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v4.1 go-live cut, Moonshot to Mistral swap, v5.x superseded

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Mark v5.0/v5.1 SUPERSEDED (error-detection thesis failed at -0.40 delta); v4.1 is canonical. Cut v4.1 proposal with 12 version strings bumped. Moonshot to Mistral across production seats; production worker de-kimi'd 2026-08-18. Data-retention posture corrected 7/9 to 8/9 no-training (DeepSeek sole exception). Reconciled COGS with measured Mistral spend. Committed deployed v4.0 content and research docs to resolve the repo/live fork.
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<body>
<!-- ====== HERO ====== -->
<header class="hero">
<div class="wrap" style="text-align:center">
<div class="badge">v4.1 · Architecture-Proven · Pre-Revenue</div>
<h1 style="font-size:42px;letter-spacing:-1px">VerdictTank</h1>
<p class="positioning-statement" style="font-size:18px;margin:10px auto 20px;max-width:640px">
We critique them; we don't write them.
</p>
<p class="sub" style="font-size:18px;max-width:820px;margin:0 auto">
VerdictTank is a multi-vendor AI proposal review product. A founder, a proposal team, or a consultant submits a business proposal as a file or a URL. VerdictTank runs that document through a panel of ten independent AI reviewer seats spanning nine vendors, returns two separate scores across ten scored dimensions, explains in plain language exactly what is missing behind every low dimension, and hands back a structured Fix-It plan the submitter can execute.
</p>
<div class="meta" style="justify-content:center;margin-top:24px">
<span><b>Document</b> v4.1 Proposal (master narrative)</span>
<span><b>Status</b> Architecture-proven, pre-revenue</span>
<span><b>Date</b> 2026-08-18</span>
<span><b>Owner</b> Germaine Brown, product owner</span>
<span><b>Surfaces</b> verdicttank.com, my.verdicttank.com, api.verdicttank.com</span>
</div>
</div>
</header>
<!-- ====== TOC ====== -->
<nav class="toc">
<div class="wrap">
<ol>
<li><a href="#executive-summary">1. Executive Summary</a></li>
<li><a href="#journey">2. The Full Journey</a></li>
<li><a href="#features">3. Feature Set</a></li>
<li><a href="#pricing">4. Pricing</a></li>
<li><a href="#roadmap">5. Roadmap</a></li>
<li><a href="#financial-model">6. Financial Model</a></li>
<li><a href="#risk">7. Risk Assessment</a></li>
<li><a href="#summary">8. Summary</a></li>
</ol>
</div>
</nav>
<main class="wrap">
<!-- ====== 1. EXECUTIVE SUMMARY ====== -->
<section id="executive-summary">
<h2><span class="n">1</span>Executive Summary</h2>
<p>VerdictTank is a multi-vendor AI proposal review product. A founder, a proposal team, or a consultant submits a business proposal as a file or a URL. VerdictTank runs that document through a panel of ten independent AI reviewer seats spanning nine vendors, returns two separate scores across ten scored dimensions, explains in plain language exactly what is missing behind every low dimension, and hands back a structured Fix-It plan the submitter can execute. The submitter revises, re-submits, and re-scores, and the delta is shown dimension by dimension. Every review lands in a queryable corpus, and every public artifact leaves the system through a sanitization gate.</p>
<p>VerdictTank does not write proposals. It critiques them. That boundary is the product. A writing tool is incentivized to tell you the draft it produced is good. A critique tool is only valuable if it is willing to tell you the draft is not ready, name the reason, and quantify how far off it is. Every design decision in v4.1 follows from that boundary: the pre-submit coach asks structuring questions and never composes paragraphs, the panel scores independently before any synthesis, and the synthesis seat never scores at all.</p>
<p>The two scores are the reason a submitter trusts the output. A single blended number hides the most useful signal in proposal review, which is the gap between a strong idea in a weak document and a weak idea in a polished document. VerdictTank separates them:</p>
<ul>
<li><strong>Proposal Strength Score (0 to 100).</strong> How well the document makes its case: clarity, evidence, financial integrity, operating plan, compliance readiness.</li>
<li><strong>Investor Readiness Score (0 to 100).</strong> How well the underlying business survives scrutiny: market reality, differentiation, go-to-market, team fit, risk exposure.</li>
</ul>
<p>Ten dimensions feed those two scores, five to each. Every dimension carries a required, evidence-backed explanation sentence naming the specific missing artifact, not a grade with no reason attached.</p>
<p>The commercial model is five price points: Free at $0, One-Shot at $29, Pro at $119 per month, Enterprise at $699 per month, and White-Label at $3,000 per month. Standard-tier cost of goods sold is $0.36 per review at the ceiling, $0.43 loaded. White-Label, which seats four reserved premium models, is $3.00 per review at the ceiling, $3.60 loaded. Every paid tier clears 88 percent gross margin at its full included allotment, and the standard tiers clear 96 percent or better. Cost is not the binding constraint on this business; distribution is.</p>
</section>
<!-- ====== 2. THE FULL JOURNEY ====== -->
<section id="journey">
<h2><span class="n">2</span>The Full Journey</h2>
<p>VerdictTank is one continuous loop, not a scoring endpoint. The loop is the product.</p>
<h3>2.1 Pre-submit coach</h3>
