AI agents
AI agents that do more than chat: they read documents, update your CRM or ERP, draft replies and prepare reports, with a person approving the steps that matter.
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AI agent development is about giving a language model access to tools so it can complete a task, not just answer a question. An agent can read an email, open the attached invoice, extract the supplier, amounts and GST details, check them against the purchase order in your ERP and prepare an entry for your accountant to approve. Each step uses a defined tool, and every action is recorded.
This matters because much office work sits between documents and systems. People read something, decide what it means and type the result somewhere else. That work is too varied for traditional rule-based automation, but it follows patterns an AI model can handle well when it is given clear instructions, limited permissions and a person to check its output.
An agent is not a replacement for good process. It works best where the task is already understood by your team and could be written down as instructions for a new employee.
The best first project is narrow, frequent and easy to check. We look for tasks where staff spend significant time on reading and re-typing, where mistakes are visible, and where a person can quickly review the agent's result. Starting this way proves the value with low risk and builds the integrations that later agents can reuse.
During discovery we sit with the people who do the task today, collect real examples, including the messy ones, and agree what a correct result looks like. That set of examples becomes the test suite the agent must pass before it goes live.
We also decide early how success will be measured, for example the share of documents processed without correction, the time saved per item or the reduction in backlog. These measures are recorded before the agent starts, so the comparison afterwards is fair and easy to explain to management.
An agent should never have more power than the task requires. We give each agent only the permissions it needs, such as reading invoices and creating draft entries but not approving payments. Steps that send money, message customers or change important records go through a human approval screen, where the reviewer sees what the agent proposes and why.
Every action is logged with its inputs and outputs, so you can audit what happened and improve the agent over time. We also track running cost per task, because AI model usage is charged by volume. Where data sensitivity requires it, agents can run with providers that do not train on your data, or with self-hosted models on your own infrastructure.
AI agents are only as useful as their connections to your systems, and that is where our background helps. We have built ERP, CRM, billing and workflow software for more than ten years for clients in India and abroad, so integrating an agent with Tally, a custom ERP, email or WhatsApp is familiar ground for us.
We are also candid about limits. If a task is better handled by simple automation, a form or a report, we will recommend that instead. You own the code and prompts we create, the work can be covered by an NDA, and we monitor and improve agents after deployment.
Agents are deployed in phases. They first run in a shadow mode where their output is compared with what your staff did, then move to producing drafts for approval, and only take on more autonomy for low-risk steps once the results justify it. This gradual approach keeps your team confident and your data safe.
What we deliver
Chatbots, AI agents, forecasting and fraud detection that put your data to work.
Identify repetitive, document-heavy work suitable for an agent.
Agents connected to email, CRM, ERP, spreadsheets and databases.
Extract data from invoices, forms, resumes and PDFs.
Approval steps before an agent sends, pays or changes records.
Permissions, limits and a full log of every action taken.
Agents measured against your own examples before they go live.
Usage and model costs tracked per task so spending stays predictable.
Hosted securely in your cloud or ours, with monitoring.
Who it’s for
Every project starts from your process, not a template. These are typical situations we are asked to solve.
Discuss your requirementHow we work
Find the tasks and decisions where AI saves real time or money.
Review the data you have, its quality and what is safe to use.
A working proof of concept on your own data, measured against today.
Connect it to your website, WhatsApp, CRM or ERP, with human review where needed.
Track accuracy and cost, and keep improving the model and prompts.
Technology
We pick the stack for your project, your team and your budget — not the other way round.
FAQ
Straight answers to what clients usually ask first.
A chatbot answers questions. An agent can also take actions across your systems, such as creating records or preparing documents.
We choose providers and settings that do not train on your data, and can use self-hosted models where required.
Yes. We recommend a single, well-defined task first and expanding once it proves itself.
High-impact steps need human approval, so errors are caught before they take effect. Every action is logged, and mistakes are used to improve the instructions and tests.
Build cost depends on the task, the systems involved and the approval flow. Running cost depends on volume and the AI model. We estimate both after discovery.
In practice agents take over repetitive reading and typing, while people keep the judgement and approvals. Most teams use the time saved for customer work and exceptions.
Speak directly with our technical architects. We provide actionable tech recommendations, architecture planning, and transparent timelines — with zero obligation.
