Custom AI software, generative AI, agents and machine learning, built around one business problem and connected to the systems you already run.
OZRIT is an AI development company in Hyderabad, India, working with startups and enterprises worldwide. We take AI from use-case workshop to a monitored production system.
Tell us what you want AI to do
A consultant replies within one business day. No sales deck.
We build with OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Hugging Face, LangChain, LlamaIndex, PyTorch, TensorFlow, AWS Bedrock, Azure OpenAI, Google Vertex AI, Pinecone, pgvector, MLflow.
OZRIT is an AI development company and AI solutions provider. Our AI software development services, also called artificial intelligence development services, cover the whole job for enterprises and startups across India and overseas, from the first workshop to a monitored system in production.
Use-case workshops and data reviews that rank your AI ideas by value and risk, and turn them into a roadmap you can fund in stages.
Custom AI applications, generative AI and LLM assistants, AI agents, chatbots and machine learning models, engineered end to end by one team.
AI added to your ERP, CRM, app or website through APIs and small services, without rebuilding what already works.
A pilot on your own data in 4 to 8 weeks, with success measures agreed up front, before you commit to the full build.
Private deployment in your own cloud, role-based access and masked data, from an ISO/IEC 27001:2022 certified team.
Monitoring, evaluation, retraining and cost control after launch, so the AI keeps performing as your data changes.
You own the code, prompts and your data, with documentation your own team can carry on from.
Add AI, data and software engineers to your own team on a monthly basis, ramped up in 10 to 15 business days.
Fifteen areas of AI work. Scroll to move through them, or pick one.
Workshops and data reviews that rank your AI ideas by value and risk, then turn them into a roadmap you can fund in stages.
The same AI techniques behave differently in each sector, because the data, the rules and the cost of a mistake differ. Here is where we usually start.
Fraud signals, credit scoring, document checks and support assistants.
Record summaries, triage support, scheduling and personalised plans.
Product search, recommendations, demand forecasting and support bots.
Route planning, ETA prediction, document processing and exception alerts.
Tutoring assistants, content generation, assessment help and engagement analytics.
Lead qualification, property search, valuation support and document handling.
Code and ticket assistants, test generation and AI features inside products.
Demand planning, batch quality checks and traceability queries.
Cost forecasting, material planning and site report summaries.
Lead scoring, call insights and route optimisation for on-ground staff.
Pick the job you want AI to do. You will see the system we would build for it, how we keep it safe, what it plugs into and how we would judge whether it works.
First working assistant in 4 to 8 weeks
Policies, manuals, wikis, tickets and product data, split and indexed with their permissions intact.
Retrieval finds the right passages; a language model writes the answer from them only.
Answers cite their source, refuse out-of-scope questions and hide what the asker may not see.
Your website, helpdesk, Slack or Teams, WhatsApp and internal portals.
Share of questions answered correctly, tickets deflected and time to answer.
Four working principles run through every AI project we deliver, from a small assistant to a platform that serves a whole company.
Data scientists, ML engineers and software developers work in one team, so every model is built for a result you can count, not for a demo.
Your documents, databases and apps feed the model through pipelines built to keep working as volume, users and sources increase.
Output quality starts with the data. We clean it, test the model on cases from your own domain, and keep a record of why it answered as it did.
Risk, privacy and oversight are planned at the start. We use published frameworks as the reference, and we document what the system does and who can change it.
OZRIT is certified to ISO/IEC 27001:2022 and ISO 9001:2015. The AI frameworks below are published standards and technical specifications, not certificates. We use them as the reference when we plan risk, governance and security for an AI project, so your compliance team finds familiar language in what we deliver.
Information security management
How OZRIT protects client data and systems, audited by an accredited body. It governs every AI project we run.
Quality management
A documented, audited process for how we plan, deliver and improve engineering work.
Published by NIST, ISO and IEC. They describe how to manage AI well; there is no certificate to hold for them.
AI Risk Management Framework
A voluntary framework for governing, mapping, measuring and managing the risks of AI systems.
Generative AI Profile
A companion to the AI RMF that lists risks specific to generative AI and ways to manage them.
AI management systems
Requirements for setting up, running and improving an organisation-wide management system for AI.
AI risk management
Guidance on identifying, assessing and treating risks across the life of an AI system.
