Deploying AI
AI deployed where it earns its keep:
your product, your operations.
We don’t sell a big transformation program. We start with one small feature, ship it with your team, and grow from there. Most of our engagements become long-term partnerships. This page shows the whole method. Nothing is saved for the sales call.
Phase 1 · Finding the first feature
Defining the right starting point for AI.
We help teams find where AI will create real impact: validating ideas, prioritising opportunities, and testing feasibility before any heavy investment. It ends with one small feature, chosen, not a strategy deck.
Does it solve a real problem
Use-case validation.
We check every AI idea against three questions: does it solve a real problem, is the value measurable, and can it be built in practice.
Across every department
Readiness interviews.
We interview people across departments to learn where AI will actually help. It doubles as an honest snapshot of how your teams work today.
Workflows and data
Opportunity mapping.
We map your workflows and data to find where AI can improve efficiency or decision making. Some problems just need a simpler fix; we say so.
Test before you build
Rapid prototyping.
We prototype the promising ideas early and put them in front of real users, to test assumptions before committing to a full build.
“One small feature, live in production. Everything else grows from there.”
Phase 2 · Execution pathways
Choosing the right path to deploy AI.
Once priorities are set, we move from plan to action: building AI into the product your customers use, or embedding it into the operations your teams run. Most companies eventually need both. We start wherever the first feature is most obvious.
AI your customers touch.
For launching AI-powered features your customers use. We take validated ideas and turn them into production-ready software: design, engineering, and support after launch.
What this includes
Product & UX design
We define scope, user experience and system behaviour, so the feature is usable and people trust it. We agree what a good result looks like before we build.
e.g. acceptance checks built from real support tickets
Engineering & AI integration
AI built into the core of the product, not bolted on, by senior engineers embedded in your team and your codebase.
e.g. retrieval pipelines, model call paths, guardrails and fallbacks
Post-launch support
After launch we stay involved to improve, iterate, and hand the feature over to your team.
e.g. runbooks, on-call handoff, quality checks that improve with real usage
AI your teams run.
For embedding AI into internal operations. We work alongside your teams to put AI into the workflows, tools and decisions they rely on: education, process redesign, and systems the team owns.
What this includes
Education
Cohorts, not workshops: a peer who has already made the switch, a real problem from your backlog, and the time to ship it properly.
e.g. small cohorts, Claude Code for non-technical teams
Enablement
Workflow automations and honest build-vs-buy calls, so you get the value without overbuilding.
e.g. automation builds, platform selection, vertical SaaS vs custom analysis
Engineering
Custom internal tools the team owns end-to-end, with a trained champion in every function.
e.g. built on Pace, our open-source framework, for finance, legal, marketing, ops
Functions we’ve put AI in front of
Making it stick
The first feature is the start, not the whole story.
A feature in production with no one to own it is a demo with uptime. Alongside every build we train the team, work side by side with your engineers, and put every architecture decision on record with the option we rejected.
Most clients keep working with us well past the first feature: new features, new teams, new functions. We grow with the work instead of selling a big bang up front, and your team gets stronger the whole way.
Read the full engagement walkthroughWho we serve
Built for teams that need AI to work.
We partner with teams across consumer products, enterprise software, finance and industry: from products millions of people use to the environments where “move fast” needs an audit trail.
Enterprise & B2B SaaS
Production AI features and internal systems that change how large organisations decide and ship, without breaking the audit trail.
Consumer & B2C products
AI features in products real people use every day: search, personalisation, support. Fast feedback loops and costs that have to make sense at scale.
Financial services
Diligence, portfolio operations, risk and reporting. AI that integrates across the workflow and stays defensible to a regulator.
Legal-heavy & industrial
Contracts, compliance, logistics, tenders. The paperwork-dense middle of the economy, where AI pays rent fastest.
Security & data
Your data, handled like it’s yours.
Working in regulated environments means the boring parts are the job. This is how we operate by default, before anyone asks.
Your environment.
We work inside your repositories, your cloud and your access controls. Access is named, scoped and revocable.
No training on your data.
Model providers are configured so your code and your data are not used to train their models.
EU by default.
Goldenberry is an EU company. NDAs and data-processing agreements are part of getting started, not an afterthought.
Everything on record.
Who has access, every architecture decision, and the options we rejected: written down and handed over.
Our work
Proof of AI in production.
Most of the client list sits under NDA, which is what you’d hope from people who work in regulated environments. Ask us anything in the working session; we’ll name what we can.
Financial services · Berlin
Bringing senior engineers over to AI.
Inside an embedded build at a fintech, we paired with principal engineers who had never gotten traction with AI tools. By the end they were shipping model-driven features on their own.
Advisory · Readiness benchmark
From “what is our AI strategy” to a ranked plan.
Interviews across engineering, operations and leadership, five dimensions scored separately, concrete moves per dimension. The scorecard went to the board; the action list went to the team.
Our products
Products we’ve built to solve real problems.
Software we build and ship ourselves, to validate ideas and sharpen how we work before any of it reaches a client. Thousands of people use these every day.
Meet all eight products in the Studio
The thinking behind this
All articles
Field note · 4 min
The board wants AI. The team is exhausted. Now what.
A short pattern for engineering leaders caught between a mandate and a morale dip.
Field note · 8 min
How we structure a 12-week AI engineering engagement.
A walkthrough of what gets done in week one, week six, and the hand-off.
Field note · 9 min
A five-dimension framework for AI readiness.
Why a single 'AI maturity score' is useless, and what we assess in a one-week benchmark instead.
The first step
Thirty minutes, free. A working session, not a pitch.
You bring the context, we bring what has worked elsewhere, and we pick the first feature together. If it fits, that feature becomes the start of the engagement.