AI-Accelerated Delivery
A working prototype in days
Traditional builds make you wait a month to see anything, and by then the requirements have changed. Senior engineers working with AI coding agents put a working prototype in front of you in days, and every change passes tests and an engineer’s review before it ships. You get the speed without a clean-up bill later.
Book a free discovery callFree 30 minute call with the engineer who would do the work. You keep the first fix whether or not you hire us. Recent work includes Royal Canin, Cigna HK, Eukanuba and Circle Line.
Teams we have built data for
Royal CaninCigna HKEukanubaCircle LineGinmoniQuantiGrippiEnclave
The problem
01
Speed or quality
Traditional builds take months. Unreviewed AI output is quick and often wrong.
02
AI ignores your standards
Generated code skips naming conventions, tests and documentation, which adds technical debt.
03
A long wait for the first prototype
Stakeholders wait a month to see anything, and by then the requirements have changed.
A prototype in days and production code by the end of the sprint
Quality checks on every change
Each engagement type has a defined scope, a list of outputs and set quality checks. Agents draft the code, automated tests check it, and a senior engineer approves it before merge. Every step is logged.
What you get
- Working prototypes early in the sprint
- Consistent standards across every model
- Tests and docs written alongside the code
- A log of every change
- Quicker feedback rounds with stakeholders
What we deliver
What the engagement includes
Each engagement has a fixed list of deliverables, agreed before work starts.
Discovery
Stakeholder goals, a source audit and a scoped plan.
Full platform
Ingestion, warehouse, dbt, semantic layer and dashboards.
Dashboard-first
Start from the business questions and build the data model to answer them.
Pipeline and dbt
New sources modelled, tested and documented.
Dashboard extension
New views on an existing model.
Platform health check
Performance, cost and test coverage review with a prioritised fix list.
Our method
How the work runs
01
Generate
AI agents draft code to your standards.
02
Validate
Automated tests, linting and data checks run.
03
Review
A senior engineer approves each merge.
Tools we work with
ClaudeGeminidbtBigQuerySnowflakeLookerFivetranDagsterLinearNotion
Is this for you?
This works best if
- You need results this month
- You care how the work is done as well as what ships
- You want your own team to use the same method
Can our team use this method too?
Yes. Enablement is one of the engagement types. We set up the tools, standards and checks in your repositories and train your team to use them.
How we work
How an engagement starts
The work runs in three steps. You see working output within weeks and can stop after any stage.
01
Discovery call
A 30 minute call with the engineer who would do the work. We look at your setup, tell you what we would fix first, and whether you need us for it.
Day 1 02
Blueprint sprint
We map your sources, stakeholders and goals, then give you an architecture and a prioritised roadmap. You keep it whether or not we do the build.
2 to 4 weeks 03
Build in sprints
We ship working tracking, models, dashboards and agents every two weeks. You can stop after any sprint, and everything built stays yours.
Every 2 weeksWhat happens on the call
Book your call- 30 minutes with the engineer who would do the work
- No slides or sales pitch
- A clear answer on whether we can help, and a referral if we can’t
- The first fix we would make, which you can use either way
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