<p>Before a submitter pays for anything, the coach is open and unlimited. It reads the draft in progress and asks structuring questions: where is the total addressable market (TAM) derivation, which competitor pricing is cited, what the operating plan assumes about hiring, which regulatory regime applies. It surfaces gaps. It never emits a score, and it never writes a full paragraph on the submitter's behalf. The submitter arrives at the review with a better draft, and the review is worth more because of it.</p>
<p>The coach is available on every tier including Free, and it is the top of the funnel. A submitter who has spent twenty minutes being asked hard questions about their own document already understands why the panel is worth paying for.</p>
<h3>2.2 Submit by file or URL</h3>
<p>Intake accepts DOCX, PDF, and TXT uploads, and it accepts pasted text. URL-to-Review accepts a link, extracts the page text, and converts it into the same canonical intake format a file upload produces, so a public pitch page or a hosted memo runs through the identical pipeline. Text is normalized to UTF-8, and a PII sanitizer redacts emails, phone numbers, and identifier-shaped patterns from the stored working text before any model call is dispatched. The original binary is preserved intact and separately.</p>
<h3>2.3 The ten-seat panel</h3>
<p>The standard panel is ten seats across nine vendors: nine independent scoring seats plus one synthesis and integrity gate that never scores. Every seat is dispatched in parallel against a single-pass contract, with a per-seat timeout and a per-seat fallback binding, and a pre-dispatch health probe runs against the rostered models before any spend is committed.</p>
<p>The nine scoring seats each own a distinct error class:</p>
<table>
<thead>
<tr><th>Seat</th><th>Error class it is built to catch</th></tr>
</thead>
<tbody>
<tr><td>Research Agent</td><td>grounding failures and context errors</td></tr>
<tr><td>Primary Reviewer</td><td>full-rubric anchor plus revenue arithmetic</td></tr>
<tr><td>Market-Reality</td><td>competitive mispositioning and TAM overstatement</td></tr>
<tr><td>Financial Integrity</td><td>revenue arithmetic and financial-model errors</td></tr>
<tr><td>Legal and Compliance</td><td>legal blockers and compliance gaps</td></tr>
<tr><td>Execution-Feasibility</td><td>execution infeasibility and timeline-scope errors</td></tr>
<tr><td>Team and Founder</td><td>team capacity and founder-fit gaps</td></tr>
<tr><td>Risk and Ethics Red-Team</td><td>safety-washing and overstated risk claims</td></tr>
<tr><td>Live Grounding</td><td>hallucinated facts and stale or uncited market data</td></tr>
</tbody>
</table>
<p>The tenth seat is the Synthesis and Integrity Gate. It reads all nine scoring outputs, reconciles them, computes the panel mean, median, and standard deviation per dimension, flags outliers at 1.5 sigma, records the spread between the anchor score and the cross-check scores, and issues the verdict. It has no scoring authority of its own, which is what makes it a gate rather than a tenth opinion.</p>
<p>Nine vendors are represented so that no single vendor's blind spots become the panel's blind spots. Vendor and model identity is server-side configuration only. It never appears on a customer-facing surface.</p>
<p>The panel is built for error detection density: the number of distinct material error classes surfaced per review, not the number of comments generated. A reviewer that produces forty stylistic notes and misses a broken revenue calculation has scored zero on the only metric that matters.</p>
<h3>2.4 Two scores across ten dimensions</h3>
<p>Each of the ten dimensions is tagged idea-facing or proposal-facing at generation time, and the two groups aggregate separately into the two published scores.</p>
<h4>Proposal Strength Score (proposal-facing, five dimensions)</h4>
<table>
<thead>
<tr><th>Dimension</th><th>What it measures</th></tr>
</thead>
<tbody>
<tr><td>Problem and Solution Clarity</td><td>whether the problem, the solution, and the causal link between them are stated without ambiguity</td></tr>
<tr><td>Evidence and Citation Quality</td><td>whether every load-bearing claim has a source, a date, and a derivation</td></tr>
<tr><td>Financial Model Integrity</td><td>whether the numbers reconcile, the unit economics close, and the assumptions are visible</td></tr>
<tr><td>Execution and Operating Plan</td><td>whether the plan has sequencing, owners, dependencies, and honest timelines</td></tr>
<tr><td>Compliance and Legal Readiness</td><td>whether the applicable regime is identified and the blockers are addressed</td></tr>
</tbody>
</table>
<h4>Investor Readiness Score (idea-facing, five dimensions)</h4>
<table>
<thead>
<tr><th>Dimension</th><th>What it measures</th></tr>
</thead>
<tbody>
<tr><td>Market Reality and Sizing</td><td>whether the market exists at the claimed size and the TAM is derived, not asserted</td></tr>
<tr><td>Competitive Differentiation</td><td>whether the moat survives contact with named, priced competitors</td></tr>
<tr><td>Go-to-Market and Traction</td><td>whether there is a repeatable path to the first and hundredth customer</td></tr>
<tr><td>Team and Founder Fit</td><td>whether the team can actually execute this plan at this scale</td></tr>
<tr><td>Risk and Ethics Exposure</td><td>whether the material risks are named honestly rather than minimized</td></tr>
</tbody>
</table>
<p>The two scores are published side by side with the disagreement delta between the anchor seat and the cross-check seats. The spread is signal, not noise: high panel agreement on a low dimension is a hard finding, and high disagreement is itself reported as a flag for the submitter to investigate.</p>
