Three offerings.
One method.
Each offering stands alone. Together they form how Goliath turns AI ambition into measurable operating advantage — and every mission runs on the same five-step method.
From AI ambition
to working systems.
We turn high-value problems into focused AI missions — diagnosed, built and measured against business value.
Identify where AI can create measurable value and which mission should move first.
Outputs
- →Opportunity map ranked by value × feasibility
- →Baseline + measurement plan for the priority mission
- →Recommended first mission with scope & success criteria
Connect data, workflow logic and accountability into a leadership-ready operating view.
Outputs
- →A single operating view across the missions that matter
- →Decision triggers wired to owners, not dashboards
- →Cadence integration: weekly review, monthly steer, quarterly reset
Redesign and automate high-friction workflows using AI, software and operating logic.
Outputs
- →Redesigned workflow with measured cycle-time uplift
- →Production system embedded in operations
- →Transfer plan: documentation, owner training, run-rate handover
When multiple missions prove value, Goliath connects them into a scalable AI operating model.
One method, applied to every mission.
Sprint, Cockpit, Mission — different scopes, different durations, the same operating discipline. Three principles. Five steps. Measured against a baseline agreed with your finance function.
Three principles that travel with every mission.
Financial baseline first
Every Goliath mission starts with a number: the baseline state of the metric we intend to move. Cost per claim. Cycle time from order to invoice. Forecast variance against actuals. We agree the baseline with the client's finance function before we touch a workflow. Without that number, value can be claimed but not measured — and we don't operate that way.
Technology agnostic
We have no contractual incentive to recommend any specific model, vendor, or stack. The mission decides the technology, not the other way around. If the right answer is a fine-tuned open-weight model running in the client's VPC, we build that. If it's a frontier API behind a thin orchestration layer, we build that. If it's a deterministic rules engine with no AI at all, we'll tell you.
Capability transfer
We don't build dependencies. Every mission produces a system that the client team can run, modify, and evolve after we leave. Documentation lives in the client's repo. Operating instructions live in the client's ritual cadence. If a Goliath partner is still answering routine questions about a mission six months after handover, the mission wasn't complete.
Five steps. One mission at a time.
01
Problem
We define the economic problem in operating terms, not aspirational terms. The wrong starting question is "Where can we use AI?". The right one is "Which metric, owned by which leader, is underperforming, and what does the decision loop around it look like?". We run structured discovery, sample the data, and write a one-page problem statement.
- —Interview the metric owner and adjacent leaders
- —Sample the workflow as it actually runs
- —Identify the baseline value, source, and reporting cadence
- —Write a one-page problem statement, signed by the owner
- —A baselined problem statement with named owners
- —A decision-loop map of how the metric moves today
- —A list of unknowns to resolve in step 02
02
Focused team
We assemble a small mission team: a domain operator from the client, a Goliath partner, an AI engineer, and — critically — someone from finance who owns the baseline. Four to six people. No steering committee. No working group. A clear charter, cadence, and end date.
- —Confirm the mission charter with the metric owner
- —Stand up the team — 4 to 6 people, no more
- —Set the operating cadence (weekly review, daily standup if needed)
- —Agree success and kill criteria in writing
- —A signed mission charter
- —A team operating manual (one page)
- —A cadence calendar with named decision moments
03
Workflow redesign
Before we build software, we redesign how the work happens. Most AI projects fail at this step — they bolt a model onto an old process and discover the model is making predictions nobody acts on. We rebuild the decision loop on paper first: who sees what, when, with what context, and what they do next.
- —Map the current workflow with timing, ownership, and handoffs
- —Identify the decision moments AI can actually improve
- —Redesign the workflow with AI in its proper place
- —Validate the redesign with the operators who will run it
- —Before/after workflow diagrams
- —A decision-moment specification — input, output, owner, cadence
- —A list of process changes that must precede any software
04
Build and transfer
Now we build. The mission team produces the working system — model, data pipeline, interface, instrumentation — and embeds it into the operating cadence. We don't hand over a demo. We hand over a system that is already running in the redesigned workflow. Transfer is continuous, not a final event.
- —Build the model, pipeline, interface, and instrumentation
- —Embed the system into the redesigned workflow
- —Train the client team on operation, maintenance, and iteration
- —Document the system in the client's own repository
- —A working system in production
- —Operating documentation in your repo
- —A trained client team that can run and evolve the system
- —A monitoring dashboard tied to the baseline metric
05
Value capture
The final step is the most often skipped one. We measure the system's effect on the baseline metric over a defined post-launch period and produce a value report — signed jointly by the metric owner and a Goliath partner. If the system moved the metric, we say so with the number. If it didn't, we say so with the number.
- —Measure the baseline metric for the agreed post-launch period
- —Compare to the pre-launch baseline with controls for confounders
- —Produce a joint value report, signed by client owner and Goliath partner
- —Document lessons learned for the next mission
- —A measured uplift against the agreed baseline
- —A joint value report (1 page)
- —A recommendation for the next mission, if appropriate
How we're different.
- —Stops at recommendations
- —Owns the strategy deck, not the system
- —Measures hours billed, not value delivered
- —Starts with their tool
- —Models without workflow context
- —Pilot success without operating change
- —Starts with the economic problem
- —Owns the working system through value measurement
- —Measured in P&L impact, signed jointly with the client
Value is not claimed after the fact. It is structured upfront.