Hand Routine Tasks to an AI Agent That Uses Your Tools
We build AI agents that change delivery dates, update CRM records and finish other routine tasks in your own tools. Each one checks its work and hands off to a person when it is unsure.
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The problem
Where AI projects stop short
Most AI pilots can talk but cannot act. They answer a question, then a person still opens five tools to do the work. An agent closes that gap: it plans the steps, uses your systems and finishes the task, with checks on what it is allowed to do.
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“Our chatbot answers questions but can't do anything.”
The agent takes the next step itself: it looks up the record, makes the change and reports back.
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“One request touches five tools and three people.”
One agent works across the tools, so the request moves without hand-offs.
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“We tried a pilot and nobody trusted it to act.”
Permissions, checks and a person in the loop make it safe to let the agent act.
Routine work that crosses more than one system
Think of an order question that needs your store and your courier, repeated every day. These are the jobs we build agents for, and where we have done it.
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Customer and order questions
Looks up the order, stock and delivery records for each customer question, drafts the answer, and hands off to a person when the records cannot answer.
ProvisCommerce support assistant (a pilot) -
Answers from your own documents
Answers questions from your own files and records, so each answer rests on your data, not a guess.
PartnerVine RAG pipeline -
AI inside your platform
Lets AI assistants such as Claude read your system's data through an MCP server, a standard connector between AI apps and your data.
TrueWholesale MCP server -
Shopping and best-deal search
Finds the best price, coupon and card offer from one question, by text or voice.
Gyanee.ai -
Back-office work across tools
Moves a task through your CRM, helpdesk and ERP, and hands the exceptions to your team.
What we build
An agent with rules your team can read and edit
You get a working agent connected to your systems and the rules on what it may do. Tests prove it works, and screens let your team watch it.
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An agent with its own tools
Each tool does one job in your systems.
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Permission settings
A list of what the agent may read and change, which your team can edit.
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A test set of real questions
Real questions from your business with the answers you expect, ready to rerun after any change.
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A hand-off queue
Where the tasks the agent is unsure of land for your team, with what it found.
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Cost and usage dashboards
You see what the agent does and what it costs to run.
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Instructions and a handover for your team
Your team gets the code, the documentation and a walkthrough.
In production
Gyanee.ai: an AI agent that finds the best deal
Gyanee.ai is an AI shopping assistant we built. A shopper asks one question, by text or voice, and its agent finds deals, coupons and card offers, and knows which of the shopper's own cards to use. It works in 9 languages.
- Built
- An AI agent that asks a short question before it searches, checks offer expiry on every answer, and holds in-app voice conversations in Indian languages.
- Hardest part
- Savings claims that are never wrong. If the team was not sure a saving was real, it left the claim out.
How an AI agent works
An AI agent is software that uses an AI model, like the one behind ChatGPT, to plan the steps of a task, then carries them out with tools connected to your systems, such as a search, a database lookup or an update. It checks each result, answers, or hands the task to a person when it is unsure.
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Request
A person or a system asks for something.
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Plan
The model works out the steps.
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Tools
The agent reads or acts in your systems, only through the tools you allow.
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Checks
Results are checked before anything is shown or changed.
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Answer or hand-off
The agent replies, or passes the task to your team.
- Request “Where is my order? Can it come on Friday instead?”
- Plan Find the order, check the delivery options, change the date if it is allowed.
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Tools
Looks up the order: shipped, arriving Thursday -
Tools
Checks the delivery options: Friday is available - Checks The order belongs to this customer, and the change is within policy.
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Tools
Moves the delivery to Friday: done - Answer “Done: your order now arrives on Friday.”
Had a check failed, the agent would have passed the request to your team with what it found.
Why Provis Technologies
Built by a team that runs its own AI agent in production
Our own AI products are live, so we know what an agent needs once real people use it. We build yours on your systems, with checks before anything acts, and support it after launch.
View our work
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We ship live AI products
Gyanee.ai, which we built, and our own Chitra and ProvisCommerce are live on the App Store, Google Play and the web.
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Built around your systems
The agent works through the CRM, helpdesk, ERP or database you already run, instead of replacing them.
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Support after launch
We monitor the agent after launch, keep it updated and improve it as new models arrive.
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Hours that overlap yours
We work on India Standard Time (IST): 4.5 to 5.5 hours ahead of London and 9.5 to 10.5 ahead of New York.
Clear limits on what the agent may read and change
An agent is only useful if you can trust it to act. We set what it may read and change, test it before every release, and make it stop and ask when it is unsure.
On every agent we build
- Tools that read are kept apart from tools that act.
- A test set of real questions runs before each release.
- A person takes over when the agent is unsure.
