AI products that make it past the demo.
For founders and product teams: we price the idea first, then build the LLM app, RAG system or AI software through to launch.
What a build looks like
30 tools
One agent, with a person approving money, messages and deletes
BigDeal AI, our in-house product
8-12 wks
From audit to a working MVP
Most projects
30 days
Of support after launch
Every build
We build the product around the model too.
LLM applications
Chat interfaces, assistants, document analysis and search, with error handling, cost controls and user permissions built in.
RAG pipelines
AI that answers from your own documents and databases, with search, chunking and re-ranking tuned for accuracy.
Agentic software
Agents that plan multi-step tasks and act inside your other systems.
AI-native SaaS
A whole product built around AI: architecture, backend, frontend, payments, sign-in and deployment.
Copilots and internal tools
Proposal generators, onboarding assistants and analysis helpers that sit inside the systems your team already uses.
AI features for an existing product
Search, auto-fill, summaries, recommendations or classification, added to the software you already run, one feature at a time.
Priced first, then built in slices.
Weeks 1-2
Audit and architecture
We work out what the product is worth. You sign off the architecture, with its speed and cost targets, before any code.
Models and data
We pick a model for each task and decide whether you need RAG or fine-tuning. This sets the quality ceiling.
Every 2 weeks
Build in working slices
A working piece goes live behind a feature flag each sprint, so you use the product for weeks before launch.
Test before it ships
Each AI feature gets its test suite first. Load tests, security review and cost monitoring go in before launch.
Launch and hand over
We deploy on the infrastructure you prefer and hand over diagrams, runbooks and dashboards, with your team trained.
Chosen for what your team can run.
Models
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
Backend
- Python
- FastAPI
- Node.js
- LangChain
- LlamaIndex
Frontend
- React
- Next.js
- TypeScript
- Tailwind CSS
Orchestration
- n8n
- LangGraph
- Temporal
- Custom orchestration
Infrastructure
- Vercel
- AWS
- GCP
- Self-hosted
- Azure
What an AI product costs in your market.
Ranges run from a focused MVP to a product with several AI parts. The audit turns the range into one fixed price.
India
₹8-40 lakh
Fixed price once the audit is done.
UAE
AED 40,000-200,000
Quoted and invoiced in AED, with delivery reviews over video.
USA
$15,000-75,000
Quoted and invoiced in USD, with delivery reviews over video.
UK
£12,000-60,000
Quoted and invoiced in GBP, with delivery reviews over video.
Questions about AI product development.
What kinds of AI products does OutGrow build?
LLM applications such as chat, document analysis and search; RAG systems that answer from your own data; AI agents for multi-step work; AI-native software products; copilots for internal teams; and AI features inside products you already run.
How much does AI product development cost in India, the UAE, the USA and the UK?
A focused AI product or MVP usually costs ₹8-15 lakh in India, AED 40,000-75,000 in the UAE, $15,000-30,000 in the USA and £12,000-25,000 in the UK. Products with several AI parts typically cost ₹20-40 lakh, AED 100,000-200,000, $40,000-75,000 or £30,000-60,000. The audit fixes your price.
How long does it take to build an AI MVP?
Most MVPs take 8 to 12 weeks from audit to launch. The first two weeks cover the audit, data review and architecture, and you see working software at the end of every sprint after that. Products with several AI parts or custom model work can take 14 to 20 weeks, and we say so upfront.
Which LLMs does OutGrow use?
We choose per task: GPT for complex reasoning and multimodal work, Claude for documents and long context, Llama for cost-sensitive or self-hosted work, and Mistral when European data residency matters. Routing by task keeps token costs well below running one big model for everything, and the model layer can be swapped without a rebuild.
What is a RAG pipeline and when do I need one?
RAG, or retrieval-augmented generation, lets AI answer from your own documents and databases instead of only what the model learned in training. You need it when answers must draw on internal docs, product knowledge or past cases accurately. Without it, models tend to make up specifics.
Can you add AI to our existing product?
Yes. Many projects add search, summaries, recommendations, classification or an in-app assistant to software a company already runs. We work in your codebase alongside your team, ship one feature at a time and document each one.
Have an AI product in mind?
Tell us what you want to build. We'll check it is worth building and give you a fixed price before any code is written.
Most businesses we meet have paid for an AI tool nobody uses. We start from your numbers, so the first build is chosen by payback.
Zain Khan and Anwar Sheikh
Founders, OutGrow
Outgrow
Intelligence
Studios.