AI/ML Strategy Consulting & Readiness Assessment

Before recommending a single model or platform, we run a structured readiness assessment: cataloguing candidate use cases, scoring each against data availability and business impact, and auditing the actual state of your data — volume, quality, accessibility, labeling — against what each use case would require. Most failed AI initiatives fail here, at the planning stage, not at the model stage.

The output is a prioritized roadmap, not a slide deck of possibilities — a build-vs-buy recommendation per use case (a commercial API like OpenAI or Azure OpenAI versus a custom-trained model), a realistic cost and timeline range, and a clear statement of which use cases the current data can support today versus which need data work first.

• Use-case discovery workshops and impact/feasibility scoring
• Data maturity and readiness audit against each candidate use case
• Build-vs-buy and platform recommendation (OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI, or custom)
• Prioritized roadmap with realistic cost and timeline ranges
• Honest go/no-go guidance where the data isn't ready yet