AI Agent Development Company
AI agents that plan a task, use your tools and finish the work, with approval steps and a complete record of what they did.
OZRIT builds AI agents for sales, support, finance and operations teams. Each agent is scoped to one named process, connected to your systems through narrow permissions, and tested against real cases before it acts on its own.
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 agent development, explained
A chatbot answers. An agent does the work and tells you what it did.
An AI agent is software that uses a language model to plan and carry out a multi-step task. Instead of replying with text, it can read an email, look up an order, update a record, draft a response and ask a person to approve the one step that carries risk.
Agentic AI development is mostly engineering around the model: clear goals, tool permissions, memory of what has happened so far, limits on cost and actions, and logs that show every step. OZRIT's AI engineers and software developers build those parts together, so the agent runs inside your real workflow rather than in a demo.
We start with one process that is repetitive, structured enough to test and valuable enough to matter, such as invoice matching, lead qualification or ticket triage. When it works and the team trusts it, we add the next process, reusing the same tool connections, approvals and monitoring.
- Agent design and scoping
- Tool and API connections
- Planning and reasoning workflows
- Multi-agent systems
- Memory and context
- Human-in-the-loop approvals
AI agent development services we offer
Everything needed to run an agent safely in a real business, not only the prompt.
Agent design and scoping
We pick the process, define what the agent may and may not do, and agree how success will be measured.
Tool and API connections
The agent gets narrow, audited access to email, CRM, ERP, calendars, databases and internal APIs.
Planning and reasoning workflows
Goals are broken into steps, each result is checked, and the agent retries or escalates when something fails.
Multi-agent systems
Work is split between specialist agents, such as a researcher, a drafter and a reviewer, under a coordinator.
Memory and context
Short-term and long-term memory so the agent remembers the case, the customer and earlier decisions.
Human-in-the-loop approvals
Costly, external or irreversible actions go to a person with the full context to decide quickly.
Guardrails and limits
Spending caps, rate limits, allowed-action lists and content filters stop an agent going off course.
Evaluation and observability
Test suites built from real cases, plus a trace of every step, so you can audit and improve behaviour.
How we build an AI agent
Six steps from a named process to an agent you can trust with real work.
Build and prove
First agent in 4 to 8 weeks
- 1
Choose the process
We pick one repetitive process, map its steps and exceptions, and set the measure of success.
- 2
Define tools and permissions
We list every system the agent needs and give it the narrowest access that works.
- 3
Build the agent loop
We build planning, tool calls, memory and error handling, with limits on cost and actions.
- 4
Test on real cases
We replay historical cases and compare the agent's work with what your team did.
- 5
Run with approvals
The agent works alongside your team, with a person approving each consequential step.
- 6
Widen autonomy
As accuracy holds, we reduce approvals step by step and add the next process.
AI agent use cases
Where agents save the most time today.
Sales operations
Qualify inbound leads, enrich records, draft follow-ups and book meetings.
Customer support
Triage tickets, look up orders, draft replies and process refunds within set limits.
Finance and procurement
Match invoices to purchase orders, chase approvals and flag exceptions for review.
HR and recruiting
Screen applications, schedule interviews and answer routine policy questions.
IT and internal helpdesk
Handle access requests, route incidents and summarise logs for engineers.
Research and reporting
Gather information from approved sources and draft summaries with citations.
AI agent 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.
Fintech app development
Fraud signals, credit scoring, document checks and support assistants for lenders, payments and banks.
Logistics app development
Route planning, ETA prediction, proof-of-delivery checks and exception alerts for fleets and supply chains.
Healthcare app development
Record summaries, triage support, scheduling and personalised plans for providers and health-tech teams.
Retail and eCommerce app development
Product search, recommendations, demand forecasting and support assistants for stores and marketplaces.
Education app development
Tutoring assistants, content generation, assessment help and engagement analytics for learning platforms.
Real estate app development
Lead qualification, property search, valuation support and document handling for developers and brokers.
Frameworks and models behind our agents
We choose the framework and model for each project on reliability, cost and where your data may go.
Agent frameworks
Chosen per project
- LangGraph
- LangChain
- LlamaIndex
- OpenAI Agents SDK
- CrewAI
- Microsoft AutoGen
Models
Chosen per task, cost and privacy need
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Hugging Face models
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 agent development
An agent is priced by how much it touches and how much it is trusted to do alone.
Tools and systems connected
Each system the agent reads or changes adds integration and testing work.
Level of autonomy
More independence needs more testing, tighter limits and better monitoring.
Accuracy and safety targets
Stricter targets mean larger test sets and more rounds of tuning.
Task volume and model use
Running cost depends on how many tasks run and which models handle them.
Security and hosting
Private cloud or on-premise deployment costs more than public APIs.
Monitoring and support
Tracing, evaluation and fixes after launch are agreed as a monthly plan.
Security and privacy in AI agent 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 agent development: frequently asked questions
Straight answers on cost, timelines, data, security and ownership.
What is an AI agent?
An AI agent is software that uses a language model to plan and complete a multi-step task. It can read inputs, use tools such as your CRM or email, check its own results and ask a person for approval, rather than only producing text.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions in a conversation. An agent takes actions across your systems to finish a task, such as updating a record or sending a draft for approval. Many projects use both: a chatbot as the front door and agents behind it.
How much does AI agent development cost?
The cost depends on how many systems the agent connects to, how much it may do without approval, and the accuracy you need. A discovery sprint from $1,000 tests the process on your own data and ends with a scoped quote.
How long does it take to build an AI agent?
A first agent for one process usually takes 4 to 8 weeks. Adding more processes, deeper integrations or stricter approvals takes longer, and we agree the plan before you commit.
Are AI agents safe to run on business systems?
They can be, with the right controls. We give agents narrow permissions, allowed-action lists, spending limits and approval steps for anything costly or irreversible, and we log every step so actions can be audited and reversed.
Which processes suit an AI agent?
Processes that are repetitive, have clear rules for most cases, use several tools and are easy to measure suit agents well. Invoice matching, lead qualification and ticket triage are common starting points.
Which frameworks and models do you use?
We work with frameworks such as LangGraph, LangChain, LlamaIndex and the OpenAI Agents SDK, and with models from OpenAI, Anthropic, Google, Meta and Mistral. We pick on reliability, cost and data privacy.
Can an agent work with our existing software?
Yes. Agents connect through APIs, webhooks or database access, so your ERP, CRM, helpdesk or internal tools stay as they are. Where a system has no API, we add a small service in front of it.
How do you test an AI agent before it goes live?
We build a test set from real historical cases, replay it against the agent and compare results with what your team did. We then run the agent alongside your staff, with approvals, before reducing oversight.
Will AI agents replace our staff?
Agents take over repetitive steps so people can focus on judgement, exceptions and relationships. We design for people to stay in control, with clear hand-offs, rather than for removing them from the process.
Who owns the agent we build?
You do. The code, prompts, tool connections and documentation belong to you. If you build on a third-party model, its provider's terms apply to that part, and we walk you through them first.
Build your first AI agent
Tell us the process you want an agent to handle. We will say whether an agent fits, which steps need approval and what a first release could look like.
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.