AI Product Development Company
AI-first products and features, designed, engineered and launched by one team: the idea, the model, the back end and the app.
OZRIT builds AI products for startups launching a new idea and for companies adding AI to an existing product. Product designers, AI engineers, software developers and QA work together from the first sprint, so what ships is a product people can use, not a prototype.
Tell us what you need
A consultant replies within one business day. No sales deck.
- 17+years building softwareFounded in Hyderabad in 2009
- 500+in-house engineersFull-time, not freelancers
- ISO/IEC 27001:2022certifiedFor information security
- 98%client retentionLong-term partnerships
AI product engineering, explained
An AI product is not a model with a screen on top. Its value depends on the model working every day.
AI product development covers the whole path from idea to a live product: finding the user problem, deciding where AI genuinely helps, designing the experience, building the models and the software around them, and running it in production. It differs from ordinary app development in the uncertainty it has to manage, because model behaviour, accuracy and running cost are not fully known until real users arrive.
OZRIT handles that uncertainty with short cycles. We prototype the AI behaviour early, test it with real users and real data, and only then invest in scale. The same team builds the web and mobile front ends, the APIs, the data pipelines and the AI components, so there are no hand-off gaps between a model team and an app team.
For startups we build an MVP that tests the core idea quickly and cheaply. For established companies we add AI features to current products, behind feature flags, so they can be released to a few customers first and measured before wider roll-out.
- Product discovery and validation
- UX design for AI features
- AI MVP development
- Model selection and integration
- Web and mobile applications
- Back end, APIs and data pipelines
AI product development services
Everything between a product idea and a live, supported AI product.
Product discovery and validation
We test the problem, the users and the value of AI before you commit to a full build.
UX design for AI features
Interfaces that show confidence, allow correction and build trust in suggestions and automated results.
AI MVP development
A focused first version that proves the core idea with real users and real data.
Model selection and integration
We choose between hosted models, open-weight models and custom-trained ones on accuracy, cost and privacy.
Web and mobile applications
iOS, Android and web front ends, with cross-platform options such as Flutter and React Native.
Back end, APIs and data pipelines
Scalable services, data ingestion and storage that keep the AI fed with clean, permissioned data.
Testing and evaluation
Functional testing plus AI-specific evaluation sets that check accuracy, safety and regressions.
Launch, scaling and support
App store submission, monitoring, cost control and a monthly plan for improvements after launch.
How we build an AI product
Six stages, each ending with something you can test with real users.
Idea to launch
Working MVP in roughly 8 to 16 weeks
- 1
Discover and define
We agree the user problem, the role of AI and the measures that will show the product is working.
- 2
Design the experience
We design the flows and the AI interactions, and test them with prototypes before writing code.
- 3
Prototype the AI
We build the AI behaviour on its own first and test it on real examples, so the riskiest part is proven early.
- 4
Build the product
We build the app, the back end and the AI components in two-week sprints, with a working build each time.
- 5
Test and harden
We run functional, load and security tests along with AI evaluations on real-world inputs.
- 6
Launch and iterate
We release to a first group of users, watch usage, quality and cost, and improve in short cycles.
AI products we build
Typical product ideas, across sectors.
AI assistants for professionals
Products that help doctors, lawyers, recruiters or advisers handle paperwork and research faster.
Personalised consumer apps
Learning, wellness, shopping and entertainment apps that adapt to each user.
AI features for existing SaaS
Search, summaries, recommendations and copilots added to products that already have customers.
Document and data products
Tools that read, classify and analyse documents or datasets and present the answers clearly.
Marketplaces with smart matching
Platforms that match supply and demand using ranking, recommendations and prediction.
Internal AI tools
Applications that give your own teams AI-powered search, reporting and automation.
AI product development for your industry
The same build applies across sectors, but the data, rules and risks differ. These are the industries where we are asked for it most.
Healthcare app development
Record summaries, triage support, scheduling and personalised plans for providers and health-tech teams.
Education app development
Tutoring assistants, content generation, assessment help and engagement analytics for learning platforms.
Fintech app development
Fraud signals, credit scoring, document checks and support assistants for lenders, payments and banks.
Retail and eCommerce app development
Product search, recommendations, demand forecasting and support assistants for stores and marketplaces.
