AI agents for data & analytics · Helsinki
AI agents for your data.
Set up in your systems.
We build data-engineer agents that work inside your environment. They run under the same roles, approvals and CI/CD as any engineer. And we teach your people to work with them.
What we do
We set up AI agents for data and analytics work inside your own systems, and teach your organisation to operate with them.
01
Agents in your systems
The agents run inside your environment, not in a sandbox beside it. Each one gets a named role, scoped network access, and a human approval before anything privileged happens. Every action it takes is logged.
- Named roles and scoped access
- Approval before privileged actions
- CI/CD deploys, full audit trail
02
Data and analytics agents
Data-engineer agents first: pipelines, modelling, data quality. Analytics agents follow, once the numbers underneath them are trustworthy. All of it stands on data platforms we design and run ourselves.
- Pipelines, modelling, data quality
- Analytics and reporting agents
- Data platforms, RAG and context layers
03
Operating with agents
Agents change who approves what and how work is tracked. We design that operating model with you and train the teams that live with it, so the agents keep delivering after we leave.
- Approval paths and role design
- Agent work tracked in your own tools
- Enablement for the teams alongside them
An agent at work
A data-engineer agent, inside an enterprise.
Roles to approve, a firewall to open, an IT ticket to wait on. The agent carries the work through all of it, and deploys through CI/CD like anyone else.
Morning. We need customer orders from the ERP into Snowflake, refreshed daily by 07:00. Can you take this?
I'm on it. Give me a moment to map the ground.
Plan is ready, details below. The source contains PII: it lands in the warehouse's restricted zone, and a masked version goes to the shared area for general use. For development I'll generate synthetic test data. To test the source connection I need to assume the role di-connect-test. Do you approve?
plan · erp-orders → snowflake
- incremental extract · ERP OData API
- landing zone · S3 + schema checks
- modelling · dbt into the warehouse
- PII → restricted zone · masked data → shared area
- synthetic test data for development
rolesdi-connect-testdi-devopsdi-data-developerdeploy via CI/CD
see the full plan → DI-142
Approved.
Connection blocked: the firewall drops traffic from the integration subnet to erp-prod:443. IT can open it via the self-service portal. Shall I file the request, or will you order it?
File it.
FW-4821 is in IT's queue. I'll pause here and resume the moment the rule is live.
The firewall rule is live. The 1 h approval has expired, so I need di-connect-test again to verify the connection, and di-devops for the scaffolding. The deploy itself goes through CI/CD. Approve and I start now, or schedule it?
Approved. Start now.
example scenario · real operating model
Want agents like this in your systems?
Book a meetingWho sets it up
I founded Aikidai in 2022 as a freelance business, with the usual plan: build a client base, hire, and grow into a data engineering agency like any other.
That plan is no longer needed. The work I would have hired people for is now done by AI agents: pipelines, modelling, data quality, platform automation. They run under the same roles, approvals and CI/CD as any engineer, and I operate them every day in real enterprise environments.
So that is what I do now. I build data and analytics agents, set them up inside your systems, and teach your teams to operate with them. The person who scopes your setup is the person who builds it, and the way of working you get is the one I run myself.
Let's Talk
Tell us which data work you want an agent to take on. We'll tell you what it needs.