Research Continuum Vision — MoE Judges Assessment Report

Document: CODITECT-Research-Continuum-MoE-Assessment
Version: 1.0.0
Purpose: Independent multi-judge evaluation of the Research Continuum Vision Document
Target: internal/analysis/research-continuum/CODITECT-Research-Continuum-Vision-Document.md
Date: 2026-02-11
Panel Size: 5 judges
Weighted Consensus Score: 7.4/10
Verdict: APPROVED WITH CONDITIONS

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Executive Summary

Five independent judges evaluated the CODITECT Research Continuum Vision Document across seven dimensions. The panel returned a weighted consensus score of 7.4/10 with a verdict of APPROVED WITH CONDITIONS.

Key Findings

The vision is technically credible and category-defining, anchored by production-validated extraction (218/218 Grade A papers, avg 0.898). The compounding knowledge graph moat is the strongest strategic asset. However, the document has critical gaps in financial modeling, team credentials, and customer validation that must be addressed before investor presentation.

Verdict: Proceed with Structured Remediation

The Research Continuum concept is fundable at Seed/Series A with the following remediation:

1. Immediate (1-2 weeks): Add unit economics model, team section, customer validation evidence, risk mitigation matrix
2. Near-term (1 month): Build realistic 3-year financial model ($500K→$3M→$12M trajectory, not $500K→$10M→$100M), define knowledge graph architecture (ADR required), validate on diverse corpus (medical, legal, business — not just arXiv ML papers)
3. Medium-term (2-3 months): Develop investor deck (10-12 slides), secure 2-3 LOIs from target customers, prototype knowledge graph with 1,000+ papers

Value Proposition (Refined by Panel)

CODITECT Research Continuum transforms static document collections into compounding knowledge assets. Unlike search tools that find papers or chatbots that summarize them, Research Continuum creates a persistent, evolving knowledge graph where every paper enriches every future query — making organizations' research investments compound over time rather than depreciate.

Course of Action

Phase	Timeline	Action	Owner
1	Week 1-2	Remediate vision document per judge recommendations	Product
2	Week 2-4	Design knowledge graph architecture (ADR-174)	Architecture
3	Month 2	Validate on diverse corpus (medical + legal + business)	Engineering
4	Month 2-3	Build investor deck, secure LOIs	Business Dev
5	Month 3-4	Prototype knowledge graph with 1,000+ papers	Engineering
6	Month 4-6	Seed fundraise with validated metrics	Founders

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Judge Panel Composition

#	Judge Role	Agent	Model Family	Weight	Perspective
1	Venture Capital Analyst	venture-capital-business-analyst	Anthropic	30%	Investment readiness, market sizing, unit economics
2	Competitive Market Analyst	competitive-market-analyst	Anthropic	25%	Competitive landscape, market positioning, threats
3	Business Intelligence Analyst	business-intelligence-analyst	Anthropic	20%	Financial rigor, market validation, cost modeling
4	Documentation Quality Agent	documentation-quality-agent	Anthropic	15%	Document structure, clarity, completeness
5	Senior Technical Architect	senior-architect	Anthropic	10%	Technical feasibility, codebase reality check

Model Diversity: Single provider (Anthropic Claude Opus 4.6) — future evaluations should incorporate OpenAI and DeepSeek for cross-model validation per H.3.5 requirements.

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Scoring Matrix

Per-Dimension Scores

Dimension	VC (30%)	Market (25%)	BI (20%)	DocQuality (15%)	Architect (10%)	Weighted
Market Opportunity	6	6	6	8	8	6.5
Moat Defensibility	8	7	7	9	7	7.6
Technical Feasibility	9	9	9	10	6	8.7
Revenue Model	5	5	4	6	7	5.2
GTM Strategy	7	7	7	7	8	7.1
Vision Clarity	9	8	8	10	9	8.7
Document Quality	8	8	8	7	9	8.0
Overall	7.3	7.1	6.7	7.9	7.4	7.4

Grade Distribution

Score Range	Grade	Count	Judges
9-10	Excellent	0	—
7-8	Approved	4	VC (7.3), Market (7.1), DocQuality (7.9), Architect (7.4)
5-6	Conditional	1	BI (6.7)
3-4	Revision Required	0	—
1-2	Rejected	0	—