- Usage and cost limits, with a log of what the agent did.
An agent that earns more work by proving itself first
We build a working agent on a sample of your data first, and grow it once it has proved itself. Each step is priced up front, and you can stop after the pilot.
The models and tools we build agents with
Standard, widely used tools, so another team could take the code over. We pick the model per task, on answer quality, cost and where your data may go, and we build so the model can be changed later without a rewrite. These are the tools our agents run on today.
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Models
OpenAI and Anthropic Claude, and open models on Ollama when data has to stay on your servers.
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Anthropic Claude
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OpenAI
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Ollama
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Model gateway
OpenRouter, so a model can be switched without rewriting the agent.
- OpenRouter
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Backend and data
FastAPI on Python, PostgreSQL with pgvector, Redis.
- FastAPI
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PostgreSQL
- Redis
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Voice
Sarvam AI.
- Sarvam AI
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Your systems
Your CRM, helpdesk, ERP, store or database, through tools we build for them.
Provis Technologies AI Integration services are phenomenal. We have been witnessing growth after the AI automation they have put into our business.
Send us one task your team repeats every week.
Tell us the task and the tools it touches. We will show you how an agent would handle it, and what to build first.
Vivek SharmaCEO & Founder, Provis Technologies
FAQ
Questions about AI agent development
What does an AI agent cost to build?
Three things drive the cost: how many systems the agent connects to, how much it may do on its own, and how ready your data is. Tell us what you want the agent to do, and within two working days we come back with an approach, a rough timeline and a rough number.
Which tasks are a good fit for an agent?
Tasks that repeat, follow rules you can write down, and touch systems other software can connect to, such as your store, CRM or database. Think answering order questions, updating records or preparing a report. Decisions that need judgement stay with a person, and the agent hands them over.
How long does a first agent take?
A pilot on a sample of your data comes first, so you see the agent work before a full build. Its length depends on the systems involved; we agree it at the discovery call.
Which AI models do you use, and can we switch?
OpenAI and Anthropic models, and open models on Ollama when data has to stay on your servers. We connect to them through a gateway such as OpenRouter, a layer between the agent and the AI provider, so the model can be changed later without rewriting the agent.
Where does our data go?
Your choice. With hosted models from OpenAI or Anthropic, data is processed by that provider. With open models on your own servers or cloud, documents and prompts stay inside your network.
What happens when the agent is wrong or unsure?
It is built to stop rather than guess: results are checked, a test set of real questions tests it before each release, and when it is unsure it hands the task to a person.
Are we locked in to one AI provider?
No. The agent talks to models through a gateway, so you can move to another provider or an open model without rebuilding it.
Do you build multi-agent systems?
Yes, when the job calls for it. A requirement with distinct jobs can be split across agents, such as one that plans, one that checks and one that acts, each with only the tools its job needs. When one agent is enough, we keep it to one: fewer moving parts are easier to test.
Can an agent work with the software we already use?
Yes. We build the agent's tools on top of the systems you run today, such as your CRM, helpdesk, ERP, store or database, so nothing has to be replaced. For TrueWholesale we added AI to its live ordering platform without rebuilding it.
How do you measure whether it works?
We agree how success is measured at the discovery call, then test against it: the test set scores the agent before each release, and the dashboards show what it does and what it costs once it is live.
Will you sign an NDA, and what access do you need?
Yes, an NDA before we start, on request. We work only in the accounts and systems you invite us into, with the access the agent needs and no more.
What does support cost after launch?
Support is priced up front like every other step, based on how much monitoring, testing and change the agent needs. You can also run the agent yourself: you own the code.
Who owns the code?
You do. You get the code, the documentation and a handover, and can run the agent yourself.
What if the pilot does not work?
You stop there and keep what was learned: what worked, what did not, and what it would take. You are not committed to a build.
Work with experts
Built for teams that need work done
We build custom AI agents that use your tools, check their own work and hand off to a person when they are unsure.
Prefer to talk directly?
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Emailsales@provistechnologies.com
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WhatsApp+91 88755 82124
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UK phone+44 7862 126522
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Video callBook 30 minutes with Vivek Sharma
We're on IST: 4.5 to 5.5 hours ahead of London, 9.5 to 10.5 ahead of New York.
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1
The agent
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Your details
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Additional info
Tell us what you want the agent to do
Describe the task, the tools it touches and who checks the work today.
Prefer to talk directly?
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Emailsales@provistechnologies.com
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WhatsApp+91 88755 82124
-
UK phone+44 7862 126522
-
Video callBook 30 minutes with Vivek Sharma
We're on IST: 4.5 to 5.5 hours ahead of London, 9.5 to 10.5 ahead of New York.