Loading…
Paid Audit
AI projects fail for predictable reasons: data that isn't ready, processes that aren't documented, integrations that are harder than expected, compliance requirements nobody checked, and use cases that were never clear enough to build against. These are all discoverable before you start building. If you look.
This is a structured 10–15 day assessment that examines your data, processes, systems, team, and use cases against a readiness framework. You receive a written report with scores, findings, your top three use cases to pursue first, and a specific list of blockers to address before building.
Tell us what you’re trying to build.
Six dimensions of AI readiness, each scored and reported with specific findings, not a generic checklist.
AI systems are only as good as the data they learn from or retrieve from. We assess whether your data is structured, accessible, labelled, and representative enough for the use cases you have in mind. Unstructured data, siloed databases, missing fields, and inconsistent formatting are the most common blockers, and the most solvable once they're named.
AI augments or automates processes. If the process isn't documented. If the logic lives only in experienced employees' heads. There is nothing for the AI to learn from or replicate. We assess the state of your process documentation and identify which processes are ready to automate versus which need documentation work first.
Most AI use cases involve connecting to existing systems: CRMs, ERPs, databases, APIs. We assess whether your current systems have the APIs, data access, and integration points needed to support the use cases on your list. Integration complexity is frequently underestimated in AI project scoping.
Data protection, sector-specific regulations, and internal compliance policies affect where data can go, which models can touch it, and what outputs can be used for decisions. We identify the compliance requirements relevant to your use cases before you build anything, not after your legal team reviews the first version.
What engineering, data, and product capability exists in-house? What will you need to hire or contract for? We assess your team's current capability against the requirements of each use case, not to make the assessment look harder, but to give you an honest picture of what build versus buy versus contract makes sense.
Vague use cases ('we want to use AI to improve our operations') fail more often than specific ones. We assess how well-defined each use case is, whether there's a clear input, a clear output, a measurable success criterion, and a defined owner. Use cases that can't answer these questions aren't ready to build yet.
Three deliverables. The report is yours to keep regardless of whether you work with us again.
A written report scoring each of the six assessment areas on a 1–4 readiness scale with the specific findings behind each score. Not a traffic light summary, named findings with named blockers.
A prioritised recommendation of the three AI use cases with the best combination of business value, technical feasibility, and data readiness. Each recommendation includes a rough scope and effort estimate.
A specific list of what needs to be addressed before development starts: data cleanup tasks, integration work, documentation gaps, compliance review items. Ordered by how much each blocker affects your priority use cases.
The assessment is designed for a specific moment in an organisation's AI journey.
Teams who have already scoped and started building
A readiness assessment is most valuable before you commit to an approach. If you're already three sprints into a build, this isn't the right engagement. You'd be better served by a targeted technical review of what you're building. We offer that separately.
Companies looking for validation rather than evaluation
We give an honest assessment. If your data isn't ready, we say so. If the use case you're most excited about has a fundamental feasibility problem, we flag it. If you want someone to confirm your existing plan is sound, this isn't the right engagement.
Teams who won't act on the findings
The assessment produces a roadmap of blockers to address. If the organisation doesn't have the authority or appetite to address data quality issues, integration work, or process documentation gaps, the assessment findings won't translate into progress. The value is in acting on the report, not in having it.