Answer
The agent answers from your live data — the same records your team sees, scoped to the same roles and permissions. If a person can't open a record, the agent won't use it to answer them.
An agent in your software. Not another tab.
Your team already works in one or two core systems. We put an agent in there — one that reads your live data, respects the roles and permissions you already set, and hands consequential work to a person before it goes anywhere.
Your team already uses AI. Just not where the work is.
They copy your data into a chatbot in another tab. It doesn't know your customers, can't touch your systems, and answers with the same confidence either way. The context lives in your software. The intelligence should live there too.
The context is stranded
Everything a model would need — the account, the history, the open ticket, the last invoice — is already in your app. The chat window only sees what somebody remembered to paste into it.
An answer is not the work
Even a good answer ends with someone returning to the app and typing it in by hand. The last mile stays manual, and that is where the time actually goes.
No permissions, no record
A general chatbot has no idea who is allowed to see what. There is no trail of what was asked, what was shared, or what changed as a result.
What you get
Answer. Act. Automate.
Three levels, in that order. Most teams start at the first and grow into the third once the agent has earned it.
Act
It creates, updates, drafts, and routes inside your existing UI. Consequential actions run through an approval step: the agent stages the work, a named person releases it.
Automate
Recurring multi-step work gets handed off — intake, triage, follow-ups, summaries — on a schedule or a trigger, and stays under human supervision the whole time.
From a mapping call to an agent your team actually opens.
- 1
Map the workflow
First weekWe sit with the people doing the work, follow one real task end to end, and write down where the answers actually come from today.
- 2
Ground the agent
Weeks two to fourWe connect it to your live data and mirror your roles and permissions, then test it against the questions your team asks every day.
- 3
Turn on actions
Once the answers hold upRead-only first. When the answers are trusted, we enable the actions your team asked for, each with an approval step where the stakes call for one.
- 4
Widen and tune
OngoingWe watch what people actually ask, correct what it gets wrong, and add the next workflow. It gets more useful because it gets corrected.
The difference
A chatbot in another tab vs. an agent in your software.
| What matters | Chatbot in another tab | Agent in your software |
|---|---|---|
| Context | Whatever someone remembered to paste in. | Your live records, read at the moment of the question. |
| Permissions | None. Everyone gets the same answer. | Your roles and permissions, enforced per person. |
| Can it act? | It can describe what to do. | It creates, updates, drafts, and routes. |
| Who reviews | Nobody, unless the user thinks to check. | A named person approves anything consequential. |
| Where the work happens | In a browser tab, then re-typed into your app. | In the screen your team already has open. |
| What you keep | A chat history in somebody's personal account. | A record of what was asked, changed, and approved. |
Both are called AI. Only one of them is inside the work.
Is this a fit?
Questions we hear about agents.
How does the agent get access to our data, and what can't it see?
It reads through your existing permission model, not around it. We connect it to the same records your application already serves, scoped per user — if a person can't open a record, the agent won't use it to answer them. Anything you want kept out entirely stays out, and that boundary gets written down before we build.
What can it actually do beyond answering questions?
Create and update records, draft messages and documents, route work to the right person, and run recurring multi-step tasks. Consequential actions are prepared rather than executed: the agent stages the work and a person approves it before anything leaves your system.
How long until something is in front of our team?
We start with one workflow, not your whole system. Mapping happens in the first week, and a grounded, read-only agent typically reaches a small internal group inside the first month. Actions get switched on after the answers have held up in real use.
What does an agent cost?
It depends on the workflow, which is why the mapping call comes first. An agent grounded in one workflow is a much smaller piece of work than the platform-wide version people imagine, and starting there is also how you find out whether it earns the next one. We scope that first workflow, fix the scope, and publish the date it lands.
Want an agent inside your software?
Tell us which workflow eats the most time. We'll map what an agent would do with it.