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---
layout: base
title: Engineering Intelligence — Deterministic Readiness, Advisory AI
description: A deterministic readiness engine for regulated engineering, with AI that stays strictly advisory and cited.
og_image: /images/Utmostconnect.png
nav:
- label: Approach
href: "#approach"
- label: Validation
href: "#validation"
- label: Governance
href: "#governance"
nav_cta:
label: Request a pilot
href: "#contact"
---
<style>
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product signal, not decoration, so it isn't merged into Utmost
Connect's single --accent. --accent itself is remapped to --gov so
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</style>
<section class="hero">
<div class="wrap">
<span class="hero-eyebrow">● engineering decision intelligence</span>
<h1>A readiness score that can't be <em>talked into</em> anything.</h1>
<p class="lede">Engineering Intelligence computes design readiness deterministically from your own evidence — then layers a strictly advisory, cited, local-only AI assistant on top that is architecturally incapable of changing the answer.</p>
<div class="hero-ctas">
<a class="btn btn-primary" href="#contact">Request a pilot</a>
<a class="btn btn-ghost" href="#approach">See how it's separated →</a>
</div>
<div class="chain-panel">
<div class="chain-label">The governed decision chain — every readiness score, traced</div>
<div class="chain-row">
<div class="chain-node">Evidence</div><span class="chain-arrow">→</span>
<div class="chain-node">Coverage</div><span class="chain-arrow">→</span>
<div class="chain-node">Consistency</div><span class="chain-arrow">→</span>
<div class="chain-node">Validation</div><span class="chain-arrow">→</span>
<div class="chain-node">Confidence</div><span class="chain-arrow">→</span>
<div class="chain-node">Readiness</div>
</div>
<div class="chain-advisory-link">
<div class="chain-advisory-dash" aria-hidden="true"></div>
<div class="chain-advisory-node"><span class="dot"></span>AI Advisory Review</div>
<div class="chain-caption">reads the same evidence · cites every claim · never writes back into the chain above</div>
</div>
</div>
</div>
</section>
<section class="block" id="problem">
<div class="wrap">
<p class="eyebrow">The problem</p>
<h2 class="title">Every design review asks one question. Answering it is still manual.</h2>
<p class="dek">"Is the evidence complete, consistent, and sufficient enough for a human to make an accountable decision?" Every regulated engineering team answers this by hand today — and every vendor is now racing to bolt an LLM onto that answer, which is precisely the wrong place to introduce uncertainty.</p>
<div class="problem-grid">
<div class="problem-card old">
<h3>What "AI-assisted" usually means</h3>
<p>A model summarizes your evidence, drafts a recommendation, maybe adjusts a risk score — and if it's wrong, there's no clean way to know which parts came from your data and which came from the model filling in gaps.</p>
<p>In a regulated design review, that's not a productivity feature. It's an audit finding waiting to happen.</p>
</div>
<div class="problem-card new">
<h3>What this platform does instead</h3>
<p>The readiness score is a fixed formula over your own structured evidence — full stop. AI is a separate, clearly-labeled advisory layer that can summarize and cite, but is technically unable to move the number.</p>
<p>You can show an auditor exactly where every input came from, and exactly where the model's opinion started and stopped.</p>
</div>
</div>
</div>
</section>
<section class="block" id="approach">
<div class="wrap">
<p class="eyebrow">The approach</p>
<h2 class="title">Two systems, deliberately kept apart</h2>
<p class="dek">Most tools blur "the score" and "what the AI thinks" into one confident-sounding output. This platform draws a hard line between them, and the line is enforced in code, not just in the UI copy.</p>
<div class="system-grid">
<div class="system-card">
<span class="system-tag">deterministic core</span>
<h3>The readiness engine</h3>
<p>Every score is computed from structured, human-entered evidence through a published, fixed pipeline. No model in the loop. The same inputs always produce the same result.</p>
<ul class="system-list">
<li>Evidence coverage against fixed regulatory categories</li>
<li>Structural consistency checks across findings and citations</li>
<li>Human-assigned confidence, not a model's confidence</li>
<li>A single documented formula for the final index</li>
</ul>
<div class="formula-strip">ECI = average(Coverage, Consistency, Validation, Confidence)</div>
</div>
<div class="system-card ai-card">
<span class="system-tag">advisory layer</span>
<h3>The AI review</h3>
<p>One locally-hosted model, never a cloud call, producing schema-constrained output that must cite the evidence it's drawing from — or say plainly that it couldn't.</p>
<ul class="system-list">
<li>Every claim traced to a cited evidence ID</li>
<li>Provenance-hashed, so the exact request is reproducible</li>
<li>Provider failure falls back to a deterministic template — never a guess</li>
<li>Fixed "advisory only" label on every output, everywhere it appears</li>
</ul>
<div class="formula-strip">human_review_required: Literal[True] — enforced, not suggested</div>
