AI workflow automation
The workflow your team repeats every week, automated with AI where it helps and plain software where it does not, run beside your current process until you trust it.
What you get
Each deliverable names the format it arrives in. These are the formats the stack produces today.
Workflow design
Document
The automation
Running and documented
Run-alongside period
Agreed length
Handover documentation and optional upkeep
Monthly
How it works
- 1
Scope one workflow
The thing your team does by hand every week, and where its information lives.
- 2
Build and run alongside
The automation runs next to the manual process until the outputs match.
- 3
Hand over
Documentation, then optional monthly upkeep as your tools change.
Where it fits
The teams whose pages list this capability.
What runs where
On the platform
- Cascades, artifacts and delivery run on the platform where they fit
Built for you
- Connectors and plain-software steps built for the engagement
Where it can run
What stays under your control
The promises that hold on every engagement, and the ones we do not make on this one.
On every engagement
- Outputs cite the document or conversation they came from, so a person can check before acting.
- A person approves anything that reaches a customer or writes to a system of record.
- Delivery is Slack, email or a document you own. Other channels are not offered.
- Fixed price, agreed before we start. Nothing numeric is published on this site.
The work runs on the TurnkeyGTM platform where it fits, and on plain software where it does not.
Not promised here
- A person approves outputs that reach customers or records.
- Connectors are scoped per engagement; we say what is reachable before we quote.
Try a piece of it
Free tools from the TurnkeyGTM ecosystem that do a small part of this job in the browser. No account needed.
Questions we get asked
More in automation & engineering
Custom hosted and on-premise AI
AI deployments designed for where your data has to live — a dedicated hosted environment or your own hardware — on open-weight models and self-hostable components.
See the deliverablesModel selection and cost optimization
Which model, at which size, for which step — chosen by measured quality and cost on your own tasks rather than by the leaderboard.
See the deliverablesAI output evaluation and quality gates
Frozen scorers and checks that decide whether an AI output is good enough to ship, so quality is measured rather than eyeballed.
See the deliverablesHow an engagement runs
One call to scope it, one fixed price to approve, one build you can trust before it goes live, then a handover.
The full process- 1
Scope the work
A thirty-minute call on what your team does by hand every week, where the information lives, and who needs the output.
- 2
Fixed price, ROI up front
You get a written proposal with a fixed price and an honest estimate of the hours it gives back.
- 3
Build beside your process
We design and build the automation and run it next to the manual version until the outputs match and you trust them.
- 4
Hand over, keep it running
Documentation and runbooks so your team owns it.
Tell us where this fits in your week.
A thirty-minute call is enough to scope it. Fixed price, agreed before we start.