Document reading
Invoices, contracts, KYC: values extracted into the system, each read auto-confirmed or queued for review.
AI agents
Work that used to take a person a day.
An agent reads, researches, drafts and routes — and a person confirms before anything leaves the building.
What it does
What an AI agent does in an operation.
Research and briefs
Company research, financial checks, proposal drafts — assembled overnight, reviewed in the morning.
Routing and review
Nothing important is sent by a machine. The agent prepares; a person approves.
Reporting
Scattered information pulled into one place on a schedule.
Three jobs an agent takes off a person's desk.
Reading documents into the system.
Supplier invoices, contracts, KYC files arrive as PDFs and photos. The agent reads each one, extracts the values — amount, date, vehicle, party — and enters them into the record. Reads it is confident about are confirmed automatically; anything uncertain goes to a review queue where a person confirms in seconds instead of typing in minutes.
Research and briefs.
A prospect, a supplier, a company you're about to sign with. The agent gathers what's public — registration, directors, filings, presence — checks claims against more than one source, marks what it couldn't confirm, and writes a brief for the morning.
Drafting and routing.
Proposals, follow-ups, summaries of long threads — drafted from your records and your rules, then routed to the right person for approval. The agent prepares; a person decides.
How it works under the hood.
An agent is a sequence of steps with an AI model deciding inside each one, connected to your systems through their normal interfaces. We build each agent with three things a demo usually skips: a review step wherever a mistake would cost money; a log of every action, so you can see what it did and why; and a check that it actually produced what it claims — an agent can't report work it didn't do. It runs on a schedule or on a trigger, and it stops and asks when it's unsure.
The proof
Built and running.
Case study
Find, research, draft — a person sends.
A multi-agent pipeline built for a digital marketing agency.
Honesty
Where agents earn their cost, and where they don't.
Agents are worth it where work is frequent, varied and currently eats hours — reading unstructured documents, researching, drafting. They aren't worth it for fixed, repetitive steps plain software can do for nothing a month, or where a mistake is expensive and still needs full checking. We tell you which is which before you commit.
FAQ
Every output is logged with its source, and anything that matters passes a person before it's used. For document reading, you see the confidence on each field and confirm the uncertain ones.
Per document, per research run, per draft — an AI model priced per use. We work out the figure at your volume and give it to you in writing, with a cap.
Yes, through the same interfaces a person would use — your accounts software, your email, your operations system. Integration is scoped in discovery.
Have a workflow that's breaking? Let's look at it together.
Free discovery — we sit with your team and map how the work actually happens today. No charge, no obligation.
Or directly: +91 99874 29712 · hello@solvern.in