Dimension Analysis

Dimension	Weighted Score	Assessment	Critical Gaps
Vision Clarity	8.7	EXCELLENT — Category-defining narrative, compelling flywheel metaphor	None
Technical Feasibility	8.7	STRONG — Production-validated extraction layer, but only 20-25% of full stack built	Knowledge graph, synthesis, orchestration layers don't exist yet
Document Quality	8.0	GOOD — Well-structured draft, but 6K words is too long for investor audience	Needs 50% reduction, add diagrams, create 1-page exec summary
Moat Defensibility	7.6	GOOD — Compounding knowledge graph is genuine moat, but claim of "3-4 year lead" is overstated	Revise to 18-24 months; agents are prompt templates, not trained models
GTM Strategy	7.1	ADEQUATE — Correct beachhead (computational biology), but no customer validation	Need LOIs, pilot commitments, pricing validation
Market Opportunity	6.5	NEEDS WORK — $1.8B TAM claim unsupported; real addressable market is smaller	Bottom-up TAM model required; literature review software is $680M-$1.5B
Revenue Model	5.2	WEAKEST — No unit economics, no CAC/LTV, no inference cost modeling	776+ agents = $5-$50/query cost; $500K→$100M trajectory implausible

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Narrative Findings

1. The Vision Is Category-Defining (Consensus: Strong)

All five judges recognized the Research Continuum as a genuinely novel concept. The insight that knowledge should compound rather than depreciate — that each paper processed enriches every future query — represents a defensible category-creation opportunity. The "knowledge production-consumption asymmetry" framing (2.5M papers/year vs 250 readable/year) resonates as a clear, quantifiable pain point.

Judge 4 (Doc Quality) awarded the highest marks: "The vision narrative is a 10/10. The flywheel concept is immediately intuitive and the progression from extraction to synthesis to generation is compelling."

Judge 1 (VC) concurred: "This is a category-creating vision, not an incremental improvement. The compounding knowledge graph moat — where each paper processed enriches the graph — creates a genuine barrier to entry that improves with scale."

2. Technical Execution Is Proven — But Only for Layer 1 (Split: 4-1)

Four judges rated technical feasibility 9-10/10 based on the demonstrated results: 218/218 Grade A papers, multi-source extraction (Docling PDF + ar5iv HTML + arXiv LaTeX), and UDOM's 25-type component taxonomy. This is production-grade work.

However, Judge 5 (Architect) provided the critical dissent after inspecting the actual codebase:

"I inspected the codebase directly. The knowledge graph layer, synthesis layer, interface layer, and orchestration layer do not exist. There is no graph schema, no graph database, no synthesis code, no user-facing interface. The extraction layer is approximately 20-25% of the full Research Continuum stack. The vision document implies these layers are designed or architected — they are not."

Furthermore, the 218 test papers are all Yann LeCun's arXiv publications — a homogeneous, cherry-picked corpus of ML papers with consistent LaTeX formatting. Real-world validation requires diverse corpora: medical literature (PubMed), legal documents, business reports, patents.

Panel consensus: The extraction layer is genuinely impressive and production-ready. But the remaining 75-80% of the vision is aspirational. The document should clearly distinguish between "built" and "planned."

3. Financial Model Is the Weakest Link (Consensus: Critical Gap)

All five judges flagged the revenue model as insufficient:

• No unit economics. What does it cost to process one paper? With 776+ agents potentially involved, inference costs could be $5-$50 per deep analysis query. No CAC, LTV, or payback period is defined.
• Implausible growth trajectory. The implied $500K→$10M→$100M revenue curve in 3 years is not supported by any bottom-up model.
• No pricing validation. Would computational biology labs pay $50K/year? $200K/year? No customer evidence is cited.

Judge 3 (BI) performed independent market research and found:

• Computational biology market: $7.4B (broader than claimed)
• Literature review software: $680M-$1.5B (much smaller than $1.8B TAM claim)
• Realistic 3-year trajectory: $500K→$3M→$12M with 20-30 enterprise customers

4. Competitive Moat Is Real but Overstated (Consensus: Moderate)

The compounding knowledge graph is a genuine moat — once built. But:

• "3-4 year lead" → 18-24 months maximum. OpenAI, Google DeepMind, Semantic Scholar, and Elicit all have resources to replicate extraction capabilities rapidly. The lead time is in domain-specific knowledge graph depth, not extraction technology.
• Agents are prompt templates, not trained models. The 150+ CODITECT agents are markdown prompt files, not fine-tuned or trained systems. Any well-resourced competitor can replicate this approach in weeks.
• The moat materializes only when the knowledge graph exists. Until then, this is a PDF-to-markdown converter — impressive but not defensible.

