AI in the engineering workflow, with a gate in front of it
We automate the recurring engineering work worth automating, choose models by measured quality and cost, and put evaluation and red-team gates in front of anything that reaches the codebase or your users.
A week in software engineering, before and after
- Mon 9:00The team tries an AI coding tool and argues about whether it helps.The evaluation set measures it on your own tasks and the scorecard says where it helps and where it does not.
- Tue 10:00A weekly release note is written by hand from the commit log.The release note is drafted from the merged changes with links, and an engineer approves it.
- Wed 14:00An AI feature ships and the first prompt-injection report comes from a customer.The feature is red-teamed before launch and the findings are ranked with mitigations.
- Thu 11:00Model costs surprise finance at month end.Models are chosen per step by measured quality and cost, with routing rules written down.
- Fri 15:00Data cannot leave the network, so AI is off the table.An in-house deployment on open-weight models is scoped, built and handed over.
What we build for software engineering teams
7 capabilities, each with its own page: the deliverables, the steps, and what is not promised.
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.
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 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 deliverablesCustom 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 deliverablesAI red teaming
Adversarial testing of an AI feature — jailbreaks, prompt injection, data exfiltration, tool abuse — with findings and mitigations you can act on.
See the deliverablesAI enablement workshops
Hands-on sessions that get a function from “tried the chatbot” to a working habit on its own material, with the guardrails included.
See the deliverablesR&D evidence capture for SR&ED
An evidence-capture practice for AI and automation builds — uncertainties, experiments, results — so a later SR&ED claim has a record to draw on.
See the deliverablesWhat 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
- Anything that reaches the codebase or your users goes through a gate your team controls.
- Red teaming is scoped and authorised in writing before any test runs.
- The TurnkeyGTM platform is not offered on-premise; in-house work builds on open-weight, self-hostable components.
How 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.
Questions we get asked
Teams software engineering works with
Engineering & leadership: Engineering, founders and compliance owners: the teams that decide how AI runs and where.
Data and analytics
Design the scoring models, the taxonomies and the pipelines that make the rest of the GTM stack's AI checkable.
Learn moreAI policy and governance
Decide what your team may use AI for, where the data may go, and how you would know if it went wrong.
Learn moreRevenue operations
Connect the record across CRM, calls and tickets so a figure in a report can be traced to the record it came from.
Learn moreTell us what your software engineering team does by hand.
A thirty-minute call is enough to know whether it is worth automating. Fixed price, agreed before we start.