An agent that does one job properly beats one that promises everything
We build agents around a job you can describe: draft this quotation from the price list, check this document set against the specification, flag the entries that do not reconcile. Narrow scope is what makes them reliable enough to use.
What you get
- Model
- FR-D820S-0.4K
- Capacity
- 0.4 kW · 2.5 A
- Quantity
- 3
Source: Manufacturer datasheet, page 12
Illustrative — not a customer's data
Grounded in your data
The agent answers from your price lists, datasheets, ledgers and documents — not from general knowledge. If the answer is not in your data, it says so instead of inventing one.
A checkable trail
Every output points back to the records it came from, so a person can verify it in seconds. An answer nobody can check is not usable in a business that has to defend its numbers.
A human gate where it matters
Anything that leaves the building — a price, a commitment, a message to a customer — waits for someone to approve it. The agent prepares; a person decides.
- Model
- FR-D820S-0.4K
- Capacity
- 0.4 kW · 2.5 A
- Quantity
- 3
Source: Manufacturer datasheet, page 12
Illustrative — not a customer's data
What it gives back
How it works today
A quotation waits two days because the one person who knows the price list and the datasheets is travelling.
With AIOSOL
The draft is ready in minutes with its source cited, and a person approves it. The bottleneck moves from writing the quote to deciding on it.
Time
The work behind it
We run an agent-assisted quotation workflow on our own industrial-equipment business: it reads authentic manufacturer datasheets, drafts the offer against a specification, and a guard check refuses to let a specification claim through that the datasheet does not support. Nothing is sent without a person approving it.
Where we say no
We do not build agents that send money, sign commitments or message customers without a human approving each one. We also do not promise an agent that handles anything you throw at it — if a job cannot be described clearly enough to check the output, it is not ready to be automated.
Frequently asked questions
A chatbot answers questions. An agent completes a task — it reads the inputs, produces a draft or a decision, and hands it over. Many businesses want the second and are sold the first.
By grounding it in your records and by checking the output against them before it is shown. In our own quotation workflow a claim that the source datasheet does not support is blocked outright — that check is part of the build, not an afterthought.
No, but an agent is only as good as the data it can reach. If your records live in scattered spreadsheets, the useful first step is usually getting them into one place — which may or may not mean an ERP.
Related services
Not sure which one you need?
Tell us the problem you are trying to solve. We will say which service fits — or that none of them does.