<h3>2.5 Explain the low score</h3>
<p>Every per-dimension score ships with a required explanation field. The field is evidence-backed and specific. A dimension score of 41 on Market Reality and Sizing does not return "market sizing is weak." It returns the concrete absence: no TAM calculation, no competitor pricing data, no source for the growth rate cited on page four. The explanation names the missing artifact, because a missing artifact is actionable and an adjective is not.</p>
<p>The explanation field is generated on the same pass as the score, so an explanation can never drift away from the number it justifies. Free tier receives the score summary; Pro and above receive the full per-dimension explanation set.</p>
<h3>2.6 The Fix-It plan</h3>
<p>After the verdict, VerdictTank generates a structured, prioritized, dimension-tagged remediation plan keyed to the lowest-scoring dimensions. Each item carries the finding, the specific fix, and where practical the instrument required to execute it: the formula to compute, the table template to fill, the citation target to obtain, the section to rewrite and what it must contain.</p>
<p>The plan is ordered by score impact, so a submitter with two hours works the top of the list rather than guessing. Pro and above receive the full structured plan. Free receives a one-paragraph summary, which is deliberate: the summary proves the plan exists and is specific, and the full plan is the upgrade.</p>
<h3>2.7 Revise and re-score</h3>
<p>The submitter revises against the Fix-It plan and re-submits through the re-score endpoint. A fresh panel runs, and the report shows a before-and-after delta per dimension along with both new scores. The original review is preserved as a historical version with full lineage, so the improvement trail is durable and auditable by the submitter. Re-scores are billed as reviews, which keeps the incentive honest: VerdictTank is paid to run panels, not to declare victory.</p>
<h3>2.8 Queryable review corpus</h3>
<p>Every review lands in a corpus record: both scores, all ten dimension scores, the explanation set, the findings, the conditions, the verdict, the remediation list, revision lineage, and the vertical classification. The corpus is the substrate for percentile context today and for outcome-calibrated scoring later.</p>
<p>Corpus handling is strict by construction. Raw proposal text is never corpus-eligible; the filter is enforced at the schema level, not by policy. Only structural and aggregate metadata is eligible, contribution is opt-in with the flag defaulting to false, identifiers are stripped, and any aggregate publication is gated behind a k-anonymity threshold. White-Label tenants are isolated: every corpus query filters by tenant, and a tenant-scoped credential cannot read across the boundary.</p>
<h3>2.9 Share, export, integrate</h3>
<p>The verdict leaves the system three ways, and all three pass the same gate.</p>
<ul>
<li><strong>Branded PDF report.</strong> A scripted pipeline renders the verdict record into the branded multi-section report with no manual steps, writes it to storage, and delivers it by authenticated download and by transactional email with a short-expiry link.</li>
<li><strong>Shareable report link.</strong> An opt-in, sanitized public score card behind an unguessable token, with optional expiry and immediate revoke. The card carries the verdict, the two scores, and the headline findings. It carries no raw proposal text and no vendor identity.</li>
<li><strong>Review-as-a-Service API.</strong> Submit, status, verdict, rescore, and export endpoints with scoped keys, so an accelerator or a proposal desk runs VerdictTank inside its own workflow.</li>
</ul>
<h3>2.10 The sanitization gate</h3>
<p>One gate governs every path out of the system. It strips personally identifiable information and it strips model and vendor identity from every public surface, including the free-text explanation and remediation fields where such identity is most likely to appear. It blocks deploys and it blocks artifacts; it is not an advisory scan. The PDF pipeline verifies the rendered output by extracting text from the finished file and asserting zero vendor names, zero model identifiers, and the presence of the non-removable AI disclaimer before the report is released.</p>
</section>
<!-- ====== 3. FEATURE SET ====== -->
<section id="features">
<h2><span class="n">3</span>Feature Set</h2>
<h3>3.1 Before the review</h3>
<div class="table-scroll">
<table>
<thead>
<tr><th>Feature</th><th>Free</th><th>One-Shot</th><th>Pro</th><th>Enterprise</th><th>White-Label</th></tr>
</thead>
<tbody>
<tr><td>Pre-submit coach (unlimited)</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>File upload intake (DOCX, PDF, TXT, paste)</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>URL-to-Review intake</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
</tbody>
</table>
</div>
<h3>3.2 During the review</h3>
<div class="table-scroll">
<table>
<thead>
<tr><th>Feature</th><th>Free</th><th>One-Shot</th><th>Pro</th><th>Enterprise</th><th>White-Label</th></tr>
</thead>
<tbody>
<tr><td>Panel size</td><td>4 scoring seats plus gate</td><td>10 seats</td><td>10 seats</td><td>10 seats</td><td>11 seats, premium models</td></tr>
<tr><td>Vendors represented</td><td>4</td><td>9</td><td>9</td><td>9</td><td>9</td></tr>
<tr><td>Two scores across ten dimensions</td><td>Single summary score</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Per-dimension explanations</td><td>Score summary only</td><td>Full</td><td>Full</td><td>Full</td><td>Full</td></tr>
