongoing quality checks, prompt and model updates, and new use cases on the same platform.
Technology
AI Tools and Technologies We Use
We pick proven, well-supported tools for each project, and keep your product free to switch models later.
AI models
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Whisper
Frameworks
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- Hugging Face
- PyTorch
Data & search
- Pinecone
- pgvector
- Weaviate
- Elasticsearch
- PostgreSQL
- Redis
Apps & APIs
- Python
- FastAPI
- Node.js
- React
- React Native
- Flutter
Cloud
- AWS Bedrock
- Azure OpenAI
- Google Vertex AI
- Docker
- Kubernetes
Pricing
Generative AI Development Cost
Starting prices for typical GenAI projects. Your free plan gives a fixed quote after we see your data and goals.
| Scope | What you get | Timeline | Starting from |
|---|---|---|---|
| Proof of concept | One use case on your data, simple UI, quality report | 2-3 weeks | $5,000 |
| GenAI product / feature | RAG or copilot in your app, admin, security, evaluation tests | 6-10 weeks | $15,000 |
| Enterprise GenAI platform | Multiple use cases, fine-tuning, SSO, private hosting, governance | 12+ weeks | $40,000 |
Model and GPU costs depend on usage and are billed at actual cost. We compare hosted and open models so you pay only for the quality you need.
Why Dinoustech
Why Choose Dinoustech for Generative AI Development
450+ projects launched
Apps, platforms and software shipped for startups and enterprises in India, the USA, UK, UAE, Canada, Australia and Singapore.
Your data stays yours
NDA from day one, private model hosting when needed, and no training of public models on your data. GDPR and HIPAA-ready builds.
Working demo in weeks
We ship a usable prototype on your real data early, so you judge results, not slides, before the full build.
Support after launch
Monitoring, accuracy reviews, cost tuning and model upgrades are part of the plan, so the AI keeps getting better.
Free, no obligation
Get Your Free Generative AI Development Plan
Share your idea and an AI expert will reply within 24 hours with:
- The best AI approach for your goal
- Features, tech stack and timeline
- A clear cost estimate
- NDA before we discuss details
FAQs
Generative AI Development FAQs
What is generative AI development?
Generative AI development is building software that uses models such as GPT, Claude, Gemini, Llama or Stable Diffusion to create text, images, code or answers. For businesses it usually means copilots, content tools, smart document search and AI features inside apps, connected securely to company data.
How much does a generative AI project cost?
A generative AI proof of concept usually starts around $5,000 and takes 2 to 3 weeks. A production GenAI feature or copilot starts around $15,000, and an enterprise platform with several use cases, fine-tuning and private hosting starts around $40,000. Model usage is billed separately.
Should I use RAG or fine-tuning?
Start with RAG in most cases. It keeps answers accurate and current by letting the model read your documents at question time, and it is cheaper to update. Fine-tuning helps when you need a fixed format, special domain language or a smaller, cheaper model, and we often combine both.
Is my data safe with generative AI?
Yes, if the solution is designed for it. We use enterprise APIs that do not train on your data, or host open models on your own AWS, Azure or Google Cloud account, encrypt data, and apply role-based access so users only see what they are allowed to.
Can you add generative AI to my existing app?
Yes. We add GenAI features such as smart search, chat assistants, content writing, summaries and image generation to existing web and mobile apps through APIs, without rebuilding the whole product.
Which industries use generative AI the most?
Retail and eCommerce, fintech, healthcare, education, real estate, media and professional services are among the biggest adopters, mainly for customer support, content, document processing and internal knowledge search.
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