Judge 2 (Market) ranked competitive threats:

1. OpenAI Research GPT (highest threat — massive resources, researcher user base)
2. Semantic Scholar + Anthropic partnership (curated academic graph + leading LLM)
3. Elicit expansion (already has research workflow product)

5. Document Structure Needs Executive Refinement (Consensus: Moderate)

The document is strong as a technical vision but not investor-ready in its current form:

Missing sections (critical):

• Team & credentials
• Traction & validation evidence
• The Ask (funding amount, use of proceeds)
• Risk factors & mitigation
• Competitive response matrix
• Timeline/roadmap with milestones

Structural issues:

• 6,000+ words is 3x too long for an investor document
• No diagrams or visuals (need 3-5 architecture/flywheel diagrams)
• Should produce a 1-page executive summary PDF alongside the full document

Judge 4 (Doc Quality) estimated 10-14 hours of remediation work to reach investor-ready quality.

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Judge-Specific Detailed Assessments

Judge 1: Venture Capital Analyst (Score: 7.3/10)

Verdict: APPROVED WITH CONDITIONS

Strengths:

1. Compounding knowledge graph moat — genuine network effect
2. 100% Grade A extraction — proves team can ship
3. Category-defining vision with clear pain point articulation

Weaknesses:

1. TAM of $1.8B is top-down only — no bottom-up validation
2. No unit economics (CAC, LTV, payback period)
3. No team section — investors fund teams, not technology
4. Revenue model lacks pricing tiers and customer willingness-to-pay data

Required Actions:

• Build bottom-up TAM model from target customer count × ACV
• Add unit economics section with inference cost modeling
• Add team credentials section
• Provide 2-3 customer validation data points (even informal)

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Judge 2: Competitive Market Analyst (Score: 7.1/10)

Verdict: APPROVED WITH CONDITIONS

Strengths:

1. Technical execution is ahead of all known competitors
2. Correct architectural insight — knowledge should compound, not be searched
3. "Agentic Knowledge Infrastructure" is a defensible new category framing

Weaknesses:

1. Market sizing lacks rigor — $1.8B not decomposed
2. Beachhead in computational biology may be too narrow for Series A story
3. Competitive response from well-funded players is underestimated

Competitive Threat Ranking:

Rank	Competitor	Threat Level	Why
1	OpenAI Research GPT	Critical	Massive resources, 200M+ users, can ship quickly
2	Semantic Scholar + Anthropic	High	Curated academic graph + best-in-class LLM
3	Elicit	High	Already has research workflow, funded, growing
4	Consensus/Scite	Medium	Citation analysis, limited scope
5	Google DeepMind	Medium	Resources but different strategic priorities

Required Actions:

• Decompose TAM by vertical (bio, legal, pharma, finance)
• Define competitive response strategy for each major threat
• Expand beachhead narrative beyond single vertical

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Judge 3: Business Intelligence Analyst (Score: 6.7/10)

Verdict: PROMISING BUT REQUIRES FINANCIAL RESTRUCTURING

Strengths:

1. Production-grade validation evidence (218 papers, zero failures)
2. Multi-vertical opportunity (bio, pharma, legal, finance)
3. Compelling pain articulation with quantified metrics

Weaknesses:

1. Revenue trajectory is financially implausible ($500K→$100M in 3 years)
2. Cost structure understated — 776+ agents at inference cost = $5-$50/query
3. Competitive moat unclear vs. incumbents with existing data assets

Market Validation (Independent Research):

Market Segment	Size	Source
Computational Biology	$7.4B	Grand View Research
Literature Review Software	$680M-$1.5B	Market analysts
Research Analytics	$2.1B	Gartner

Recommended Financial Model:

Year	Revenue	Customers	ACV	Rationale
Y1	$500K	5-8	$75K	Seed customers, discounted pilots
Y2	$3M	20-25	$130K	Beachhead expansion, full pricing
Y3	$12M	50-60	$200K	Multi-vertical, knowledge graph moat active

Required Actions:

• Build realistic 3-year financial model
• Model inference costs per query/per customer
• Define pricing tiers with cost-plus analysis
• Show path to gross margin >70%

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Judge 4: Documentation Quality Agent (Score: 7.9/10)

Verdict: STRONG DRAFT REQUIRING EXECUTIVE REFINEMENT

Strengths:

1. Vision narrative — 10/10, immediately compelling
2. Technical credibility — 10/10, backed by production results
3. Competitive differentiation — 9/10, clear category creation

Weaknesses:

1. Missing critical sections: team, traction, the ask, risks, timeline
2. Document length (6K+ words) — 3x too long for investor audience
3. Financial model incomplete — no unit economics

Document Remediation Plan:

Action	Priority	Effort
Add Team & Credentials section	P0	2h
Add Traction & Validation section	P0	3h
Add The Ask (funding, use of proceeds)	P0	1h
Add Risk Factors & Mitigation	P1	2h
Reduce to 3,000 words	P1	3h
Add 3-5 diagrams (flywheel, architecture, TAM)	P1	4h
Create 1-page executive summary PDF	P2	2h
Total estimated remediation		~14h

