PRISM: Procurement & RFP Intelligence for Solution Matching

Grant-Aligned Development Roadmap

Document Purpose: Strategic roadmap for PRISM as a new ARCHER-powered use case, mapping development milestones to grant funding opportunities.

Date: January 27, 2026
Status: Strategic Planning Document

Executive Summary

PRISM addresses a critical gap in the AI marketplace: small businesses and government agencies struggle to find AI solutions that actually match their needs. While the AI vendor landscape has exploded to 1,000+ tools, buyers lack the expertise to evaluate capabilities, and vendors lack standardized ways to communicate what their solutions actually do.

PRISM creates an outcome-driven matching platform powered by the ARCHER stack—translating business needs into measurable outcomes, maintaining a structured AI Capability Knowledge Graph, and connecting buyers to solutions based on what AI can actually deliver, not just what vendors claim.

Strategic Position: PRISM is positioned as open infrastructure for AI discovery—democratizing access to AI evaluation capabilities that currently only large enterprises possess.

Primary Funding Angle: Economic opportunity through AI access for small businesses

Secondary Angle: Government procurement modernization

Funding Potential: $1.5M–$3.5M over 24 months across diversified sources

Part I: The Problem

For Small Businesses

For Government Agencies

For AI Vendors

The Root Cause

There is no standardized taxonomy for AI capabilities.

Vendors describe solutions in technical terms (NLP, computer vision, transformers). Buyers describe needs in business terms (faster customer response, reduced errors, automated reporting). No translation layer exists to connect them based on outcomes actually achieved.

Part II: The PRISM Solution

Core Value Proposition

"PRISM translates what you need into what AI can actually do—matching businesses to AI solutions based on proven outcomes, not marketing claims."

How ARCHER Powers PRISM

┌─────────────────────────────────────────────────────────────────────────┐
│                              PRISM                                       │
│         Procurement & RFP Intelligence for Solution Matching             │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐     │
│  │      ODIE       │    │   COGNISCIENT   │    │     ARCHER      │     │
│  │                 │    │                 │    │  ORCHESTRATOR   │     │
│  │ • Translates    │    │ • AI Capability │    │                 │     │
│  │   business needs│    │   Knowledge     │    │ • Drives match  │     │
│  │   to outcomes   │    │   Graph         │    │   workflow      │     │
│  │ • Extracts RFP  │    │ • Vendor        │    │ • Clarification │     │
│  │   requirements  │    │   profiles      │    │   dialogs       │     │
│  │ • Defines       │    │ • Use case      │    │ • Evaluation    │     │
│  │   success       │    │   evidence      │    │   coordination  │     │
│  │   criteria      │    │ • Outcome       │    │ • Recommendation│     │
│  │                 │    │   tracking      │    │   delivery      │     │
│  └────────┬────────┘    └────────┬────────┘    └────────┬────────┘     │
│           │                      │                      │               │
│           └──────────────────────┼──────────────────────┘               │
│                                  │                                      │
│                                  ▼                                      │
│                    ┌─────────────────────────┐                          │
│                    │        FLUXIO           │                          │
│                    │                         │                          │
│                    │ • Vendor outreach       │                          │
│                    │ • Comparison reports    │                          │
│                    │ • RFP optimization      │                          │
│                    │ • Match notifications   │                          │
│                    └─────────────────────────┘                          │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Component Responsibilities

Part III: The AI Capability Taxonomy

The Core Differentiator

PRISM's primary IP is the AI Capability Taxonomy—a structured framework for describing what AI solutions can actually do, independent of vendor marketing language.

