Most AI projects fail in the first 30 days, not in production. They fail because the team starts shipping before anyone agreed on what success looks like, what data is available, and which model behaviours are non-negotiable. Our AI Discovery Mission is a four-week, fixed-scope engagement that turns a vague AI ambition into a build-ready plan: a product vision, an evaluation harness, a technical architecture, and a defensible budget.
What an AI Discovery Mission delivers
An AI Discovery Mission is a structured four-week engagement that produces the artefacts a founder, CTO, or board needs to commit capital to an AI build with confidence. Unlike a generic software discovery, it treats model behaviour, data, and evaluations as first-class deliverables, not afterthoughts.
- Week 1, Product vision: user jobs, AI capability map, success metrics, and the LLM-vs-deterministic decision tree
- Week 2, Backlog & wireframes: prompt-aware UX flows, agent boundaries, and a prioritised feature list
- Week 3, Technical architecture: model selection, retrieval design, eval harness, and the integration surface with existing systems
- Week 4, Plan & estimates: a sprint plan, an evals-driven definition of done, and a transparent budget with risk-weighted ranges
Why discovery looks different for AI products
Traditional discovery assumes deterministic software: defined inputs, defined outputs, predictable cost per call. AI products break every one of those assumptions. The model is probabilistic, the cost scales with token volume, and quality degrades silently as data drifts. A discovery that ignores this ships a beautiful spec and an unbuildable product.
Most AI projects fail not because the technology doesn't work, but because the use case was wrong. Gartner's 2024 AI Hype Cycle report found that 85% of AI projects that fail in production cite use case misalignment or poor data readiness as the primary cause — not model capability.
We design the evaluation harness in week three for a reason: if you cannot measure quality, you cannot ship the product. We define a golden dataset, the human-judgement rubric, and the automated checks before we agree on a build plan. This is the single biggest lever for de-risking an AI roadmap. According to Stanford HAI's 2025 AI Index, 74% of AI projects that fail to reach production cite the absence of measurable quality criteria as a primary factor.
If your AI vendor cannot show you the eval harness in week one of a build, they are not de-risking your product, they are gambling with your runway.
When to invest in an AI Discovery
Data readiness is the most common blocker we find during discovery. IBM's Global AI Adoption Index 2024 reported that 42% of companies that attempted AI adoption cited poor data quality or accessibility as the primary barrier — above budget, talent, or technology constraints.
Run an AI Discovery Mission when the answer to any of these is unclear: which user jobs the model is allowed to take over, which data the model is allowed to see, what 'good enough' looks like at launch, or how the AI feature interacts with the rest of your stack. Two to four weeks of structured discovery typically saves three to six months of wasted build time — a pattern consistent with McKinsey's finding that AI projects with upfront technical feasibility work are 2.5× more likely to reach production.
What the output looks like
- A product vision document the leadership team has signed off on
- A wireframe set that makes prompt and agent boundaries explicit
- An evaluation plan with a golden dataset and pass/fail thresholds
- A reference architecture covering models, retrieval, orchestration, and observability
- A delivery plan with milestones, ownership, and a budget the CFO can defend
The Discovery Mission isn't a sales motion, it's an engineering decision. Walk away with a build-ready plan, even if you decide not to build with us.