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Why this works

A Big 4 consulting engagement follows a well-established process: research the organization, interview stakeholders, apply analytical frameworks, identify threats and opportunities and synthesize findings into deliverables. The methodology is proven. What varies is who executes it, how long it takes and how consistent the output is.

Mireto follows the same process at every step. The difference is execution. Strategic frameworks are applied in parallel rather than sequentially, each purpose-prompted to its analytical lens. Every dimension is rated against a calibrated rubric with hard boundaries, not estimated by an analyst having a good or bad day. Every finding carries a confidence tag so readers know what is verified and what needs validation. The strategy workshop puts four specialist AI Advisors in the room, each working through different facets of the organization's position. And every AI Strategic Blueprint is reviewed by an automated quality agent that works through a 14-point checklist before release.

The same rigor, running continuously

Everything above describes a single organization's assessment. Separately, five research tracks run every week and feed Signal and the Research Console: frontier capability releases, adoption evidence, regulatory activity, patent activity and federal spend data. Nothing is published unless it ties to a checkable source, and each week's evidence is synthesized once into a single issue, not regenerated per reader.

Calibrated rubrics, not AI-generated numbers

The most common failure in AI-generated assessments is that scores sound plausible but are not grounded in anything reproducible. An AI model asked to “rate this company's AI exposure on a scale of 1-10” will produce a number. It will not produce the same number twice, and it cannot explain why it chose 7 instead of 6.

The scoring model uses calibrated rubrics with hard boundaries for each dimension. A workforce replaceability rating of 7 or more requires that more than half of the organization's roles are knowledge work AI can credibly perform. A market velocity rating of 7 or more requires product or service cycles under a year and AI-native competitors that have entered the market in the last 24 months. These boundaries are not suggestions; they are constraints that prevent score inflation and ensure that different organizations receive meaningfully different scores.

Maturity scores from the strategy workshop follow the same calibrated approach, scored against hard boundary rules and clamped to prevent inflation. Unlike the Snapshot scores, these can be reviewed and adjusted before strategy generation.

Three-tier confidence system

Every finding in the assessment carries one of three confidence tags:

When validated data is provided through the strategy workshop (confirming or correcting an inference) the confidence tag upgrades, the underlying data changes across every analysis and deliverable, and the finding's evidence line records that you confirmed it. This is how a desktop assessment becomes a grounded strategy, and it is why the evidence base ends up part yours.

A shared, continuously verified evidence base

Everything above draws on the same underlying store: externally sourced facts, refreshed on a standing schedule rather than assembled once and left to age. Regulatory changes, market and adoption signals, verified company filings and peer activity all feed it.

Membership is about where a fact came from, not what it is about. A fact enters only if it came from a public source: a filing, a regulatory notice, a verified report, admin-curated reference material. It never enters if it is your own confidential input, your documents, your notes, your workshop conversation, or Mireto's own output about your organization specifically. That material stays on your own record and never crosses into the shared base other clients' work draws on.

The same discipline runs the other way. What a peer's evidence can say about you is deliberately narrow: a competitor's AI activity is described on its own terms, never as a named comparison against you. A claim that later turns out to be unsupported is removed outright, not just flagged.

This is the same evidence base your Snapshot, your AI Disruption Analysis, the AI Playbook, the Portfolio Assessment and every AI Advisor conversation draw on: current, kept apart from anyone's confidential material, and honest about what has since been retracted.

What happens before you see results

Before any deliverable is released, an automated quality agent reviews it against a 14-point checklist. Every area it covers:

Findings that fail the review are corrected automatically. Narrative tone is actively moderated. Language severity is calibrated to each disruption band to prevent the analysis from being more alarming than the evidence supports.

Analysis that speaks the right language

Government organizations do not have “customers”; they have citizens. They do not have “revenue”; they have appropriations. Universities measure enrollment, not sales. Not-for-profits track mission impact, not profit margins.

The analysis adapts every dimension (language, metrics and policy context) based on whether the organization is a company, government agency, university, SME or not-for-profit. Government analysis is grounded in current policy frameworks for the jurisdiction. This is not a cosmetic word replacement. It reshapes how threats and opportunities are framed and what language appears in deliverables.

Proven consulting methodologies, executed by AI

The analysis is built on six proprietary strategic frameworks developed from established consulting methodologies:

These frameworks are applied to every assessment automatically, in language calibrated to the organization's industry and entity type. They provide the analytical structure that transforms raw data into strategic insight.

How the assessment is built

The sections below describe the specific steps, scoring dimensions and generation process in detail.

AI Disruption Assessment

The initial scan produces an AI Disruption Snapshot based entirely on publicly available information. No organizational data is required at this stage.

The Eight Dimensions

The AI Disruption Score is calculated across eight weighted dimensions. Four measure exposure (how much AI reshapes that part of the business), one measures the opportunity in proprietary data, and three measure defensibility. The three defensibility dimensions are scored inversely, so a lower score means stronger protection. Proprietary data is scored as the stakes of the data arena: the number is high whether the organization holds the advantage or faces the gap. Weights reflect relative importance and sum to 10.0, so a perfect score across all dimensions equals 100. Every term used here is defined in the glossary.

Disruption Bands

Playbook or AI Strategic Blueprint

From the Snapshot, two paths diverge. Both start from the same AI Disruption Snapshot.

The sections below cover Strategy Development, which describes the AI Strategic Blueprint path only.

AI Strategy Development

When an organization moves to the AI Strategic Blueprint, the AI readiness assessment is enriched with organizational data through three inputs: document uploads, a guided strategy workshop and AI maturity self-assessment.

What's behind the platform

The AI. Mireto is powered by Anthropic's Claude. Three model tiers are used deliberately, each matched to the cognitive load of the task:

• Claude Opus: highest-reasoning tasks: AI Advisor persona outputs, strategic recommendations, financial estimates
• Claude Sonnet: synthesis, mid-complexity analysis, deliverable content generation
• Claude Haiku: lightweight routing, validation and classification

Data sent to the Claude API is processed by Anthropic and is not retained for model training, per their published commercial terms. Scan data is stored in the jurisdiction of the organization being assessed. Full data-handling details are in the security overview and privacy policy.

The agentic framework. The six AI Advisors aren't a single model with different prompts. Each runs as an independent agent with a distinct system prompt, defined area of expertise and access to the analytical frameworks relevant to its discipline. They work through the scan and workshop responses sequentially during the workshop, their findings are reconciled by an automated quality reviewer and the delivering partner signs off on AI Strategic Blueprint deliverables before release.

The organization's role in the analysis. The initial industry classification is inferred from public web data, business registration and website content. Industry-level taxonomies are imperfect: an organization with a niche focus, a peak body or a government agency by portfolio area can land in the wrong category. After the scan completes, the classification can be confirmed or corrected on the AI Disruption Snapshot page; the peers, dimension scores and headline disruption score are re-run against the corrected category. The narrative summary refreshes on progression to AI Strategy.

Every AI Strategic Blueprint engagement is reviewed and signed off before deliverables are released. This isn't a platform that runs the Blueprint unattended: the delivering partner reads what the AI Advisors produce, checks it against the organization's context and stands behind it with a signed cover letter.

For methodology questions, guided sessions or partnership conversations, get in touch.