<tr><td>Panel spread and outlier flags</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Vertical auto-classification</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Vertical templates</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Configurable Review Rules Engine</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr>
</tbody>
</table>
</div>
<h3>3.3 After the review</h3>
<div class="table-scroll">
<table>
<thead>
<tr><th>Feature</th><th>Free</th><th>One-Shot</th><th>Pro</th><th>Enterprise</th><th>White-Label</th></tr>
</thead>
<tbody>
<tr><td>Fix-It plan</td><td>1-paragraph summary</td><td>Full structured</td><td>Full structured</td><td>Full structured</td><td>Full structured</td></tr>
<tr><td>Re-score with per-dimension delta</td><td>No</td><td>No (re-purchase)</td><td>Yes, billed</td><td>Yes, billed</td><td>Yes, billed</td></tr>
<tr><td>Branded PDF report</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Shareable report link</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Review-as-a-Service API</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr>
<tr><td>Reviewer accuracy track record</td><td>Feeds data</td><td>Feeds data</td><td>Feeds data</td><td>Data plus dashboard</td><td>Data plus dashboard</td></tr>
<tr><td>White-Label track: domain, logo, email templates, portfolio console</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr>
</tbody>
</table>
</div>
<h3>3.4 Foundation, every tier</h3>
<table>
<thead>
<tr><th>Feature</th><th>Status</th></tr>
</thead>
<tbody>
<tr><td>Review corpus with two-score schema</td><td>All tiers</td></tr>
<tr><td>Sanitization gate on every public surface</td><td>All tiers, blocking</td></tr>
<tr><td>Automated PDF pipeline, zero manual steps</td><td>Paid tiers</td></tr>
<tr><td>Outcome-tracking cron at T+90, T+180, T+365</td><td>All tiers, accumulating</td></tr>
<tr><td>Tenant isolation on corpus and credentials</td><td>White-Label</td></tr>
<tr><td>Non-removable, versioned AI disclaimer on every report</td><td>All reports</td></tr>
</tbody>
</table>
<h3>3.5 Reviewer accuracy track record</h3>
<p>Every seat accumulates a track record from its own scores against later recorded outcomes. VerdictTank surfaces that track record on Enterprise and White-Label dashboards and continues accumulating it on every tier. v4.1 does not weight live verdicts by accuracy. A weighting scheme applied before the corpus can support it would be a confidence claim the data cannot back, so the track record is published and the verdict stays unweighted.</p>
</section>
<!-- ====== 4. PRICING ====== -->
<section id="pricing">
<h2><span class="n">4</span>Pricing</h2>
<p>Five price points. Monthly, with annual available on the three subscription tiers.</p>
<div class="table-scroll">
<table>
<thead>
<tr><th>Tier</th><th>Monthly</th><th>Annual (per month)</th><th>Included reviews</th><th>Panel</th><th>Implied per review</th><th>Overage</th></tr>
</thead>
<tbody>
<tr><td>Free</td><td>$0</td><td>n/a</td><td>1 lifetime</td><td>4 scoring seats plus gate</td><td>n/a</td><td>none</td></tr>
<tr><td>One-Shot</td><td>$29</td><td>none</td><td>1</td><td>Full 10-seat</td><td>$29.00</td><td>none</td></tr>
<tr><td>Pro</td><td>$119</td><td>$99</td><td>8 per month</td><td>Full 10-seat</td><td>$14.88</td><td>$18</td></tr>
<tr><td>Enterprise</td><td>$699</td><td>$582</td><td>50 per month</td><td>Full 10-seat</td><td>$13.98</td><td>$16</td></tr>
<tr><td>White-Label</td><td>$3,000</td><td>$2,499</td><td>100 per month</td><td>11-seat premium</td><td>$30.00</td><td>$28</td></tr>
</tbody>
</table>
</div>
<div class="pricing-grid">
<div class="price-card">
<div class="price-purpose">Free</div>
<div class="price">$0<span>/ lifetime</span></div>
<ul>
<li>One lifetime review</li>
<li>4 scoring seats plus gate</li>
<li>Single summary score</li>
<li>Unlimited pre-submit coach</li>
<li>Percentile context</li>
<li>One-paragraph Fix-It summary</li>
</ul>
<div class="price-note">A demonstration, not a workflow.</div>
</div>
<div class="price-card">
<div class="price-purpose">One-Shot</div>
<div class="price">$29<span>/ one review</span></div>
<ul>
<li>Full 10-seat panel</li>
<li>Both scores across ten dimensions</li>
<li>Full per-dimension explanations</li>
<li>Full structured Fix-It plan</li>
<li>Branded PDF report</li>
<li>Shareable report link</li>
</ul>
<div class="price-note">No overage. No annual plan. Revise and re-score by buying again or moving to Pro.</div>
</div>
<div class="price-card price-card--featured">
<div class="ribbon">Most popular</div>
<div class="price-purpose">Pro</div>
<div class="price">$119<span>/ month</span></div>
<ul>
<li>8 reviews per month</li>
<li>Full 10-seat panel</li>
<li>Both scores and full explanations</li>
<li>Full structured Fix-It plan</li>
<li>Re-score with per-dimension delta</li>
<li>Branded PDF and shareable link</li>
<li>Overage at $18</li>
</ul>
<div class="price-note">Annual: $99 per month. Implied per review: $14.88.</div>
</div>
<div class="price-card">
<div class="price-purpose">Enterprise</div>
<div class="price">$699<span>/ month</span></div>
<ul>
<li>50 reviews per month</li>
<li>Vertical templates</li>
<li>Configurable Review Rules Engine</li>
<li>Review-as-a-Service API</li>
<li>Accuracy dashboard</li>
<li>Overage at $16</li>
</ul>
<div class="price-note">Annual: $582 per month. Implied per review: $13.98.</div>
</div>
<div class="price-card">
<div class="price-purpose">White-Label</div>
<div class="price">$3,000<span>/ month</span></div>
<ul>
<li>100 reviews per month</li>
