The same manual work, every week
People pull the same inputs, apply the same rules, build the same output, and chase the same approvals.
AI automation services for work your team still does by hand.
We pick one process, connect the systems it runs through, and test it with the people who do the work. Then it goes live.
Teams we've built for
One process
One result you can measure
Start where the cost, delays, errors, or missed revenue are easy to see.
4–8 wks
Typical first release
Most first releases reach users 4–8 weeks from kickoff.
Your repo
Code your team can run
Keep the system and the runbook, or keep us on after launch.
Similar work, already live
“We'd been putting off automating our reporting for ages and they had it done in about three weeks.”
“They rebuilt our onboarding, which was half manual and half Zapier flows that kept breaking, and support tickets went way down after that.”
“Honestly the main thing is our inventory numbers are actually right now. It all used to be typed in by hand, now the scanners feed straight into the system.”
Founded by Colin Burke in 2018.
What an AI automation agency should actually deliver
A model call is the easy part. A working automation also needs approved data, permissions, integrations, business rules, evaluation, error handling, and a place in someone's actual job.
We start where the cost or delay is obvious. Systems that work stay. Weak data or handoffs get fixed only as far as this workflow needs.
Start with one process where the result is obvious
A good first process repeats, costs enough to notice, and has an owner who can tell good output from bad.
Data moves by copy and paste
Key details travel through spreadsheets, email, or one person's memory.
The demo never made it into the job
There's a model or a chatbot, but it never got the permissions, data, evaluation, review steps, or integrations it needs for daily use.
Off-the-shelf tools stop short
They handle the common path. Your rules, integrations, and awkward cases still land back on the team.
From one good use case to a controlled release
The system only gets more to do after each step checks out.
- / 01
Measure the current work
Count the hours, errors, delays, and missed revenue. Name the person who owns the result.
- / 02
Decide what needs AI
Plain software handles what it can. A model does the parts that need one. A person keeps the decisions that matter.
- / 03
Build the whole path
Connect the data and tools, build what users touch, test real cases, and make failures visible and easy to recover from.
- / 04
Put it into the job
Release to a small group, fix the gaps, and write down support, running costs, and who owns what.
We build everything around the model too
We do the product and engineering work it takes to turn one use case into something people use every day.
- A workflow map, a baseline, and a clear production scope
- AI agents with tools, permissions, review steps, and versioned evaluations
- Automation for intake, routing, reporting, approvals, and handoffs
- APIs and integrations across your systems and model providers
- Internal tools, dashboards, and customer-facing product work
- Deployment, monitoring, audit trails, a runbook, and launch support
Use the simplest design that works
Sometimes that's one model step. Sometimes it's an agent, an integration, a small internal tool, plain automation, or a mix.
Keep the parts of your stack that work
Your ERP, CRM, warehouse, support desk, or internal database stays. We plug the workflow into what's already there.
Fix only what blocks the work
Build the smallest data or workflow layer the job needs. One automation shouldn't turn into a company-wide replatform.
Four first projects with clear edges
Narrow projects are easier to test on real cases, easier to adopt, and cheaper to change.
Recurring reports
Collect the source data, apply your rules, build the report, and flag anything unusual.
Client or vendor onboarding
Documents, tasks, approvals, messages, and exceptions in one flow everyone can see.
Document processing
Read incoming files, check the required fields, update the system of record, and send unclear cases to a person.
Focused internal tools
One place for the information, actions, approvals, and history the team needs to finish the job.
Three fixed-price ways to start
Start with something real users can try. Then decide if it's worth going bigger.
Sprint
1 week$5,000
Map the process and ship a narrow working slice or proof in your repository.
MVP
~1 monthFrom $20K
One automated workflow, end to end, with design, integrations, deployment, and handover.
Scale-up
2+ months$75K–$250K
A dedicated team for automation across several systems, teams, or higher-risk operations.
Questions before we start
How do you pick the first process to automate?
We look for repeated work with a visible cost, usable inputs, and an owner close enough to the job to judge the output. We also check whether plain automation can do it without AI.
Will you replace our existing systems?
Only if a missing or weak system blocks the result. If yours work, we connect to them and leave them alone.
How much does AI implementation cost?
A one-week sprint is $5,000. An MVP starts at $20,000. Larger builds across several systems usually cost $75,000 to $250,000. We agree the price before we start.
Which AI providers do you work with?
Claude, OpenAI, Gemini, smaller or self-hosted models, and plain automation. We pick based on quality, privacy, speed, and cost.
What happens after launch?
Your team gets the code and the docs. We can hand over fully, or stay on to monitor, maintain, and improve it.
How do you handle security and human review?
Before it touches live work, we set the data boundaries, permissions, review steps, escalation paths, logs, and stop conditions.
galactus / contactaccepting projects
Pick the job you'd automate first.
Tell us what repeats, which systems it touches, and where it gets stuck. We'll tell you where we'd start.