AI automation
Triage, lookups, reporting, routing, first drafts. If it eats hours every week and is done the same way each time, we connect agents to your ticketing, CRM and databases so it runs without anyone doing it by hand, with plain scripts wherever a model is not reliable enough.
| Engagement | Project · 3–8 weeks |
|---|---|
| Built for | Ops leads with manual processes |
| Price | Quoted on scope |
Your systems as tools
Your ticketing, CRM, database and internal services exposed as tools an agent can call, over MCP or direct integration. No brittle scripts scraping screens.
One assistant, not five
A single interface your team talks to, in Slack or wherever they already work, instead of everyone picking a different chatbot.
Scoped data access
Agents read your data through views that exclude what they should not see, so the boundary is enforced in the tool layer rather than by asking the model nicely.
Scripts where scripts win
Where a deterministic script beats a language model on reliability, we write the script and document why.
Most AI pilots stall because the agent cannot see your systems
The demo was never the hard part. A chat window wired to a model answers general questions well, then turns out unable to see a single ticket, order or customer record.
The value is in the connections. An agent that can read your ticket queue, look up an order, check a policy document and write back to the right system is useful on day one. One that cannot is a toy.
We start from the process, not the model
You name the internal work that costs the most time. We trace how it happens today and decide with you which steps go to an agent, which stay with a person, and which a plain script handles better.
Then we expose the systems involved as tools, through MCP or direct integration, and put one assistant in front of them. Access is scoped at the tool layer, so an agent cannot read fields it has no business reading.
Which work is worth automating
Internal work with a clear input and a clear output, done the same way every time. It pays off fastest on:
- Processes that cost hours every week and that nobody owns
- Knowledge spread across systems that nobody wants to stitch together by hand
- Teams each using a different model with no shared, trusted interface
What does it cost to maintain afterwards?
You get the ongoing cost in writing before you commit. Agents need maintenance when the systems around them change, so we document the tool layer and hand it over. An automation nobody can maintain is worse than the manual process it replaced.
What can you actually automate?
Internal processes with a clear input and a clear output: support triage, lookups across systems, recurring reports, routing and assignment, first drafts. If a person does it the same way every time, it is a candidate.
Will it make things up?
Any language model can. We ground agents in your own data, use plain automation wherever correctness matters more than flexibility, and mark in writing which steps of a workflow are safe to hand over and which stay with a person.
Does our data end up with a model provider?
It stays in systems you control. Each task sees only the fields it needs, and we document exactly what is sent to which model provider before anything is built.
Have you actually shipped one of these?
Yes. Sakurai is an in-house Slack-native agent platform with more than fifteen focused agents covering support, code review, release notes, moderation and analytics, built on Nest.js with company services exposed over MCP.
What if we already use an off-the-shelf tool?
Keep it if it covers the work. Building pays off when the value is in the connections to your own systems, which a generic tool cannot reach.
Which process costs your team the most hours?
Tell us what comes in, what goes out and who does it now. Within one business day we reply with what we would automate, what we would leave with a person, and what the first part costs.