We don’t sell products.
We build what your P&L
is asking for.
Goliath is a forward-deployed engineering company: fluent in AI, multilingual in everything beneath it — cloud, data, design, experience, finance and product. Every client starts with the same Sprint. Then we build whatever it proves is worth building.
AI Value Sprint
Three weeks inside your operation to find where AI pays, and what to build first.
You leave with
- →The one metric worth moving, and who owns it
- →A baseline your finance team has signed
- →The first build, scoped and ready to start
Typical · 2 to 3 weeks
Every engagement begins here. A fixed scope, a fixed duration, and a number your CFO recognises. Three weeks later you know where the value is, what it is worth, and what to build first.
- Duration
- 2 to 3 weeks
- Team
- A partner and an engineer, embedded with your metric owner
- Output
- The metric, the baseline, the first build
- Commitment
- Fixed scope · Fixed duration
One method.
Four steps.
The same discipline on every build: find the number, redesign the work, build the system, prove the result. The method is fixed so the output can be whatever your problem needs.
01
Sprint
We start where the money is. Not “where can we use AI?” but which metric, owned by which leader, is underperforming — and what it would take to move it. Three weeks later you have the number, the owner and the first build.
- —Sit with the metric owner and the people who run the work
- —Sample the data and the workflow as they actually are
- —Agree the baseline with finance
- —Set the team, the cadence and the end date
- —The metric worth moving, and who owns it
- —A baseline your finance team has signed
- —The first build, scoped, with success and kill criteria
02
Design
Before a line of code, we redesign how the work happens. Most AI projects fail here: a model bolted onto an old process, making predictions nobody acts on. We rebuild the decision loop first — who sees what, when, and what they do next — so the system has a job the moment it goes live.
- —Map the workflow as it runs today: timing, owners, handoffs
- —Find the decision moments AI actually improves
- —Redesign the work with AI in its proper place
- —Validate it with the operators who will run it
- —The before-and-after workflow, on one page
- —A specification for every decision moment: input, output, owner, cadence
- —The process changes that go first, before any software
03
Build and embed
Now we build — whatever the design calls for. A decision cockpit, an automated workflow, an agent, a data foundation; often several at once. It goes live inside the redesigned workflow, not in a demo, with the baseline metric instrumented from day one.
- —Build only what the design calls for
- —Wire it into the workflow and the operating cadence
- —Instrument the baseline metric inside the system
- —Document in your repository as we go
- —A working system in production
- —Cockpits, automations or agents — as the problem requires
- —A live view of the metric you are paying to move
04
Measure and transfer
The step everyone skips. We measure the effect on the baseline over an agreed period and sign the result with you. If the metric moved, we say so with the number. If it didn’t, we say that too. Then we make sure your team runs it without us.
- —Measure the metric over the agreed post-launch window
- —Adjust for seasonality and other known changes
- —Train your team to operate and evolve the system
- —Sign the value report together
- —A measured result against the baseline
- —A one-page value report, signed by both sides
- —A team that owns the system
- —The next build, if there is one worth doing
Four principles
that travel with every build.
Financial baseline first
Every build starts with a number: the metric we intend to move, agreed with your finance function before we touch a workflow. Without it, value can be claimed but never measured — and we don’t operate that way.
Technology agnostic
No contractual incentive to recommend any model, vendor or stack. The problem decides the technology. If the right answer is a rules engine with no AI in it, we will say so.
Capability transfer
We don’t build dependencies. Documentation lives in your repo; operating instructions live in your rituals. If we are still answering routine questions six months after handover, the build wasn’t finished.
Small teams. Real ownership.
Four to six people, no steering committee. Whoever diagnoses the problem stays through the build. Whoever builds the system stays through the measurement.
How we’re different.
- —Stops at recommendations
- —Owns the strategy deck, not the system
- —Measures hours billed, not value delivered
- —Hands over the deck and leaves
- —Starts with their tool
- —Models without workflow context
- —Pilot success without operating change
- —Hands over the licence and leaves
- —Starts with the economic problem
- —Stays accountable for the working system through value measurement
- —Measured in P&L impact, signed jointly with the client
- —Embeds engineers until the metric moves
Value is not claimed after the fact. It is structured upfront.