Core concepts

These terms form the product's shared language. Keeping them distinct prevents the interface, documentation, and roadmap from making conflicting promises.

Agentic Brain

The Agentic Brain is the product name for the connected understanding the app builds about an AI opportunity. It brings together confirmed context, open questions, the operating model, readiness, memory, and the artifacts derived from them.

The Brain is not claimed to be conscious or independently knowledgeable. It becomes useful through the evidence and decisions accumulated with the user.

Operating model

The operating model is the precise current description of the operation the proposed AI will touch: people, systems, information, relationships, constraints, outcomes, and readiness. It is the structured entity underneath the product language of the Brain.

Canvas

The Canvas is the primary builder and editor for the operating model. Its areas guide discovery from business problem through systems, business impact, and an implementable workflow. It is not separate from the Brain; it is one of the main ways the Brain is built and corrected. The current Canvas has 11 authored sections. AI Readiness is computed from their evidence rather than authored as a separate section.

Evidence

Evidence is information supported by a user answer, attached source, public research, or an explicit decision. The origin and strength of evidence matter. AI-generated wording can organize evidence, but fluent wording does not make an unsupported claim true.

Progressive completion

Progressive completion describes how an area matures. Useful states include:

• Empty — no usable information yet.
• Seeded — an initial source or answer offers a starting point.
• Partial — some useful context exists, with important gaps.
• Supported — the core content is backed by adequate evidence.
• Decision-ready — the content and remaining uncertainty are sufficient for the next decision.

The exact labels may evolve, but the principle remains: a checkbox should not imply certainty the team has not earned.

Readiness

Readiness measures whether the organization can responsibly move toward implementation. It spans five dimensions: data readiness, process readiness, decision clarity, stakeholder alignment, and governance readiness. Low readiness does not mean the opportunity is bad; it makes the necessary preparation visible.

The 0–100 score shown on the main Brain and AI Readiness pages is the current readiness score. It is not a measure of the Brain's intelligence, a percentage of Canvas completion, or an ROI percentage.

Agentic ROI

Agentic ROI is a Brain-derived, evidence-based assessment of whether the expected value is worth building for and how confident the team can be in that business case. Its 0–100 score measures ROI confidence, not a promised return or ROI percentage.

The assessment considers value potential, baseline evidence, cost complexity, readiness dependency, and payback confidence. It can identify value drivers and missing baselines from existing Canvas evidence, but it shows annual value and payback ranges only when the user supplies the required real baselines. Evidence remains labeled as validated, estimated, missing a baseline, or unknown.

Agentic ROI is distinct from the authored Business Impact & ROI Canvas section. The section captures the team's impact claims, KPIs, costs, and assumptions; the Agentic ROI view evaluates that evidence together with the rest of the Canvas.

Brain questions

Brain questions are targeted prompts that resolve high-value unknowns. They should be prioritized by how much an answer improves the operating model or unlocks a decision, not by a desire to fill every field.

Memory and connected sources

Memory preserves relevant conversation and model history. Connected sources provide external context. A raw source does not automatically become Brain evidence: it must be reviewed and approved as attached memory before its summary and facts can ground answers. Neither memory nor sources remove the need to distinguish sourced facts, inferences, user decisions, and unknowns.

Peer benchmark

A peer benchmark compares a readiness assessment with an anonymized cohort in the same sector. The comparison is directional until the minimum sample size is available. Benchmark records contain sector, project type, readiness scores, and limited outcome metadata—not company names, user ids, or free-text Canvas content.

Projections and artifacts

A projection presents part of the operating model for a purpose or audience. Summaries, slides, workflows, diagrams, blueprints, ROI views, and security assessments are artifacts projected from the same evolving source.

The app organizes them into three hubs: Agentic Brain contains Brain, AI Readiness, and Agentic ROI; Canvas contains Overview, Summary (what the project is, refreshable), Slides, and Export (every export in one place); Outputs contains Blueprint, Workflow, and Security Review.

Agentic workflow

An agentic workflow describes how AI participates in an operation: triggers, inputs, decisions, actions, systems, human handoffs, exceptions, controls, and outcomes. It is more specific than saying "use an AI agent" and earlier than deploying one.

Security Review

The Security Review is an AI-generated planning assessment of described data categories, potentially applicable regulations, protection strategies, and a high-level checklist. It supports an early governance conversation; it is not legal advice, a compliance certification, a penetration test, or a substitute for review by qualified security and legal teams.

Chat, Guided, and Agent modes

These modes control who approves application within the current Canvas scope. Chat keeps changes behind individual Apply actions. Guided proposes improvements and always asks for approval before applying. Agent executes a visible, bounded sequence and can be stopped. They do not deploy production agents or authorize work outside the Canvas. See Using the Agentic Brain for the surface-specific behavior and approval rules.

Digital twin

A digital twin is the future aspiration: an operating model that remains synchronized with live operational signals and can represent behavior over time. The current product does not claim this capability.

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