Taxonomy Structure

AI CAPABILITY TAXONOMY v1.0
│
├── CAPABILITY LAYER (What can it DO?)
│   │
│   ├── GENERATE
│   │   ├── Text (long-form, short-form, structured, creative)
│   │   ├── Images (realistic, artistic, product, editing)
│   │   ├── Code (languages, frameworks, complexity)
│   │   ├── Audio (speech, music, effects)
│   │   ├── Video (generation, editing, enhancement)
│   │   └── Data (synthetic, augmented)
│   │
│   ├── ANALYZE
│   │   ├── Sentiment (text, voice, multi-modal)
│   │   ├── Patterns (time series, behavioral, anomaly)
│   │   ├── Content (topics, entities, relationships)
│   │   ├── Performance (metrics, trends, forecasts)
│   │   └── Risk (fraud, compliance, security)
│   │
│   ├── CLASSIFY
│   │   ├── Documents (type, intent, priority)
│   │   ├── Images (objects, scenes, defects)
│   │   ├── Audio (speaker, emotion, events)
│   │   ├── Entities (people, organizations, products)
│   │   └── Intent (customer, search, action)
│   │
│   ├── EXTRACT
│   │   ├── Structured data (forms, tables, invoices)
│   │   ├── Entities (names, dates, amounts)
│   │   ├── Relationships (connections, hierarchies)
│   │   ├── Key information (summaries, highlights)
│   │   └── Metadata (attributes, tags)
│   │
│   ├── PREDICT
│   │   ├── Outcomes (conversion, churn, success)
│   │   ├── Demand (sales, inventory, capacity)
│   │   ├── Risk (default, fraud, failure)
│   │   ├── Timing (maintenance, events, trends)
│   │   └── Behavior (next action, preference)
│   │
│   ├── RECOMMEND
│   │   ├── Products (e-commerce, content, services)
│   │   ├── Actions (next best action, optimization)
│   │   ├── Content (personalization, curation)
│   │   ├── Decisions (routing, allocation, prioritization)
│   │   └── Matches (search, similarity, compatibility)
│   │
│   └── AUTOMATE
│       ├── Workflows (multi-step, conditional, triggered)
│       ├── Decisions (rule-based, ML-driven, hybrid)
│       ├── Responses (customer service, notifications)
│       ├── Processes (data entry, reconciliation, reporting)
│       └── Orchestration (multi-system, API chains)
│
├── REQUIREMENT LAYER (What does it NEED?)
│   │
│   ├── DATA REQUIREMENTS
│   │   ├── Input types (text, images, structured, real-time)
│   │   ├── Volume thresholds (minimum, optimal, maximum)
│   │   ├── Quality requirements (labeled, clean, complete)
│   │   └── Format specifications (file types, APIs, schemas)
│   │
│   ├── INTEGRATION REQUIREMENTS
│   │   ├── Deployment model (cloud, on-prem, hybrid, edge)
│   │   ├── API availability (REST, GraphQL, SDK, webhook)
│   │   ├── Authentication (OAuth, API key, SSO, SAML)
│   │   └── Data residency (regions, compliance zones)
│   │
│   ├── OVERSIGHT REQUIREMENTS
│   │   ├── Human-in-the-loop (required, optional, none)
│   │   ├── Approval workflows (thresholds, escalation)
│   │   ├── Audit trails (logging, explainability)
│   │   └── Override capabilities (manual intervention)
│   │
│   └── COMPLIANCE REQUIREMENTS
│       ├── Security certifications (SOC 2, ISO 27001, FedRAMP)
│       ├── Privacy frameworks (GDPR, CCPA, HIPAA)
│       ├── Industry standards (PCI-DSS, HITRUST)
│       └── Accessibility (WCAG, Section 508)
│
├── OUTPUT LAYER (What does it PRODUCE?)
│   │
│   ├── OUTPUT CHARACTERISTICS
│   │   ├── Format (text, JSON, file, API response)
│   │   ├── Latency (real-time, near-real-time, batch)
│   │   ├── Volume (throughput, rate limits)
│   │   └── Quality (accuracy, consistency, reliability)
│   │
│   ├── EXPLAINABILITY
│   │   ├── Confidence scores (availability, calibration)
│   │   ├── Reasoning traces (chain of thought, citations)
│   │   ├── Feature importance (for ML models)
│   │   └── Uncertainty quantification
│   │
│   └── AUDITABILITY
│       ├── Decision logs (what, when, why)
│       ├── Version tracking (model, data, config)
│       ├── Reproducibility (deterministic, seeded)
│       └── Compliance reporting (pre-built, customizable)
│
└── EVIDENCE LAYER (What has it ACHIEVED?)
    │
    ├── USE CASE EVIDENCE
    │   ├── Industry vertical (verified deployments)
    │   ├── Company size (SMB, mid-market, enterprise)
    │   ├── Use case type (customer service, operations, etc.)
    │   └── Implementation complexity (time, resources)
    │
    ├── OUTCOME EVIDENCE
    │   ├── Quantified results (%, $, time savings)
    │   ├── Before/after comparisons
    │   ├── Third-party validation (case studies, references)
    │   └── Failure cases (transparency on limitations)
    │
    └── SCALE EVIDENCE
        ├── Customer count (by segment)
        ├── Transaction volume (processed)
        ├── Uptime/reliability (SLA performance)
        └── Growth trajectory (maturity indicators)