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Judge 5: Senior Technical Architect (Score: 7.4/10)

Verdict: CONDITIONAL PROCEED

Critical Finding — Codebase Reality Check:

Layer	Vision Document Implies	Actual Codebase State
Extraction (UDOM)	Built, production-ready	Built — 218/218 Grade A, 3-source pipeline
Knowledge Graph	Designed, architecture planned	Does not exist — no schema, no code, no ADR
Synthesis Layer	Part of the agent system	Does not exist — no synthesis logic
Interface Layer	Navigator serves content	Partial — static viewer only, no interactive query
Orchestration	776+ agents coordinate	Prompt templates — markdown files, not trained models

Technical Readiness: 20-25% of full vision

Strengths:

1. Extraction layer is genuinely world-class
2. UDOM schema (25 types) is well-designed for extensibility
3. Multi-source alignment is a genuine technical achievement

Weaknesses:

1. 218 papers are all Yann LeCun arXiv publications — homogeneous corpus
2. Knowledge graph is the core differentiator but has zero implementation
3. "3-4 year lead" is not credible — 18-24 months maximum
4. Agent "moat" is prompt templates, not proprietary technology

Required Actions:

1. Design knowledge graph architecture (ADR-174) — schema, technology choice (Neo4j/FoundationDB/custom), entity types, relationship taxonomy
2. Validate on diverse corpus — at minimum: medical (PubMed), legal (case law), business (SEC filings)
3. Define agent orchestration architecture — how do 776+ agents coordinate for synthesis?
4. Clearly distinguish "built" vs. "planned" in all communications

Path to Production (Estimated):

Component	Effort	Timeline
Knowledge graph v1	6-8 weeks	Month 1-2
Cross-document entity linking	4-6 weeks	Month 2-3
Synthesis engine v1	8-12 weeks	Month 3-5
Interactive query interface	4-6 weeks	Month 4-6
Multi-corpus validation	2-4 weeks	Month 2-3
Total to MVP		6-8 months

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Consensus Recommendations

Must-Do (All 5 Judges Agree)

1. Add unit economics and realistic financial model — inference costs, CAC/LTV, pricing tiers
2. Add team section — credentials, track record, domain expertise
3. Validate on diverse corpus — break out of arXiv ML papers
4. Clearly distinguish built vs. planned — extraction is built, knowledge graph is vision
5. Design knowledge graph architecture — this is the moat, and it has zero design work

Should-Do (3+ Judges Agree)

6. Reduce document to 3,000 words with 1-page exec summary
7. Add competitive response strategy
8. Build bottom-up TAM model
9. Add risk factors and mitigation matrix
10. Define "The Ask" — funding amount, use of proceeds, milestones

Consider (2 Judges)

11. Revise moat timeline from "3-4 years" to "18-24 months"
12. Add 3-5 diagrams (flywheel, architecture, TAM visualization)
13. Prototype knowledge graph with 1,000+ papers before fundraise

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Assessment Methodology

Process

1. Deliverable Analysis — Vision document read in full (429 lines, ~8,000 tokens)
2. Judge Selection — 5 judges selected for investment readiness evaluation
3. Independent Evaluation — Each judge evaluated independently via Task subagent
4. Dimension Scoring — 7 dimensions, 1-10 scale per judge
5. Weighted Synthesis — Scores combined using judge weights
6. Consensus Extraction — Cross-judge agreement analysis

Scoring Rubric

Score	Meaning
9-10	Exceptional — exceeds standards, ready as-is
7-8	Good — minor improvements needed
5-6	Adequate — significant gaps to address
3-4	Below standard — major revision required
1-2	Inadequate — fundamental rethink needed

Evaluation Dimensions

Dimension	Definition
Market Opportunity	TAM/SAM/SOM clarity, market sizing rigor, growth potential
Moat Defensibility	Competitive barriers, network effects, switching costs
Technical Feasibility	Implementation readiness, proof points, architecture soundness
Revenue Model	Unit economics, pricing strategy, growth trajectory realism
GTM Strategy	Beachhead selection, customer acquisition, go-to-market plan
Vision Clarity	Narrative quality, problem articulation, future state clarity
Document Quality	Structure, completeness, audience-appropriateness

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Assessment Date: 2026-02-11 Panel Assembled By: Claude (Opus 4.6) Task ID: T.6 Related Documents:

• Vision Document: internal/analysis/research-continuum/CODITECT-Research-Continuum-Vision-Document.md
• Architecture Decision: internal/architecture/adrs/ADR-174-research-continuum-agentic-knowledge-infrastructure.md