<li>11-seat premium panel, 4 reserved models</li>
<li>Custom domain, logo, email templates</li>
<li>Portfolio console</li>
<li>Tenant-isolated corpus</li>
<li>Overage at $28</li>
</ul>
<div class="price-note">Annual: $2,499 per month. Implied per review: $30.00.</div>
</div>
</div>
<h3>4.1 What each tier is for</h3>
<p><strong>Free, $0, one lifetime review on the reduced panel.</strong> Four scoring seats plus the gate, single summary score, percentile context, unlimited coach, and a one-paragraph Fix-It summary. It proves the panel is real without giving away the full ten-seat output. One review is lifetime, not monthly, so Free is a demonstration rather than a workflow.</p>
<p><strong>One-Shot, $29, one review on the full ten-seat panel.</strong> The bridge for the founder who needs one honest read and is not ready for a subscription. It runs the complete standard panel, both scores, the full per-dimension explanations, the full structured Fix-It plan, the branded PDF, and a shareable link. There is no overage, because a single purchase has nothing to exceed, and there is no annual plan, because it is not a subscription. The buyer who wants to revise and re-score buys again or moves to Pro.</p>
<p><strong>Pro, $119 per month, eight reviews.</strong> Eight reviews is a real iteration cadence: two per week, propose, review, revise, re-review. At $14.88 implied per review it prices below the one-off, so the subscription reads as the better deal for anyone actually iterating. Overage at $18 sits just above the included rate, which nudges heavy solo users toward the bundle they already have or up to Enterprise. Annual is $99 per month.</p>
<p><strong>Enterprise, $699 per month, fifty reviews.</strong> Roughly six times Pro's volume for roughly six times the price, so the ladder stays proportional and the upgrade is easy to justify. A team running multiple proposals and request-for-proposal (RFP) responses lands in the thirty to fifty range per month, so fifty is generous but bounded. Enterprise adds vertical templates, the Configurable Review Rules Engine, the API, and the accuracy dashboard. Overage at $16. Annual is $582 per month.</p>
<p><strong>White-Label, $3,000 per month, one hundred reviews.</strong> The reseller and consultancy tier. Custom domain, custom logo, branded email templates, a portfolio console, and tenant-isolated corpus segments, so a consultancy runs the panel entirely under its own brand. It is the only tier that seats the four reserved premium models, across eleven seats rather than ten: a premium primary reviewer, a premium execution-feasibility seat, a premium red-team seat, a premium synthesis gate, and a retained cross-check seat that preserves vendor diversity against the premium anchor. Per-review cost is roughly ten times standard, and the price carries it. Overage at $28 reflects the premium roster. Annual is $2,499 per month.</p>
<h3>4.2 Configurable Review Rules Engine</h3>
<p>Enterprise and White-Label administrators define additive custom checks through a no-code builder: compliance rules, brand-voice guidelines, internal investment criteria, mandatory sections. Custom rules layer on top of the fixed ten-dimension rubric. They never replace it and they never suppress a dimension score, so a tenant cannot configure away a finding it does not want to see. That constraint is what keeps a white-labeled verdict worth the same as a first-party one.</p>
</section>
<!-- ====== 5. ROADMAP ====== -->
<section id="roadmap">
<h2><span class="n">5</span>Roadmap</h2>
<h3>5.1 The v4.1 release track</h3>
<p>v4.1 is planned as five gated phases over twenty-four weeks, built by a solo developer plus an AI-agent build pipeline. The review engine and pipeline are proven in operation today; the client portal, billing, and API are the build. Each phase closes on a hard acceptance gate, and no phase closes on a self-report.</p>
<div class="table-scroll">
<table>
<thead>
<tr><th>Phase</th><th>Weeks</th><th>Deliverable</th><th>Closing gate</th></tr>
</thead>
<tbody>
<tr><td>0. Foundation and corpus</td><td>1 to 3</td><td>Blocking sanitization gate covering explanation and remediation fields; scripted PDF pipeline; corpus schema with two-score columns and reserved outcome columns; outcome cron scheduled</td><td>Zero manual sanitization steps in any public path; one review runs end to end into the corpus and out as a PDF with no manual step; cron logs its first run</td></tr>
<tr><td>1. Intake and scoring</td><td>4 to 8</td><td>Coach tier-wide; URL-to-Review alongside file upload; two scores as the panel's default output contract; per-dimension explanation as a required field; accuracy tracking begins</td><td>Every review emits two scores and a per-dimension explanation; coach and URL intake in production with zero gate failures; accuracy tracking records every seat on every review</td></tr>
<tr><td>2. Fix-It and reports</td><td>9 to 13</td><td>Structured Fix-It on Pro and above; shareable report links; vertical auto-classification and the first three vertical templates</td><td>Fix-It plan on 100 percent of Pro-and-above reviews; a shareable report passes the gate end to end; two of three vertical templates validated against known outcomes</td></tr>
<tr><td>3. Enterprise controls and API</td><td>14 to 18</td><td>Rules Engine to Enterprise and White-Label; Review-as-a-Service API to Enterprise; vertical templates to five</td><td>Rules Engine live with three Enterprise accounts; the API completes 100 reviews with zero gate failures</td></tr>
