Readiness Assessment
Before you buy a single GPU: what you have, what it's graded, what to build first.
Most failed AI initiatives were lost before the first model ran: wrong first use case, ungraded data, infrastructure surprises in month three. The readiness assessment front-loads all of it into two to three weeks and ends with a roadmap you can execute, with us or without us.
What we assess
Four lenses, each producing something concrete:
- Data: what exists, where it lives, what grade it carries (public through ITAR), and what shape it's in. Deliverable: the data-grade map that decides your deployment architecture.
- Infrastructure: GPU capacity you already own, network and identity readiness, and what a right-sized deployment would add. Deliverable: gap list with costs.
- Use cases: the candidate list from your teams, scored for ROI, feasibility, and data readiness. Deliverable: a ranked portfolio with a recommended first build.
- People: who will own it, who will champion it, and what enablement the rollout needs. Deliverable: the adoption plan, including workshop recommendations.
Why the first use case decides everything
Organizations get one credibility window with AI. A first project that ships in weeks, touches a felt pain, and produces checkable answers buys permission for everything after it. A six-month moonshot that demos poorly poisons the well for years. The assessment's most valuable output is usually the re-ranking: the flashy use case moved to phase three, the unglamorous document-retrieval win moved to phase one.
The honest infrastructure verdict
Teams routinely overestimate what on-prem AI requires and underestimate what they already have. Idle GPU capacity in engineering workstations and virtualization clusters often covers a pilot outright. The assessment inventories reality, and the verdict is sometimes 'you can start Monday with what you own,' which is a much better conversation than a procurement cycle.
What you walk away with
A written roadmap: data-grade map, infrastructure gap list with budget ranges, the ranked use-case portfolio, a first-project plan with timeline and success metrics, and the deployment recommendation (on-prem, cloud, or hybrid) with the reasoning documented. It is designed to be executable by any competent team. Most clients build with us; the roadmap does not require it, and that independence is exactly why it gets trusted internally.
Straight answers
Who should be in the room from our side?
IT or infrastructure leadership, one or two operational leaders who own candidate use cases, and someone who can speak to compliance obligations. Roughly six to ten hours of your team's time across the two to three weeks, mostly in structured interviews.
How is this different from a big-firm AI strategy engagement?
It is shorter, cheaper, and written by people who then have to build what they recommend, which is a powerful honesty filter. No 80-slide deck about transformation; a roadmap with part numbers, week counts, and a ranked backlog.
Do you assess cloud readiness too, or just on-prem?
Both. The data-grade map is deployment-neutral: it tells you which workloads may use cloud, which must stay inside, and which want hybrid routing. The recommendation follows the evidence, not our specialty.
What does the assessment cost?
It is scoped to your size and complexity, and it is deliberately priced as a low-friction first engagement. For leadership teams still upstream of this, our free AI awareness workshop is the zero-cost starting point; the assessment is the natural next step after it.
Three weeks to a roadmap you can defend.
Tell us where you are (including 'nowhere yet'). We'll scope the assessment and you'll know exactly what to build first.
Scope your readiness assessmentFree AI workshops available. A founder reads every message.