AI automation
We take the internal work that eats hours every week (triage, lookups, reporting, routing, drafting) and wire agents into your real systems so it happens without anyone doing it by hand.
| 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.
An honest verdict
Where a deterministic script beats a language model on reliability, we write the script and tell you why.
The problem with most AI projects
They stall after the demo. Someone wires a chat window to a model, it answers general questions impressively, and then it turns out it cannot see a single ticket, order or customer record. The demo was never the hard part.
The work 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.
How we build it
We start from the process, not the model. You tell us which internal work costs the most time, we trace how it happens today, and we decide together which steps are worth handing to an agent and which should stay with a person or a plain script.
Then we expose the systems involved as tools an agent can call, through MCP or direct integration, and put one assistant in front of them. Access is scoped at the tool layer, so an agent physically cannot read fields it has no business reading.
Where it pays off
- Processes that quietly cost hours every week and 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 it costs you afterwards
Agents need maintenance when the systems around them change. We document the tool layer, hand it over, and are explicit about the ongoing cost before you commit, because 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, data lookups across systems, recurring reports, routing and assignment, first-draft writing. If a person does it the same way every time, it is a candidate.
Will it make things up?
Any language model can. We reduce it by grounding agents in your own data and by using plain automation wherever correctness matters more than flexibility. We will tell you which parts of a workflow are safe to hand over and which are not.
Where does our data go?
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 built this before?
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?
Then we will probably tell you to keep it. This is worth building when the value is in the connections to your own systems, which is exactly what a generic tool cannot reach.
Tell us the one thing it has to do.
If it's sharp enough to prove in a week, we'll say so and give you a date. If it isn't, we'll tell you that too, along with what we'd build first instead.