<tr><td>4. White-Label and GA</td><td>19 to 24</td><td>White-Label track live with custom domain, logo, email templates, portfolio console, tenant isolation; API generally available; five price points live on both surfaces</td><td>First White-Label pilot renews past month one; API generally available to Enterprise; marketing site and portal show the five price points with no stale pricing anywhere</td></tr>
</tbody>
</table>
</div>
<h3>5.2 What comes next</h3>
<p>v4.1 and beyond, in order of expected value:</p>
<ol>
<li><strong>Outcome-calibrated scoring.</strong> The outcome cron accumulates from launch day at T+90, T+180, and T+365, and the schema carries the outcome columns from day one, so no migration is required. Once the corpus clears a minimum-N threshold, the accuracy track record becomes a published dashboard and then a candidate weighting input.</li>
<li><strong>Competitive review comparisons.</strong> Full benchmarking of a new proposal against the corpus distribution, dimension by dimension and vertical by vertical, replacing the percentile context available today.</li>
<li><strong>Market simulation.</strong> Replaces the static financial table with a twelve-month trajectory model, adding a per-review cost that the Enterprise and White-Label price points absorb.</li>
<li><strong>Second-opinion audit agent.</strong> A dedicated blind-spot pass over the panel's own output, held until the corpus and accuracy data can measure its catch rate against a real baseline rather than an assumption.</li>
<li><strong>Adversarial red-team per vertical.</strong> Industry-specific attack vectors, sequenced after the vertical classifier has a proven accuracy record.</li>
</ol>
<h3>5.3 Explicit non-goals</h3>
<p>Scope is bounded on purpose:</p>
<ul>
<li>No community or peer-review surface of any kind. A public roast board carries moderation and abuse overhead with no revenue tie, and it dilutes the independent-panel identity that is the entire product.</li>
<li>No weighting of live verdicts by reviewer accuracy in v4.1. Track only.</li>
<li>No product-facing outcome dashboard in v4.1. The cron runs and accumulates; the dashboard follows the data.</li>
<li>No standard-tier access to the four reserved premium models.</li>
<li>No proposal ghostwriting, ever. The coach asks questions. The panel critiques. Neither one composes the submitter's argument for them.</li>
</ul>
</section>
<!-- ====== 6. FINANCIAL MODEL ====== -->
<section id="financial-model">
<h2><span class="n">6</span>Financial Model</h2>
<h3>6.1 Unit economics and cost of goods sold</h3>
<p>Costing assumption: 12,000 input tokens per scoring seat for a twenty-page proposal, roughly 40,000 characters, with the worker truncating above that; the synthesis gate reads roughly 20,000 input tokens. Per-seat cost is input rate times input tokens plus output rate times output budget. Dispatch is single-pass chat completion with no tool calling, which is the regime that makes a ten-seat panel cost cents rather than dollars. All margin guarantees below use the ceiling figure, which assumes every seat burns its full upper-bound output budget. The base figure is the typical case and is never used for a margin claim.</p>
<table>
<thead>
<tr><th>Roster</th><th>Seats</th><th>COGS ceiling per review</th><th>Loaded ceiling (x1.20)</th><th>COGS base per review</th></tr>
</thead>
<tbody>
<tr><td>Free reduced panel</td><td>5 model calls</td><td>$0.15</td><td>$0.18</td><td>$0.10</td></tr>
<tr><td>Standard panel</td><td>10</td><td>$0.36</td><td>$0.43</td><td>$0.25</td></tr>
<tr><td>White-Label premium panel</td><td>11</td><td>$3.00</td><td>$3.60</td><td>$1.52</td></tr>
</tbody>
</table>
<p>The loaded figure applies a flat 20 percent infrastructure and overhead buffer covering the application host, the database, object storage, the PDF renderer, and email delivery.</p>
<h3>6.2 Gross margin at full allotment consumption</h3>
<p>Margin is computed at the pessimistic bound: every included review consumed, every seat at its ceiling output budget, loaded cost.</p>
<table>
<thead>
<tr><th>Tier</th><th>Revenue</th><th>Included reviews</th><th>Loaded COGS at full consumption</th><th>Gross margin</th></tr>
</thead>
<tbody>
<tr><td>Free</td><td>$0</td><td>1 lifetime</td><td>$0.18 one time</td><td>loss leader</td></tr>
<tr><td>One-Shot</td><td>$29</td><td>1</td><td>$0.43</td><td>98.5%</td></tr>
<tr><td>Pro</td><td>$119</td><td>8</td><td>$3.44</td><td>97.1%</td></tr>
<tr><td>Enterprise</td><td>$699</td><td>50</td><td>$21.50</td><td>96.9%</td></tr>
<tr><td>White-Label</td><td>$3,000</td><td>100</td><td>$360.00</td><td>88.0%</td></tr>
</tbody>
</table>
<p>Overage is itself high margin by construction. Pro overage at $18 and Enterprise overage at $16 both sit far above the $0.43 loaded standard cost, so overage carries better than 95 percent margin while still reading as a nudge toward the next tier. White-Label overage at $28 against $3.60 loaded carries roughly 87 percent margin.</p>
<p>Stress case: if both premium reasoning seats in the White-Label roster burn a full 12,000-token output budget, per-review ceiling reaches $3.87, or $4.64 loaded. One hundred such reviews cost $464.40 against $3,000 revenue, which is 84.5 percent gross margin. The worst realistic case on the most expensive tier still clears 84 percent.</p>
<p>The structural conclusion is that cost of goods sold is not the constraint on this business. Even two hundred Enterprise reviews in a month cost roughly $86 loaded against $699 revenue. Review allotments are therefore set by value anchoring and ladder logic, not by cost recovery, and pricing pressure can be absorbed without touching the panel.</p>
