Workflow map
The task broken into steps, with what the agent may do alone and what needs a person.
AI agents & automation
AI agents that don’t just chat — they finish the work.
Assistants wired into your inbox, CRM, documents and internal systems — tested against real past work, with a human approving anything risky.
- PilotReal data, in weeks
- CoreLLMs · RAG · tool use
- QualityEval suites
- ControlHuman approval
Bright Infonet builds AI agents that do real work inside your systems: reading emails and documents, looking up answers with RAG, updating tools like your CRM or calendar, and asking a person before risky actions. Every agent ships with an eval suite built from your real cases and monitoring for cost, accuracy and failures.
An agent is only useful if you can trust what it does. So we start from a narrow, repetitive task — triaging tickets, extracting data from documents, drafting replies — and measure how well the agent handles real past examples before it touches live work.
Under the hood we combine LLM APIs with retrieval over your own documents and tool calls into the systems you already use. Guardrails limit what the agent may do, risky steps wait for human approval, and every action is logged so you can see why it happened.
We work with teams across India and worldwide, and with businesses near Panchkula, Mohali and Chandigarh who prefer to map workflows face to face. A pilot on real data usually runs in weeks, not months.
What you get
An agent you can measure and trust.
RAG over your documents
Manuals, SOPs, policies and past tickets indexed so answers cite the source they came from.
Tool integrations
Secure connections to email, calendar, CRM, helpdesk, Slack or your own APIs.
Eval suite
A test set built from real past cases, run on every change so accuracy never silently drops.
Human approval
Review screens for risky or unusual actions, with the agent’s reasoning and sources shown.
Cost & quality monitoring
Dashboards for token spend, latency, error rates and eval scores, with alerts when they drift.
Where agents help
Hours of busywork, handed off.
Inbox triage
Sort, summarise and route incoming emails, and draft replies for a person to send.
Document extraction
Pull fields from invoices, forms, reports or case files into structured records.
Knowledge assistant
Staff ask questions in plain language and get answers from your own documents, with citations.
Support deflection
Answer routine customer questions and hand complex ones to your team with context attached.
Data entry between tools
Move information between systems that don’t talk to each other, with a log of every change.
Case pre-review
Read each case first and pre-fill fields, as we do in PVgenix, so reviewers never start from zero.
Is this for you?
A good fit if…
- People on your team spend hours on inboxes, tickets or documents every day.
- Answers are buried in manuals, SOPs and policies that staff struggle to search.
- Staff copy the same data between tools that don’t integrate.
- You want AI that is tested and monitored, not a demo that breaks on real inputs.
FAQ
Common questions.
What is an AI agent for business?
It is software that uses a language model to read information, decide the next step and act in your tools — such as updating a CRM or drafting a reply — within limits you set.
How do you stop an AI agent from making mistakes?
We test it against real past cases with an eval suite, restrict which actions it can take, and require human approval for risky steps. Monitoring flags drops in quality.
What is RAG and do I need it?
Retrieval-augmented generation lets the model answer from your own documents instead of its general training. You need it when answers must match your policies, products or records.
How much does it cost to run an AI agent?
Running cost depends mostly on volume and model choice. We estimate it during the pilot and track token spend on a dashboard so there are no surprises.
Is our data safe with an AI agent?
We use provider settings that exclude your data from model training where available, limit access by role, and keep logs of what the agent read and did.
How long does an AI agent pilot take?
A focused pilot on real data usually takes a few weeks, ending with eval results you can use to decide whether to roll it out.
Keep reading
Related pages.
Have a project in mind?Talk to an engineer →