Taxonomy as Moat

The AI Capability Taxonomy becomes PRISM's core IP and competitive moat:

1. Vendor-agnostic: Works across all AI solutions, not tied to any platform
2. Outcome-anchored: Connected to ODIE's outcome framework
3. Evidence-based: Requires verification, not just vendor claims
4. Continuously updated: Learns from match success/failure
5. Open for adoption: Taxonomy itself can be open; implementation is proprietary

Part IV: User Flows

Flow 1: Small Business Discovery

"I need AI for..."

User: "We're a 50-person manufacturing company. Our customer service 
       team spends too much time answering the same questions about 
       order status and product specs."

PRISM (via ODIE):
┌─────────────────────────────────────────────────────────────────┐
│ I understand you want to reduce repetitive customer inquiries.  │
│ Let me translate this into specific outcomes:                   │
│                                                                 │
│ PRIMARY OUTCOME:                                                │
│ "Reduce customer service response time for routine inquiries    │
│  from [current avg] to [target] while maintaining satisfaction" │
│                                                                 │
│ CLARIFYING QUESTIONS:                                           │
│ • What's your current average response time?                    │
│ • What % of inquiries are "routine" (order status, specs)?      │
│ • Do you use a CRM or helpdesk system?                          │
│ • What's your monthly inquiry volume?                           │
│ • Any compliance requirements (data residency, etc.)?           │
└─────────────────────────────────────────────────────────────────┘

User provides context...

PRISM (via Cogniscient + Archer):
┌─────────────────────────────────────────────────────────────────┐
│ Based on your needs, I found 12 solutions with verified         │
│ outcomes in similar contexts. Here are the top 3 matches:       │
│                                                                 │
│ 1. [Solution A] - 94% match                                     │
│    ✓ Deployed at 15+ manufacturers under 100 employees          │
│    ✓ Achieved 73% deflection rate for order status queries      │
│    ✓ Integrates with your CRM (Salesforce)                      │
│    ✓ $299/mo for your volume                                    │
│    ⚠ Requires 2-week setup; no custom product spec training     │
│                                                                 │
│ 2. [Solution B] - 89% match                                     │
│    ✓ Specializes in product spec Q&A                            │
│    ✓ Can train on your product documentation                    │
│    ✓ Pay-per-resolution pricing ($0.15/query)                   │
│    ⚠ No native CRM integration (Zapier required)                │
│                                                                 │
│ 3. [Solution C] - 82% match                                     │
│    ✓ Full customer service automation suite                     │
│    ✓ Includes human handoff workflows                           │
│    ⚠ Higher price point ($899/mo)                               │
│    ⚠ Better fit at 100+ employees                               │
│                                                                 │
│ [Compare All] [See Details] [Request Demos]                     │
└─────────────────────────────────────────────────────────────────┘

Flow 2: Government RFP Optimization

"We're writing an RFP for..."

Agency uploads draft RFP for "AI-powered document processing system"

