Most clients start with a Rapid Assessment. When the need is broader, the work usually expands into one of the engagement types below.
Most AI strategies are prioritization exercises dressed as vision documents. This engagement produces the thing that actually matters: a defensible view of where AI creates value in your P&L, which initiatives deserve serious investment, and what sequence of bets will survive the first board review. Every line item is anchored to the Economic Readiness Model — a structured framework that quantifies AI opportunities before any code is written (Volume × Recovery − Cost to Automate = Net Recovery).
CIOs, CTOs, or CEOs preparing to defend an AI investment thesis to a board or investor group. PE-backed operators running a value-creation plan. Organizations that have accumulated a portfolio of AI pilots and need an honest reassessment before the next capital cycle.
A prioritized initiative portfolio with quantified business cases. A 12–24 month sequenced roadmap with dependencies and decision gates. A build-vs-buy-vs-configure matrix for every initiative. And an executive narrative that holds up under board-level scrutiny.
When needed, strategy engagements include executive alignment work: preventing bad AI decisions driven by leadership misunderstanding, aligning operations, finance, engineering, and legal on what is real versus hype, and reducing adoption failure before it starts. This is strategic alignment — not a training program.
We map how work actually happens, identify where AI can replace, accelerate, or support decisions, model the economics, and define what a production-safe pilot looks like. The goal is a clear answer on what to automate, what it's worth, and how to build it so it actually ships.
Operations-heavy organizations where engineering and operations hold different views of how the work actually gets done. Teams with large populations of repetitive or judgment-based tasks. Leaders who suspect significant work is going untouched because the unit economics don't support human processing — but might under an AI-first approach.
End-to-end workflow documentation. A prioritized automation roadmap with quantified impact estimates. A unit-economics model comparing human and AI processing costs. A pilot architecture with human-in-the-loop checkpoints. And a production-path plan that closes the pilot-to-production gap.
Every serious AI initiative eventually runs into the same wall: the data and systems underneath it aren't ready. Legacy platforms, inconsistent schemas, missing data models, and in-flight cloud migrations create a foundation where no automation, no agent, and no analytics layer can be trusted. This engagement makes your systems and data usable for AI.
Organizations running two or more legacy systems with no canonical data model. Teams mid-migration to a modern cloud or data platform who need architecture alignment before the point of no return. Operations holding critical data in transaction streams (EDI, webhooks, event feeds) that are materially under-utilized today.
A canonical data model specification. Cross-system entity mapping and reconciliation design. MDM governance framework and data-stewardship model. Current-state assessment with gap analysis. Cloud platform migration alignment. A data catalog implementation roadmap.
This is not advisory drift. It is accountable, embedded delivery — a senior operator sitting inside your team, carrying a named outcome, and making calls in the room where the decisions actually happen. Multi-month engagements structured around a defined result, not a deliverable list.
Leadership teams that need senior AI and architecture judgment on a recurring basis but don't need or can't yet justify a full-time hire. PE-backed operating companies where AI and data capability is on the value-creation critical path. Organizations running a transformation that needs an accountable operator, not just advice.
A named outcome, owned end-to-end. Direct leadership of cross-functional workstreams. Hiring-plan and team-design input. Vendor selection and oversight. Board or investor communication support. And the institutional knowledge handoff required to make the role eventually redundant.
Recurring cadence, board-level accountability, portfolio ownership across all AI initiatives.
Full-weight architecture leadership during a transition, migration, or leadership gap.
Embedded ownership of a single critical-path workstream through to production and handoff.
Some problems need more than a recommendation. They need an initial build.
A 10–12 week engagement for companies that already know the opportunity is real and need a clear path from workflow reality to a production-intent AI pilot.
This engagement is designed for teams that have moved past AI curiosity and need deeper work: understanding how the business actually operates, identifying where automation will create measurable value, designing the architecture and controls required to support it, and building an initial pilot that can be hardened and moved toward production.
We observe how work is actually performed, map friction and exception paths, and identify the repetitive, rules-based, and judgment-heavy decisions that shape throughput, cost, and quality.
We identify the workflow most worth acting on now based on operational value, feasibility, implementation effort, and deployment risk.
We define the systems, data requirements, validation approach, human oversight points, and governance needed to support a pilot that is built for real deployment.
We build one initial AI-enabled workflow pilot in a way that is meant to be extended and hardened, not discarded. The goal is to prove the workflow, the economics, and the control model with a foundation that can move toward production.
We define the remaining work required for broader deployment, including integration depth, security, observability, QA, approvals, and ownership across business and technical teams.
Most clients begin with a Rapid Assessment. When the need is broader or the goal is to leave the engagement with a real pilot already underway, the work expands into a Transformation Sprint.
If the problem is already clear and urgent, we can scope a direct engagement. Otherwise, the Rapid Assessment is the lowest-risk way to get a concrete answer before committing budget.