Lucentive

Thesis: Most AI programs scale only as fast as their slowest gating step. Most leaders watch two of those steps (compliance backlog, model updates) when there are seven. We map the full set and name the step about to set the ceiling.
Scope: Three to four weeks. Senior interviews, evidence pull, working sessions with the engineering and program owners.
Outcome: A written diagnosis naming the step under load and the next concrete change worth making. Delivered as a working document, not a deck.
Fit: Leadership running an AI program that worked in pilot and is not scaling. The problem is felt; the cause is contested.
Shape: Founder-led. Fixed scope. Fixed price.

Thesis: Agent output is bounded by what context the system can reach. Better search does not fix uncurated context. A usable context system gives the agent the same goals, constraints, standards, and decisions every run, then records which context was applied.
Scope: Six to ten weeks. One workflow chosen for the first context layer. In-line review and evidence wired in from week one.
Outcome: A shipped reusable-context layer for the chosen workflow, plus a propagation pattern your team can extend. Intuitive Agent System (IAS) is the ready software path for teams that want the repo layer off the shelf.
Fit: Teams whose agent output is structurally right and substantively wrong. The schema is fine; the substance is missing.
Shape: Mixed team. Fixed scope on the first workflow, optional retainer for propagation.

Thesis: A few engineers already work differently with AI. The rest of the organization has not moved. Hiring will not close that gap. Propagation work captures practice, packages the harness, and moves learning across team boundaries.
Scope: One quarter. Strong-developer practice capture, harness review and packaging, captured-learning instrumentation, propagation across two to three additional teams.
Outcome: Propagation infrastructure inside the organization, not a training deck. Captured practices, packaged harnesses, and a measurement loop the organization keeps running after we leave.
Fit: Organizations where individual AI leverage is real and visible, and the gap to the rest of engineering is widening.
Shape: Mixed team. Quarter-scale. Monthly review with engineering leadership.

Thesis: A diagnostic or an architecture engagement closes; the operating-model rebuild keeps running for quarters after. Leaders running through that rebuild want depth without the re-onboarding cost of a new firm each cycle.
Scope: Monthly. Two working sessions plus async availability. Founder-led, with continuity across any prior engagement.
Outcome: A thinking partner inside the rebuild, not a deliverables vendor. Decisions accelerate; expensive mistakes get caught earlier.
Fit: CIOs, CTOs, and Chief AI officers running the AI program directly. A prior engagement helps but is not required.
Shape: Monthly retainer. Cancel quarterly. Founder-led.

Four shapes. Founder-led.