PRISM (via ODIE):
┌─────────────────────────────────────────────────────────────────┐
│ RFP ANALYSIS COMPLETE                                           │
│                                                                 │
│ EXTRACTED REQUIREMENTS:                                         │
│ • Process 10,000+ documents/month                               │
│ • Extract data from forms (structured)                          │
│ • Classify document types                                       │
│ • FedRAMP Moderate required                                     │
│ • Integration with existing DMS                                 │
│                                                                 │
│ IDENTIFIED GAPS:                                                │
│ ⚠ No accuracy threshold specified                               │
│   Recommendation: "Minimum 95% extraction accuracy on          │
│   standard forms, validated against sample set"                 │
│                                                                 │
│ ⚠ Ambiguous "AI-powered" language                               │
│   Recommendation: Specify capability requirements using         │
│   structured taxonomy (Extract > Structured Data > Forms)       │
│                                                                 │
│ ⚠ No human oversight requirements                               │
│   Recommendation: Define exception handling and escalation      │
│                                                                 │
│ MARKET ANALYSIS:                                                │
│ • 23 vendors meet FedRAMP + document processing requirements    │
│ • 8 have verified deployments at similar scale                  │
│ • Estimated competitive responses: 12-18                        │
│                                                                 │
│ [Apply Recommendations] [View Matching Vendors] [Export Report] │
└─────────────────────────────────────────────────────────────────┘

Flow 3: AI Vendor Opportunity Matching

"Where do we fit?"

Vendor registers capabilities via structured profile:

PRISM Vendor Profile:
┌─────────────────────────────────────────────────────────────────┐
│ CAPABILITY REGISTRATION                                         │
│                                                                 │
│ Primary Capabilities:                                           │
│ ☑ EXTRACT > Structured Data > Invoices, Forms, Tables          │
│ ☑ CLASSIFY > Documents > Type, Priority                        │
│ ☑ AUTOMATE > Workflows > Approval routing                      │
│                                                                 │
│ Requirements:                                                   │
│ • Deployment: Cloud (AWS, Azure, GCP), On-prem available       │
│ • Compliance: SOC 2 Type II, FedRAMP Moderate (in process)     │
│ • Integration: REST API, Zapier, native Salesforce             │
│                                                                 │
│ Evidence:                                                       │
│ • 150+ customers (45 in financial services)                    │
│ • Case study: Regional bank, 89% extraction accuracy           │
│ • Case study: Insurance company, 60% processing time reduction │
│                                                                 │
│ [Save Profile] [View Matching Opportunities]                    │
└─────────────────────────────────────────────────────────────────┘

Vendor receives match alerts:

┌─────────────────────────────────────────────────────────────────┐
│ NEW OPPORTUNITY MATCH - 91% fit                                 │
│                                                                 │
│ Mid-size accounting firm seeking document processing            │
│ • 5,000 invoices/month                                         │
│ • QuickBooks integration required                               │
│ • Budget: $500-1,000/month                                      │
│                                                                 │
│ Your advantages:                                                │
│ ✓ Invoice extraction is your specialty                         │
│ ✓ You have accounting industry case studies                    │
│ ✓ Price point within their budget                              │
│                                                                 │
│ Gaps to address:                                                │
│ ⚠ No native QuickBooks integration (Zapier available)          │
│                                                                 │
│ [Express Interest] [Pass] [See Similar]                         │
└─────────────────────────────────────────────────────────────────┘

Part V: Revenue Model

Sustainability Framework

For grant positioning, PRISM demonstrates clear path to sustainability:

Grant Messaging

"PRISM's free tier ensures small businesses can access AI discovery tools regardless of budget. Revenue from vendor subscriptions and enterprise contracts sustains the platform while expanding access."

Part VI: Grant Landscape

Tier 1: High Fit, No Academic Partner Required

Tier 1 Potential: $1.15M–$4.95M

Tier 2: Corporate Programs (Non-Dilutive)

Tier 2 Potential: $700K in non-cash resources

Tier 3: Government/Economic Development

Tier 3 Potential: Uncertain pending federal programs

Tier 4: Research Partnerships (Longer-term)

Tier 4 Potential: $750K–$2M (with academic partnerships)

Part VII: Funder-Specific Positioning

GitLab Foundation (Primary Target)

Their Focus: "AI solutions that unlock siloed data, expand agent interoperability, reduce service delivery costs, personalize learning, validate skills, and strengthen labor market intelligence."

PRISM Pitch:

"PRISM creates the AI Capability Taxonomy—an open standard for describing what AI solutions can actually do. This directly addresses agent interoperability by providing a shared language for AI capabilities that any system can implement.

For small businesses, PRISM democratizes access to AI evaluation—giving a 50-person manufacturer the same ability to find and assess AI solutions that Fortune 500 companies have through dedicated procurement teams.

