Find the AI Opportunity Worth Building

A structured assessment that identifies your highest-return AI opportunity, validates the data foundation, and delivers a funded business case - in two weeks, not two quarters.

The companies making genuine progress with AI are not running large transformation programmes. They identified one process where manual work was expensive and repetitive, automated it, measured the result, and used that to justify the next.

The blockers are rarely technical. Most organisations do not know which process to target first, are uncertain what it will actually cost and return, and have teams with legitimate questions about what automation means for how they work.

Gradion starts with a structured audit that answers all three questions before any build begins. The output is not a strategy deck. It is a decision: which process, what it costs, what it returns, and whether your data is ready to support it.

What We Deliver

Five components, delivered as a single engagement.

Process & Cost Audit

A structured mapping of your operations to identify where AI automation creates the highest financial return. Each candidate process gets a cost estimate, a feasibility assessment, and a risk rating. You leave with a ranked shortlist and a clear recommendation on where to start.

Data Readiness Assessment

The most common reason AI projects fail in production is not the model - it is the data. Before any build begins, we assess what data is available, how clean it is, where it lives, and what would need to change for an agent to run reliably on it. This is a standard component of every strategy engagement, not an optional add-on. We surface the gaps before they become expensive surprises.

Business Case & Roadmap

A financial model for the first use case and a sequenced view of what comes next. Investment stages, decision gates, and a clear line between cost and saving. Written to be read by executives and defensible in a board review.

Governance & Compliance Framing

For organisations in regulated sectors or with EU AI Act exposure, we map the compliance surface and design governance into the approach from the start - model transparency, human oversight requirements, audit trails, and data sovereignty where required.

Implementation Advisory

Once the strategy is agreed, we stay engaged through the build. Not as a project management layer, but as the team that ensures what gets built matches what was scoped. This is what prevents the pilot from becoming permanent.

How We Engage

Total engagement: 1–2 weeks · Fixed price

What happens next: If the strategy recommends agentic automation, Gradion builds it. If it recommends MLOps infrastructure, Gradion delivers it. If it recommends a design sprint to test feasibility, we scope one. The strategy engagement connects directly to every offering in Gradion's AI practice - same team, same context, no handover gap.

The Economics

The strategy engagement is typically in the low five-figure range. The output is a funded decision, not a report.

Most clients who proceed to a first build invest in the mid five-figure to low six-figure range. The return is measurable within the first year - usually within the first quarter. Once the first agent is live and the saving is real, the next process is easier to justify and cheaper to build, because the data layer and architecture already exist.

The teams working alongside the new system are involved in how it is designed: escalation paths are explicit, nothing is silently dropped, and the agent handles the repetitive, low-judgement work while decisions and exceptions stay with the people best placed to make them.

Proof In Production

Procelo - From Feasibility Assessment to Production Agent in 8 Weeks Procelo engaged Gradion to assess whether AI could automate how companies evaluate complex ERP data - work that had previously consumed significant analyst time. The engagement began with a structured audit before a line of code was written: process mapping, cost analysis, data readiness assessment, and a business case that justified the investment. The strategy led directly to a build. The resulting agent reached 80%+ SQL query accuracy across complex ERP schemas within eight weeks. Without the strategy work, the build would have targeted the wrong problem.

IDNow - From Prototype to Regulated Production IDNow, one of Europe's leading identity verification providers, had AI capabilities that worked in demonstration but could not run at the latency and compliance requirements of regulated production. Gradion's engagement began with an assessment of what was missing between prototype and production - infrastructure gaps, monitoring requirements, compliance surface - then moved into a multi-year ML engineering partnership. The strategy assessment determined the engineering path; the engineering path delivered real-time ML at enterprise scale.

Manufacturing Platform - Strategy Before Engineering The company needed to translate a platform vision into something investors could evaluate and engineers could build against. Gradion delivered the strategic assessment first: market positioning, technical feasibility, architecture direction, and a roadmap that sequenced investment against validated assumptions. The strategy work made the engineering viable - and made the investment case fundable.

All figures are from live engagements. Additional references available under NDA.

Data Residency

For organizations where even the strategy assessment involves sensitive operational data - financial records, health data, customer PII - we work within your data sovereignty requirements from day one. Assessments can be conducted under NDA with data handled on EU sovereign infrastructure where required.