AI features your users will actually pay for.
Copilots, retrieval, prediction, personalization and natural-language access, engineered into your product rather than stapled on.
What is AI-powered product engineering?
Eazyware engineers AI into products people pay for: LLM applications, in-app copilots for SaaS, predictive machine-learning models, retrieval and knowledge systems, natural-language querying over databases, and personalization engines, all built with evaluations, observability and cost control for production.
AI-Powered Product Engineering services
6 services in this line, each with a published starting price.
Why product companies hire a generative AI development company
Users now expect AI inside the products they pay for: a copilot that answers from their own data, search that understands a question, recommendations that feel personal, reports built from a sentence. Building those features well is different from building software: prompts need versioning and evaluation, retrieval needs permissions and refresh, models change underneath you, and inference has a cost per request that shows up on the invoice. A generative AI development company brings the engineering that makes AI features accurate, fast and affordable at scale, and turns them into something you can sell as a premium tier rather than a cost you absorb.
What businesses hire us for
The requests that most often turn into generative AI development engagements.
- 01
AI copilot development for SaaS
In-app assistants that know the account, the screen and can act through your API
- 02
LLM application development
Production applications with multi-model routing, evals and cost control
- 03
RAG development services
Retrieval over your documents with citations, permissions and measured relevance
- 04
Text-to-SQL and conversational analytics
Ask your database a question and get a governed, correct answer
- 05
Recommendation engine development
Personalisation with A/B-tested lift on conversion and retention
- 06
Machine learning development
Forecasting, churn, fraud and NLP models trained on your data
Who this is for
- SaaS companies adding an AI tier to an existing product
- Product teams whose first AI feature works in demos and fails at scale
- E-commerce, media and education platforms with event data to personalise on
- Companies with large document sets that need grounded, citeable answers
What you can expect
- AI features with an evaluation score per release, not anecdotes
- p95 latency and cost per request you can put in a plan's margin
- Retrieval that respects permissions and refreshes on schedule
- A codebase your own team can extend after handover
How to choose a generative AI development company
- 01They can show accuracy on a golden set built from your data
- 02They route across models rather than lock you to one vendor
- 03Prompts are versioned and tested like code
- 04They meter usage per tenant so the feature can be priced
- 05They design for permissions and data isolation from day one
How much does generative AI development cost?
LLM application development at Eazyware starts at $21,000 (₹13.5 lakh); an in-app SaaS copilot from $19,500 (₹13 lakh); production RAG and knowledge engineering from $14,000 (₹8.8 lakh); natural-language data querying from $12,500 (₹8 lakh); personalisation engines and custom ML from $17,500–21,000 (₹11–13.5 lakh). Ranges depend on data readiness, integrations and evaluation depth, and a three-week ProofRun on your own data gives a fixed price before the build.
Why Eazyware for generative AI development
- We build and run our own AI-native SaaS suite, so the patterns are proven in production
- Evaluation suites and observability shipped with every feature, gating every release
- Multi-model routing across OpenAI, Anthropic, Google and open-weight models to control cost
- Per-tenant metering and billing so AI becomes revenue, not overhead
Industries we serve
Clients across India, the Middle East, Europe, North America, Africa and Australia. Delivery from Bengaluru with New York and London studios.
AI-Powered Product Engineering: common questions
01Which model do you use?
Whichever wins the benchmark for your task. Most systems route between two or three.
02Will it work with our existing backend?
Yes. We integrate via your APIs, no rewrite.
03LLM or ML?
We pick per problem, often both in one system.
04How much does generative AI development cost?
LLM application development at Eazyware starts at $21,000 (₹13.5 lakh); an in-app SaaS copilot from $19,500 (₹13 lakh); production RAG and knowledge engineering from $14,000 (₹8.8 lakh); natural-language data querying from $12,500 (₹8 lakh); personalisation engines and custom ML from $17,500–21,000 (₹11–13.5 lakh). Ranges depend on data readiness, integrations and evaluation depth, and a three-week ProofRun on your own data gives a fixed price before the build.