The platform is outcome-driven: instead of matching keywords, PRISM connects business needs ('reduce customer response time') to solutions with verified evidence of achieving similar outcomes in similar contexts.

The AI Capability Taxonomy will be published as open infrastructure. Revenue comes from vendor subscriptions and enterprise contracts, ensuring sustainability while keeping core discovery free for small businesses."

Key Alignment Points:

• ✓ Agent interoperability (open taxonomy)
• ✓ Economic opportunity (SMB access to AI)
• ✓ Open infrastructure model
• ✓ Clear sustainability path

Patrick J. McGovern Foundation (Secondary Target)

Their Focus: "AI for public purpose" and "civic infrastructure." Recent grants include open-source modeling tools and AI-enabled information tools.

PRISM Pitch:

"Small businesses represent 99.9% of U.S. companies but lack the resources to evaluate the exploding AI marketplace. PRISM provides the AI discovery infrastructure that levels the playing field—translating business needs into structured outcomes and matching them to solutions with proven results.

Built on ARCHER's outcome-driven AI architecture, PRISM ensures that every recommendation traces back to explicit, measurable outcomes. This isn't feature-matching; it's outcome-matching.

The AI Capability Taxonomy creates transparency in the AI marketplace—making vendor capabilities legible and comparable. This reduces failed implementations, wasted budgets, and AI skepticism that holds back adoption."

Key Alignment Points:

• ✓ AI for public purpose (democratized access)
• ✓ Transparency and accountability
• ✓ Open-source/open infrastructure elements
• ✓ Practical deployment focus

Humanity AI Coalition

Their Focus: "AI is not destiny, it is design." Five priority areas include "AI used to enhance how people work, rather than replace them."

PRISM Pitch:

"The AI marketplace is failing small businesses. Overwhelming choice, vendor hype, and language mismatches mean most AI investments don't deliver. PRISM ensures that when businesses adopt AI, they adopt the right AI—solutions matched to their actual needs with evidence of real outcomes.

This is AI that enhances procurement, not replaces human judgment. PRISM surfaces relevant options and explains why they match; humans make the final decision with better information.

By creating an open AI Capability Taxonomy, PRISM contributes to a healthier AI ecosystem where capabilities are transparent and outcomes are verifiable."

Key Alignment Points:

• ✓ AI enhancing (not replacing) work
• ✓ Human-centered design
• ✓ Ecosystem health
• ✓ Economic opportunity

Part VIII: Development Phases & Grant Alignment

Phase 1: Foundation (Q1–Q2 2026)

Focus: Core taxonomy, basic matching, SMB flow

Technical Milestones

• AI Capability Taxonomy v1.0 (core structure)
• ODIE integration for outcome translation
• Basic Cogniscient knowledge graph (100 vendors)
• SMB discovery flow (natural language → matches)
• Simple web interface
• Manual vendor profile intake

Grant Opportunities

Phase 1 Funding Target: $200K–$500K + $450K credits

Phase 2: Intelligence Layer (Q2–Q3 2026)

Focus: Vendor self-service, evidence layer, government flow

Technical Milestones

• Vendor self-registration portal
• Evidence verification system
• RFP analysis flow (document upload → gap analysis)
• Expanded knowledge graph (500+ vendors)
• Match quality feedback loop
• Basic API for integrations

Grant Opportunities

Phase 2 Funding Target: $500K–$2M

Phase 3: Scale & Ecosystem (Q4 2026+)

Focus: API ecosystem, government contracts, advanced matching

Technical Milestones

• Public API for taxonomy queries
• Advanced matching (ML-enhanced)
• Government procurement integrations
• Vendor analytics dashboard
• Partner channel (consultants, integrators)
• Knowledge graph at 2,000+ vendors

Grant Opportunities

Phase 3 Funding Target: $500K–$2M

Part IX: 12-Month Grant Calendar

January 2026 (NOW)

February 2026

March 2026

April–June 2026

Q3–Q4 2026

Part X: Funding Scenarios

Conservative (No Academic Partners, 18 months)

Realistic Range: $1M–$1.8M (including credits)

Moderate (Strategic Positioning, 24 months)

Add to conservative:

Cumulative Realistic Range: $1.5M–$3M

Aggressive (Academic Partners Secured, 24 months)

Add to moderate:

Cumulative Realistic Range: $2M–$3.5M

Part XI: IP Strategy

What's Open vs. Proprietary

Grant Messaging by Funder Type

Part XII: Competitive Landscape

Existing Players (Government Contracting Focus)

Existing Players (AI Discovery)

PRISM's Unique Position

"PRISM is the only platform that translates business outcomes to AI capabilities using a structured, verified taxonomy—serving buyers (not vendors) with evidence-based matching."

