From 90f35df3e8a9cb53dc4d584b45fa5f647b4cc496 Mon Sep 17 00:00:00 2001 From: PAE Date: Sat, 2 May 2026 04:00:02 +0000 Subject: [PATCH] proposal: company_proposal task={task.id} --- ...al-59b34e1f-17c6-4cca-86b4-dfcb1f9200ae.md | 95 +++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 deliverables/proposals/proposal-59b34e1f-17c6-4cca-86b4-dfcb1f9200ae.md diff --git a/deliverables/proposals/proposal-59b34e1f-17c6-4cca-86b4-dfcb1f9200ae.md b/deliverables/proposals/proposal-59b34e1f-17c6-4cca-86b4-dfcb1f9200ae.md new file mode 100644 index 0000000..cf98afa --- /dev/null +++ b/deliverables/proposals/proposal-59b34e1f-17c6-4cca-86b4-dfcb1f9200ae.md @@ -0,0 +1,95 @@ +# Proposal: Foreman Assessments Group (FAG) - Operationalizing AI Benchmarking + +**Prepared For:** Executive Strategy Committee +**Prepared By:** [Your Name/Team] +**Date:** October 26, 2023 +**Version:** 1.0 + +--- + +## 1. Executive Summary + +The exponential growth and subsequent hyper-saturation of large language models (LLMs) have created an urgent, inadequately measured market. Current reliance on vendor marketing claims, anecdotal evidence, and flawed single-metric benchmarks (e.g., simple GPT-3.5 prompts) provides an incomplete, often misleading, view of model capability. + +**Foreman Assessments Group (FAG)** proposes the institutionalization of a comprehensive, repeatable, and multifaceted AI benchmarking framework. This framework transforms research from an ad-hoc activity into a scalable, objective operational function. + +**Our Mission:** To provide defensible, measurable, and comparative performance evaluations of commercial and open-source AI models across critical, real-world vectors (Reasoning, Safety, Context Handling, Multimodality). + +**Key Deliverables:** +1. **The FAG Benchmark Suite:** A proprietary library of validated, challenging evaluation prompts and datasets. +2. **Operational API Scoring:** A middleware service allowing clients to submit models/prompts for standardized scoring against the FAG Suite. +3. **Insight Reports:** Deep-dive comparative reports highlighting strengths, weaknesses, and emergent ethical risks per model family. + +--- + +## 2. The Problem & The Opportunity + +### 2.1 The Problem: The Benchmark Churn +The AI evaluation industry suffers from "Benchmark Churn"--where proprietary metrics are introduced, rapidly retired, or proven insufficient against escalating model complexity. +* **Hallucination Risk:** Models are excellent at sounding confident, but poor at *being* correct. Current metrics fail to capture *why* or *where* the failure occurred. +* **Lack of Context Depth:** Most benchmarks test isolated skills (Code generation OR Summarization), failing to test the *integration* of skills in long-form, real-world tasks (e.g., "Diagnose this error in this codebase, given these three unrelated user documents"). +* **Opacity:** Evaluation results are often black boxes, preventing clients from understanding the underlying comparative advantage. + +### 2.2 The Opportunity: From Evaluation to Intelligence Layer +By developing a rigorous, modular assessment layer, FAG can position itself not merely as a testing service, but as the **Intelligence layer *above* the models.** We monetize *certainty* and *comparative insight*, which commands a substantial premium from high-stakes enterprise deployment clients (Finance, Pharma, Legal). + +--- + +## 3. Our Solution: The FAG Framework (The 4 Pillars) + +The FAG Framework evaluates models across four non-negotiable operational pillars, moving beyond simple accuracy scores. + +| Pillar | Assessment Goal | Sample Test Vectors | Metric Output | +| :--- | :--- | :--- | :--- | +| **1. Deep Reasoning (The "Why")** | Assessing logical consistency, multi-step deduction, and counterfactual thinking. | Chain-of-Thought (CoT) decomposition on abstract reasoning puzzles; diagnosing failure modes in simulated systems. | Logical Path Score (LPS) / Deviation Rate. | +| **2. Context & Memory (The "How Much")** | Evaluating the ability to maintain coherence, recall details, and synthesize information across huge contexts. | Long-Context Summarization with cross-referencing; RAG/QA on thousands of pages of technical documentation. | Context Recall Score (CRS) / Information Decay Rate. | +| **3. Safety & Alignment (The "Should")** | Stress-testing for bias, guardrail bypass, refusal policy adherence, and toxic output generation. | Red Teaming against known prompt injection vectors; bias testing across demographics. | Alignment Index Score (AIS) / Exploit Surface Area. | +| **4. Modality Synthesis (The "What")** | Testing the seamless integration of different data types (e.g., interpreting a graph *and* writing legal text about it). | Image Captioning paired with natural language inference; Video summarization to board presentation markdown. | Modality Synthesis Ratio (MSR) / Data Integrity Score. | + +--- + +## 4. Implementation Roadmap & Phasing + +We propose a phased, iterative rollout to manage resource allocation while proving MVP value quickly. + +### Phase 1: MVP & Validation (0 - 3 Months) +* **Focus:** Pillars 1 (CoT Reasoning) and 2 (Long Context QA). +* **Product:** Launch the "FAG Reasoning Validator API." +* **Goal:** Secure 2-3 pilot clients in a single vertical (e.g., Financial Industry) to validate the scoring model against their internal gold standards. +* **Deliverable:** Proof-of-Concept validation report. + +### Phase 2: Core Platform Build (4 - 9 Months) +* **Focus:** Integrating Pillars 3 (Safety) and 4 (Multimodality). +* **Product:** Launch the full **FAG Assessment API**. +* **Goal:** Achieve full operational capability for the core architecture. Begin hiring dedicated Red Team experts. +* **Deliverable:** Full-spectrum scoring reports and SOC 2 compliance readiness. + +### Phase 3: Scaling & Productization (10+ Months) +* **Focus:** Automated updates, vertical specialization, and service integration. +* **Product:** Offering customized, "Niche Scorecards" (e.g., "FAG Scorecard for Patent Law" or "FAG Scorecard for Drug Discovery"). +* **Goal:** Establish FAG as the mandatory pre-deployment vetting step for large enterprise AI adoption. + +--- + +## 5. Required Resources & Financial Ask + +To achieve the Phase 1 MVP within 3 months, we require the following initial investment: + +| Resource Category | Specific Need | Allocation Focus | Estimated Cost | +| :--- | :--- | :--- | :--- | +| **Personnel** | 1x Senior ML Engineer (Contract) | Building the orchestration and logging backbone. | \$XXX,000 | +| **Data/Compute** | Cloud compute budget increase (Azure/AWS) | Running large batches of diverse model inferences. | \$YYY,000 | +| **Personnel** | 1x Domain Expert Consultant (Contract) | Establishing the gold-standard, domain-specific ground truth datasets. | \$ZZZ,000 | +| **Total Initial Ask** | | | **[Total Financial Figure]** | + +*Note: The total cost reflects upfront development and execution of Phase 1, with revenue projections to follow based on secured pilot contracts.* + +--- + +## 6. Conclusion & Call to Action + +The quality of AI deployment will inevitably become a bottleneck before the compute power does. The solution is standardized, objective evaluation. + +**FAG is not a cost center; it is a *revenue enabler* that mitigates systemic enterprise risk.** By funding the FAG Benchmark Suite, the Committee will be funding the creation of the industry standard for AI due diligence. + +**Recommendation:** Approve the initial funding tranche to commence Phase 1 development immediately, enabling us to secure the first high-value pilot client within 45 days. \ No newline at end of file