Framework for AI using machine learning
A common description of the parts of a machine learning system and how they fit together.
AI concepts and terminology
Shared definitions for AI, so teams, clients and auditors mean the same thing by the same word.
Tell us your compliance requirements and we will say how a project would meet them.
ISO, IEC and NIST names and marks belong to their owners and appear here only to identify the standards and frameworks. They do not indicate endorsement of OZRIT.
Two products from our portfolio where AI sits inside a working system. Each links to its full case study with the client's own words.
An AI-powered CRM for sales teams that run cold calling and lead follow-up, built for a founder in India.
OZRIT built the CRM with AI-driven lead insights, auto-dialing, call tracking and follow-up automation, so agents work the leads most likely to convert instead of dialling from spreadsheets.
6 months: 5 engineers, 1 designer, 1 QA engineer, 1 project lead
Read the case study
A field force management product for companies whose sales reps, technicians and delivery staff work on the road.
OZRIT built the Flutter field app, manager console and admin portal in under six weeks, including AI route optimisation, geofenced attendance, spoof detection and proof of visit.
Under 6 weeks: 4 engineers, 1 designer, 1 QA engineer, 1 project lead
Read the case studyMore work is on our case studies page.
We pick on accuracy, cost, speed and privacy for your use case. Nothing here is a reseller arrangement, so the recommendation is whichever fits, including open-weight models you can host yourself.
Chosen per task, cost and privacy need
For building, testing and serving
Your data, searchable and permissioned
From notebook to production
Matched to the problem, not the trend
Both are valid. The right choice depends on your requirements, your budget, the systems you already use and who will maintain the software. Custom AI development is not automatically better for every company.
We compare both in discovery and tell you plainly which fits. Sometimes the best answer is an existing tool with a small integration, and we would rather say so than build something you do not need.
The questions a security or compliance team will ask about enterprise AI, answered before they have to. These are the safeguards we build into every AI project.
OZRIT is ISO/IEC 27001:2022 certified for information security.
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.
Assistants respect the permissions of the person asking. Someone who cannot open a document cannot get its content through a chat window either.
Personal and financial fields can be masked before they reach a model, and logs can be redacted or kept inside your environment.
We ground answers in retrieved sources, show the citation, and set a confidence threshold below which the system says it does not know or hands over to a person.
Inputs are filtered, tool permissions are kept narrow, and risky actions need explicit approval, so a crafted message cannot make the system do what it should not.
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.
Review queues, approval steps and easy overrides are built in where an error would be costly. You decide how much runs on its own.
OZRIT is ISO/IEC 27001:2022 certified for information security. You own the code, prompts, models you commission and your data.
AI projects vary too much for an honest flat price. Choose how you want to start, and we will give a fixed-scope or time-and-materials quote once discovery shows what the work involves.
From $1,000
Best when: You have an idea and want proof before committing
You get: Use-case ranking, data review, a tested prototype and a scoped quote
Fixed price per phase
Best when: A defined use case with clear acceptance criteria
You get: A pilot, then a production build with integrations, testing and handover
Monthly rate
Best when: A roadmap of AI work that will run for months
You get: AI, data and software engineers added to your team, ramped in 10 to 15 business days
Monthly retainer
Best when: AI already live that needs care
You get: Monitoring, evaluation, retraining, cost control and new features
Six things decide the cost of AI development. We go through each one with you in discovery. Team rates are on How We Work, and you can hire AI developers directly.
Higher accuracy and stricter safety rules mean more testing and tuning.
Clean, reachable data is quick to use. Data scattered across systems takes work first.
Using a hosted model through an API costs less than training or fine-tuning your own.
Each connection to your CRM, ERP or app adds design and testing.
Private cloud or on-premise deployment costs more than public APIs.
Model usage, hosting and monitoring continue after launch. We estimate them up front.
Our AI implementation services follow seven steps in two phases. The first phase proves the idea on your own data before you commit to the second.
Usually 4 to 8 weeks
We agree the decision or task to improve, who uses it, and the number that will show it worked.
We review quality, access and privacy, and tell you plainly what is possible with the data you have.
We build a small working version and test it on real examples, with a simple baseline to compare against.
A limited group uses it in real work. We measure results and decide together whether to scale.
Usually 2 to 4 more months
We build the full solution and connect it to your systems, with security and cost limits in place.