<h3>6.3 Revenue projection, floor and ceiling</h3>
<p>Two scenarios at month twelve post-launch, measured as monthly recurring revenue (MRR). Both are stated as assumption sets, not forecasts. Both assume every included review is consumed, which overstates cost and understates margin.</p>
<h4>Floor scenario, month 12</h4>
<table>
<thead>
<tr><th>Line</th><th>Accounts or volume</th><th>Monthly revenue</th><th>Monthly loaded COGS</th></tr>
</thead>
<tbody>
<tr><td>One-Shot</td><td>40 purchases per month</td><td>$1,160</td><td>$17.20</td></tr>
<tr><td>Pro</td><td>35 accounts</td><td>$4,165</td><td>$120.40</td></tr>
<tr><td>Enterprise</td><td>3 accounts</td><td>$2,097</td><td>$64.50</td></tr>
<tr><td>White-Label</td><td>0 accounts</td><td>$0</td><td>$0.00</td></tr>
<tr><td><strong>Total</strong></td><td></td><td><strong>$7,422 MRR</strong></td><td><strong>$202.10</strong></td></tr>
</tbody>
</table>
<p>Floor gross margin: 97.3 percent. Annual run rate at month twelve: $89,064.</p>
<h4>Ceiling scenario, month 12</h4>
<table>
<thead>
<tr><th>Line</th><th>Accounts or volume</th><th>Monthly revenue</th><th>Monthly loaded COGS</th></tr>
</thead>
<tbody>
<tr><td>One-Shot</td><td>150 purchases per month</td><td>$4,350</td><td>$64.50</td></tr>
<tr><td>Pro</td><td>180 accounts</td><td>$21,420</td><td>$619.20</td></tr>
<tr><td>Enterprise</td><td>14 accounts</td><td>$9,786</td><td>$301.00</td></tr>
<tr><td>White-Label</td><td>4 accounts</td><td>$12,000</td><td>$1,440.00</td></tr>
<tr><td><strong>Total</strong></td><td></td><td><strong>$47,556 MRR</strong></td><td><strong>$2,424.70</strong></td></tr>
</tbody>
</table>
<p>Ceiling gross margin: 94.9 percent. Annual run rate at month twelve: $570,672.</p>
<p>Free tier cost is a one-time charge per account rather than recurring, since Free grants one lifetime review. At $0.18 loaded per Free review, one thousand two hundred cumulative Free reviews cost $216 in total and six thousand cost $1,080 in total. Free is affordable at any signup volume the funnel can realistically produce, which is why the reduced panel exists rather than a time-limited trial.</p>
<p>Annual billing at $99, $582, and $2,499 per month trades 16.7 percent of headline revenue, two months free, for twelve months of committed cash and materially lower churn exposure. At the ceiling scenario, a fifty percent annual mix on Pro and Enterprise reduces month-twelve MRR by roughly $2,619 and converts roughly $156,000 of annualized revenue into prepaid commitment.</p>
<h3>6.4 What moves the model</h3>
<p>Sensitivity, ranked:</p>
<ol>
<li><strong>Pro account count.</strong> Pro is the volume tier and the largest single revenue line in both scenarios. It is the number to move.</li>
<li><strong>White-Label logos.</strong> Each White-Label account is worth roughly twenty-five Pro accounts. Landing one changes the shape of the revenue curve; landing four is the difference between the floor and the ceiling scenario.</li>
<li><strong>One-Shot to Pro conversion.</strong> One-Shot is priced as a bridge, and its value is mostly in what fraction of buyers subscribe after seeing the full ten-seat output once.</li>
<li><strong>Enterprise seat expansion.</strong> Enterprise is the highest-effort sale, and the Rules Engine and API are the features that make it defensible rather than a volume discount.</li>
<li><strong>Cost of goods sold.</strong> Last, and by a wide margin. A doubling of every model rate in the panel would still leave Pro above 94 percent gross margin.</li>
</ol>
</section>
<!-- ====== 7. RISK ASSESSMENT ====== -->
<section id="risk">
<h2><span class="n">7</span>Risk Assessment</h2>
<p>Ranked by expected impact on the product at launch, each with the control that is in place.</p>
<h3>7.1 Reviewer availability <span class="tag tag-red">High impact</span></h3>
<p>The panel depends on nine vendors, and any one of them can rate-limit, exhaust credit, or return transport errors. Controls: a pre-dispatch health probe runs one cheap call per rostered model before spend is committed; every seat carries a named fallback binding; a review completes on a documented reduced panel rather than failing when a seat cannot be filled, and any review that ran reduced is flagged as such on the report and in the corpus record. The live-grounding seat is the single most availability-sensitive seat in the standard roster and is provisioned with a direct vendor credential rather than a shared route, plus a same-vendor-class fallback that is already live in the panel.</p>
<h3>7.2 Model output integrity <span class="tag tag-red">High impact</span></h3>
<p>Reasoning models can truncate structured output at low token caps, and some models constrain sampling parameters. Controls: per-seat output budgets are sized above the truncation threshold for every reasoning seat rather than set to a global default; seats with sampling constraints carry an explicit per-seat parameter quirk in the roster configuration; every seat response is schema-validated before it enters synthesis, and a seat returning unparseable output is retried once and then fails over rather than silently contributing a null score. Models without a published per-token input cost are not seated in any cost-guaranteed tier at all.</p>
<h3>7.3 Cost verification <span class="tag tag-amber">Medium impact</span></h3>