Part XIII: Risk Factors & Mitigations

Part XIV: Immediate Action Items

This Week (Jan 27–31, 2026)

February 2026

March 2026

Appendix A: Sample Taxonomy Entry

vendor:
  name: "Acme Document AI"
  id: "vendor-001"
  
capabilities:
  primary:
    - category: EXTRACT
      subcategory: Structured Data
      specifics: [Invoices, Forms, Tables]
      confidence: verified
      
    - category: CLASSIFY
      subcategory: Documents
      specifics: [Type, Priority]
      confidence: verified
      
  secondary:
    - category: AUTOMATE
      subcategory: Workflows
      specifics: [Approval routing]
      confidence: claimed

requirements:
  data:
    input_types: [PDF, PNG, JPEG, TIFF]
    volume_minimum: 100/month
    volume_optimal: 1000-10000/month
    
  integration:
    deployment: [Cloud-AWS, Cloud-Azure, On-prem]
    api: [REST, SDK-Python, SDK-Node]
    auth: [API-key, OAuth]
    
  compliance:
    certifications: [SOC2-Type2, HIPAA]
    in_progress: [FedRAMP-Moderate]
    data_residency: [US, EU]

evidence:
  customers:
    total: 150
    by_segment:
      SMB: 95
      MidMarket: 45
      Enterprise: 10
    by_vertical:
      FinancialServices: 45
      Healthcare: 30
      Manufacturing: 25
      
  outcomes:
    - context: "Regional bank, 500 employees"
      metric: "Extraction accuracy"
      result: "89%"
      verified: true
      
    - context: "Insurance company, 200 employees"
      metric: "Processing time reduction"
      result: "60%"
      verified: true
      
  scale:
    monthly_documents_processed: 2000000
    uptime_sla: 99.9%
    avg_latency_ms: 1200

pricing:
  model: usage
  tiers:
    - name: Starter
      price: "$99/mo"
      includes: "1,000 pages"
    - name: Professional
      price: "$299/mo"
      includes: "5,000 pages"
    - name: Enterprise
      price: "Custom"
      includes: "Unlimited + SLA"

Appendix B: ODIE Outcome Translation Example

User Input:

"We spend too much time answering customer questions about order status"

ODIE Translation:

outcome_definition:
  direction: Reduce
  measure: Time spent
  object: Answering customer order status inquiries
  context: Customer service team
  
clarifying_questions:
  - "What is your current average response time for order status inquiries?"
  - "What percentage of total inquiries are order-status related?"
  - "How many order status inquiries do you receive per month?"
  - "Do you have an existing helpdesk or CRM system?"
  
structured_outcome:
  primary: "Reduce customer service time spent on order status inquiries from {current} to {target}"
  
  success_criteria:
    - metric: "Response time for order status"
      baseline: null  # To be provided
      target: null    # To be provided
      measurement: "Average time from inquiry to resolution"
      
    - metric: "Deflection rate"
      baseline: "0%"  # Currently manual
      target: "50%+"  # Industry benchmark
      measurement: "% of inquiries resolved without human intervention"
      
    - metric: "Customer satisfaction"
      baseline: null  # To be measured
      target: "Maintain or improve"
      measurement: "CSAT score for order inquiries"

capability_requirements:
  must_have:
    - AUTOMATE > Responses > Customer service
    - Integration with order management system
    
  nice_to_have:
    - ANALYZE > Intent > Customer
    - Human handoff capability
    
  constraints:
    - Budget: To be specified
    - Integration: Must work with existing systems

Appendix C: Key Metrics for Grant Reporting

Document prepared January 27, 2026
PRISM Grant Funding Roadmap v1.0 Open Outcomes Institute / VeritexAI