</div>
</div>
</div>
</section>
<section class="block">
<div class="wrap">
<p class="eyebrow">Why this is credible</p>
<h2 class="title">Built like a regulated tool, not a demo</h2>
<p class="dek">Four things that hold up under a skeptical technical read, not just a sales conversation.</p>
<div class="proof-grid">
<div class="proof-card">
<h4>Real regulatory grounding</h4>
<p>IEC 62304, ISO 14971, IEC 60601, 21 CFR Part 803, and FDA design-control guidance are modeled as first-class reference data, not marketing copy.</p>
</div>
<div class="proof-card">
<h4>Local-first by architecture</h4>
<p>No external API dependency in the core workflow. The one AI integration point validates at startup that its endpoint is local-only — enforced, not promised.</p>
</div>
<div class="proof-card">
<h4>Validated on real cases</h4>
<p>Runs against actual public FDA recall cases alongside a synthetic composite, producing stable, explainable readiness scores across every one.</p>
</div>
<div class="proof-card">
<h4>Process discipline as a product feature</h4>
<p>Architecture decision records, a release-review checklist, and versioned release manifests — the kind of trail a compliance-minded buyer checks before the UI.</p>
</div>
</div>
</div>
</section>
<section class="block" id="validation">
<div class="wrap">
<p class="eyebrow">Validation</p>
<h2 class="title">Tested against real regulatory history, not just synthetic data</h2>
<p class="dek">Three case types, same deterministic pipeline, explainable results in every one.</p>
<div class="case-strip">
<div class="case-card" style="--case-accent:var(--critical)">
<div class="kind">Public FDA recall case</div>
<h4>Tandem Mobi</h4>
<p>A real, publicly documented device recall used to stress-test coverage and consistency scoring against actual regulatory evidence.</p>
</div>
<div class="case-card" style="--case-accent:var(--critical)">
<div class="kind">Public FDA recall case</div>
<h4>Abiomed Impella CP</h4>
<p>A second independent public case, confirming the scoring pipeline generalizes rather than being tuned to one dataset.</p>
</div>
<div class="case-card" style="--case-accent:var(--gov)">
<div class="kind">Synthetic composite</div>
<h4>NovaPump</h4>
<p>A controlled fixture case used as the platform's regression anchor — same formulas, fully reproducible, used in every automated test.</p>
</div>
</div>
</div>
</section>
<section class="block" id="governance">
<div class="wrap">
<p class="eyebrow">Governance</p>
<h2 class="title">The rules aren't a policy page — they're architecture decisions</h2>
<p class="dek">Two decisions, on the record, that any technical evaluator can go read.</p>
<div class="adr-row">
<div class="adr-item">
<div class="id">ADR-002</div>
<div>
<h4>AI is advisory</h4>
<p>AI output must be structured, cited, limited, and visibly distinct from governed facts. It cannot create evidence, alter results, approve records, or declare compliance.</p>
</div>
</div>
<div class="adr-item">
<div class="id">ADR-009</div>
<div>
<h4>Local AI gateway</h4>
<p>Provider-independent, schema-constrained, local-only by validated configuration. Provider failures reject execution — they never silently fall back to a guess.</p>
</div>
</div>
</div>
</div>
</section>
<section class="block">
<div class="wrap">
<p class="eyebrow">Where it stands today</p>
<h2 class="title">Stated plainly, not oversold</h2>
<div class="stage-callout">
<span class="stage-badge">Pre-revenue · MVP</span>
<p>This is a single-user, local-only platform today — no authentication, no multi-tenant deployment yet, and it has been validated on public case data but not yet piloted inside a live design team. The durable audit-ledger and multi-specialist AI review layers already exist as designed interfaces, deliberately held back from the live product until the current single-specialist experiment is reviewed and accepted. That sequencing is a considered roadmap, not a missing feature. The raise stays deliberately lean: the AI runs locally, so there's no metered inference cost to fund — the ask covers design-team pilots, not a full go-to-market build-out.</p>
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<h2>Bring one real design review.</h2>
<p>Run it against your own evidence and compare the output to what your review board would have concluded independently — or talk through where this fits as an investment or partnership.</p>
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<a class="cta-path" href="pilot-setup.html">
<strong>Open pilot setup guide →</strong>
<span>Choose the shared ZIP distribution path with step-by-step instructions</span>
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<a class="cta-path" href="mailto:amir.yousef.sajjadi@gmail.com?subject=Engineering%20Intelligence%20—%20Investor%20/%20partner%20inquiry">
<strong>Investor & partner inquiry →</strong>
<span>Raising pre-seed funding — or talk distribution, integration</span>
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<p>This platform supports engineering judgment — it does not independently determine product safety, regulatory compliance, root cause, or corrective-action effectiveness, and does not itself constitute engineering approval.</p>
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