Logistics app development
Route planning, ETA prediction, proof-of-delivery checks and exception alerts for fleets and supply chains.
Real estate app development
Lead qualification, property search, valuation support and document handling for developers and brokers.
The technology behind our AI products
We pick the front end, back end and models that fit your product, your team and your budget.
Models
Chosen per task, cost and privacy need
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Hugging Face models
Applications
Web and mobile
- Flutter
- React Native
- React
- Next.js
- Swift
- Kotlin
Back end
APIs and services
- Node.js
- Python
- FastAPI
- Laravel
- PostgreSQL
- MongoDB
Data and search
Your data, searchable and permissioned
- PostgreSQL + pgvector
- Pinecone
- Weaviate
- Elasticsearch
- Redis
Cloud and deployment
In your cloud account or ours
- AWS Bedrock and SageMaker
- Azure OpenAI and ML
- Google Vertex AI
- Docker
- Kubernetes
What decides the cost of AI product development
An AI product has a build cost and a running cost, and we estimate both before you start.
Scope of the first release
A focused MVP costs far less than a full platform with every feature.
Platforms and screens
iOS, Android and web, and how many screens each needs, shape the effort.
AI complexity
A hosted-model feature is simpler than a custom-trained or multi-step AI system.
Data and integrations
Connecting to your data and third-party systems adds engineering work.
Running costs
Model usage, hosting and monitoring continue after launch and scale with users.
Support and iteration
Post-launch improvements are agreed as a monthly plan.
Security and privacy in AI product development
A security or compliance team will ask where the data goes, who can see it and how quality is checked. These are the safeguards we build into every project.
Where your data lives
Models can run in your own cloud account or on private endpoints, with provider settings that stop your data being used for training. You choose the region.
Access control
Assistants respect the permissions of the person asking. Someone who cannot open a document cannot get its content through a chat window either.
Testing before launch
We build an evaluation set from your real cases and measure accuracy, safety and cost before and after every change, not just once at launch.
Certification and ownership
OZRIT is ISO/IEC 27001:2022 certified for information security. You own the code, prompts, models you commission and your data.
All eight AI safeguardsCertifications and AI frameworks we work to
AI product development: frequently asked questions
Straight answers on cost, timelines, data, security and ownership.
What does an AI product development company do?
It takes an AI product from idea to launch: validating the problem, designing the experience, building the AI and the software around it, testing, launching and supporting it. The aim is a working product, not only a model.
How much does AI product development cost?
The cost depends on the scope of the first release, the platforms, the AI complexity and the integrations. A discovery sprint from $1,000 defines the product and ends with a scoped quote, and we also estimate the running cost of the AI.
How long does it take to build an AI MVP?
An AI prototype usually takes 2 to 4 weeks and a working MVP roughly 8 to 16 weeks, depending on scope. We agree the plan and milestones before you commit.
Can you add AI to our existing product?
Yes. We add AI features through APIs and services, behind feature flags, so you can release to a few customers first. You do not need to rebuild the product.
Do you build both the app and the AI?
Yes. Product designers, AI engineers, mobile and web developers, back-end engineers and QA work as one team, so the AI and the app are built together.
Should we use an existing model or train our own?
Most products start with a hosted or open-weight model and your own data, which is faster and cheaper. Training a custom model makes sense when you have unique data and a clear accuracy gap that other approaches cannot close.
How do you control the running cost of AI features?
We choose models by cost as well as quality, cache and batch where possible, set usage limits and track cost per user. We give you an estimate of running cost before the build, not after.
Do you work with startups?
Yes. Startups usually need a fast, focused MVP that tests the core idea. We keep the first release small, measure it with real users and expand only what proves its value.
Can you publish the app to the App Store and Google Play?
Yes. We handle store submission, review requirements and release management for iOS and Android, as part of launch.
Who owns the product and the code?
You do. The code, designs, prompts and documentation belong to you, while any third-party model you choose stays under its provider's own terms, which we go through with you before launch.
Plan your AI product
Tell us the product idea and who it is for. We will say what AI should and should not do in it, and what a first release would involve.
What happens next
- 1An AI consultant reads your note and may reply with a few questions.
- 2We hold a call to understand the problem, your data and the systems involved.
- 3If AI fits, we propose a discovery sprint or a scoped pilot with clear success measures.