<p>Two standard-roster seats bill through providers whose usage responses do not always carry a cost field, so their per-review contribution is computed from configured rates rather than reconciled against metered spend. Controls: those seats are provisioned with native vendor credentials so spend meters end to end; the margin guarantee uses the ceiling COGS across the whole roster, which absorbs a material rate change on any single seat; the combined ceiling contribution of the two affected seats is $0.0632 per review against a $0.36 total, so even a doubling of both moves loaded standard COGS from $0.43 to roughly $0.51 and leaves Pro above 96 percent margin. Base COGS figures are internal planning numbers and are not quoted externally.</p>
<h3>7.4 Sanitization and confidentiality <span class="tag tag-red">High impact if it fails</span></h3>
<p>A critique product handles unreleased strategy documents, and a leak of raw text or reviewer identity is an existential trust failure rather than a bug. Controls: the sanitization gate blocks rather than warns, and it covers the free-text explanation and remediation fields alongside structured fields; raw proposal text is schema-level ineligible for the corpus; corpus contribution is opt-in and defaults to false; the PDF pipeline extracts text from the finished file and asserts zero vendor and model identifiers plus the verbatim presence of the AI disclaimer before release; share tokens are unguessable, expirable, and revocable, and a revoked token returns not-found; tenant isolation is enforced on every corpus query and every scoped credential.</p>
<p>VerdictTank does not use submitted proposals to build its own products, train models, or inform its own proposals. A submission is processed only to produce that submitter's review, and is retained only for the submitter's own reference and legal record. Review processing does run through third-party AI providers under their own data-handling terms.</p>
<h3>7.5 Verdict liability <span class="tag tag-amber">Medium impact</span></h3>
<p>A submitter can act on a verdict and attribute an outcome to it. Controls: a versioned, non-removable AI disclaimer renders on every report and cannot be templated away; inter-seat agreement and the panel spread are published beside every score so confidence is visible rather than implied; the liability cap is the greater of $100 or twelve months of fees; the status page distinguishes VerdictTank incidents from upstream provider incidents so an outage is not misread as a product defect.</p>
<h3>7.6 Positioning drift <span class="tag tag-amber">Medium impact and slow</span></h3>
<p>The most likely way this product degrades is by drifting toward writing. Customers will ask for it, and a generated paragraph is easier to deliver than an honest score. Controls: the coach is architecturally forbidden from composing paragraphs, the Rules Engine is additive only and cannot suppress a dimension score, and no roadmap item shifts VerdictTank toward authorship. We critique them; we don't write them, and that is a product constraint, not a slogan.</p>
<h3>7.7 Concentration and capacity <span class="tag tag-amber">Medium impact</span></h3>
<p>v4.1 is built and operated by a solo developer plus an AI-agent build pipeline, and the review worker is serial at launch. Controls: the review state machine is durable and replayable, with per-seat evidence stored so a partial panel resumes rather than restarting; the scale path is a depth cap plus parallel workers, which is a configuration change and not a redesign; the full customer-facing stack and the review engine sit on a single host with one state machine, so there is no cross-host coordination to debug under load. At the ceiling scenario, month-twelve volume is roughly 2,690 reviews per month, which a serial worker at a sixty-second critical path clears with substantial headroom.</p>
<h3>7.8 Distribution <span class="tag tag-red">Highest impact overall</span></h3>
<p>The panel works and the margins are structurally excellent, which means the binding risk is that not enough submitters find the product. Controls: Free is a real full-loop demonstration on a reduced panel rather than a time-limited trial; One-Shot at $29 removes the subscription objection entirely; the coach is unlimited on every tier and is the widest part of the funnel; shareable report links put a branded verdict in front of the submitter's own investors and advisors; the API and the White-Label track make other people's distribution into ours.</p>
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<h2><span class="n">8</span>Summary</h2>
<p>VerdictTank v4.1 ships the complete critique loop: coach the draft, submit a file or a URL, run ten seats across nine vendors, publish a Proposal Strength Score and an Investor Readiness Score across ten dimensions, explain every low dimension with the specific missing artifact, hand back a prioritized Fix-It plan, re-score the revision with a per-dimension delta, keep it all in a queryable corpus, and ship the verdict as a branded PDF, a shareable card, or an API response with a sanitization gate on every path out.</p>
<p>Five price points cover the range from a single honest read at $29 to a fully branded reseller platform at $3,000 per month, and every paid tier clears 88 percent gross margin at full consumption with the standard tiers above 96 percent. The economics are settled. The architecture is proven. What remains is the build, then distribution.</p>
<div class="positioning-statement">We critique them; we don't write them.</div>
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VerdictTank v4.1 - AI Proposal Review &amp; Scoring<br>
We critique them; we don't write them.
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