We test accuracy, safety, performance and security against your acceptance criteria before launch.
We launch, watch quality and cost, and keep improving as real usage shows what to change.
We are a software company first, so AI arrives as part of a product that works, not as a standalone experiment.
500+
in-house engineers under one roof in Hyderabad
AI engineers, data engineers, developers and designers work as one team, so the assistant lands inside a product people use.
We work with OpenAI, Anthropic, Google, Meta and open-weight models and pick on accuracy, cost and privacy, not on a partnership.
If a rule, a report or a better form would do the job, we say so in discovery, before you spend on a model.
Code, prompts, pipelines and data belong to you, with documentation so your team or another vendor can carry on.
Straight answers on cost, timelines, data, security and ownership.
AI is the broad goal of making software act intelligently. Machine learning is a method within AI where models learn patterns from data. Most business AI today combines machine learning models, rules and large language models, which is why our AI development services cover all three.
AI development services cover everything needed to turn AI into working software: finding the right use case, preparing data, building and testing models or assistants, connecting them to your systems, and monitoring them after launch. They include AI consulting, custom AI software development, generative AI, AI agents, chatbots and machine learning.
An AI development company helps you decide where AI will pay off, then designs, builds, secures and runs the solution. At OZRIT that means AI engineers, data engineers, software developers and designers working as one team, so the AI is built into your product or workflow rather than left as a demo.
The cost depends on the use case, data quality, integrations, accuracy needed and hosting. A discovery sprint from $1,000 tests the idea on your own data and ends with a fixed-scope or time-and-materials quote. Team rates are on our How We Work page.
A discovery and pilot usually take 4 to 8 weeks. Moving a proven pilot into production, with integrations, security and monitoring, typically takes another 2 to 4 months. Simple features built on hosted models can be faster.
AI consulting and discovery, custom AI software development, generative AI and LLM development, RAG assistants, AI agents and workflow automation, chatbots and conversational AI, ChatGPT and OpenAI integration, machine learning and predictive analytics, computer vision and document AI, AI integration into existing systems, and MLOps support.
Yes. Most projects extend an existing product. We connect AI through APIs or small services, so your ERP, CRM, app or website gains AI features without a rebuild.
Not always. Many modern AI features use pre-trained models plus your own documents, which needs far less data than training from scratch. We check your data in discovery and tell you plainly what is possible.
We can run models in your own cloud account, use private endpoints and no-training API settings, mask sensitive fields, apply role-based access and keep logs inside your environment. OZRIT is ISO/IEC 27001:2022 certified.
We ground answers in your own documents through retrieval, show the source, set a confidence threshold so the system says when it does not know, and test against an evaluation set built from your real questions. Where an error would be costly, a person reviews first.
Ready-made tools are often right when your process is standard and speed matters most. Custom AI makes sense when you need your own data, workflows, integrations, privacy rules or a feature inside your own product. We compare both honestly in discovery.
Yes. Startups usually want a fast first version to test with users. Enterprises need security reviews, integrations and governance, and we plan for those from the start.
Yes. You can add AI and data engineers to your team on a monthly basis, with a typical ramp-up of 10 to 15 business days. See our hire AI developers page for roles and engagement details.
We agree the success measures up front, such as accuracy, time saved, tickets resolved or conversion, and test against real examples before and after launch. Results are tracked on a dashboard so you can see them without asking us.
Ask who will actually do the work, for working examples close to your problem, and how they test accuracy before launch. Check the security certification, who owns the code and data, and whether they will tell you when AI is not the right answer. At OZRIT the engineers who scope your project are the ones who build it, and we hold ISO/IEC 27001:2022 certification.
RAG lets an assistant answer from your own documents and systems, with the source shown, instead of from the model's memory alone. It cuts wrong answers, keeps answers current without retraining the model, respects who is allowed to see what, and usually costs less than fine-tuning a model.
We offer a support and MLOps plan that covers monitoring for drift and failures, accuracy checks against your test set, model and prompt updates, retraining when needed and cost control. A named engineer owns it, and response times are agreed in the contract.
You do. The code, prompts, pipelines and any models we build for you belong to you, along with your data. Third-party models stay under their providers' own terms, which we explain up front.
Tell us the job you want AI to do, even if you are not sure it is possible. A consultant will tell you plainly whether AI fits, what it would take and what a first release could look like.
