Cogniscient: Adaptive Intelligence Substrate

Grant-Aligned Funding Roadmap

Document Purpose: Strategic roadmap mapping Cogniscient's capabilities to grant funding opportunities. Cogniscient is positioned as proprietary infrastructure powering OOI's open use cases—the semantic memory layer that gives AI systems the ability to remember, relate, reason, and evolve.

Date: January 27, 2026
Status: Strategic Planning Document

Executive Summary

Cogniscient is the adaptive intelligence substrate—the component that enables persistent, contextual, evolving memory for AI systems. Unlike databases that store facts or search engines that match keywords, Cogniscient stores experiences, understands relationships, and enables contextual intelligence.

Strategic Position: Cogniscient is fully proprietary infrastructure (VeritexAI IP) that powers OOI's open use cases. Grant funding targets the deployment of Cogniscient-enabled solutions, not Cogniscient development itself.

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

Part I: Cogniscient's Fundable Value Propositions

Angle 1: Institutional Memory for Mission-Driven Organizations

The Pitch:

"Nonprofits lose 40% of institutional knowledge with each staff transition. Cogniscient preserves organizational context—relationships, decisions, patterns, history—so new staff can hit the ground running and organizations don't keep re-learning the same lessons."

Key Cogniscient Features:

• Experience-based storage (gist, not verbatim)
• Entity resolution across systems and time
• Relationship graph (who knows whom, what connects to what)
• Temporal awareness (what happened when, how things evolved)

Strongest Funder Fit:

• Patrick J. McGovern Foundation
• Siegel Family Endowment
• Humanity AI Coalition
• AWS Imagine Grant

Funding Potential: $600K–$1.5M

Grant Messaging: "Cogniscient powers [USE CASE]'s ability to preserve and leverage institutional knowledge, ensuring mission-driven organizations don't lose critical context when staff change."

Angle 2: Outcome-Linked Knowledge Infrastructure

The Pitch:

"Most organizational knowledge is disconnected from outcomes. Cogniscient links every piece of information to the outcomes it serves—so organizations can see which knowledge actually matters, which relationships drive results, and which patterns predict success."

Key Cogniscient Features:

• Integration with ODIE (outcomes as first-class entities)
• Evidence linking (beliefs traced to supporting knowledge)
• Pattern recognition across experiences
• Outcome-weighted retrieval (prioritize knowledge that matters)

Strongest Funder Fit:

• Patrick J. McGovern Foundation
• Siegel Family Endowment
• Google.org Accelerator

Funding Potential: $500K–$2M

Grant Messaging: "Cogniscient enables [USE CASE] to connect organizational activities to measurable outcomes, transforming scattered data into actionable intelligence."

Angle 3: Federated Context Assembly

The Pitch:

"Information lives in silos—email, CRM, documents, project tools. Cogniscient creates a unified semantic layer that understands how information across systems relates, without centralizing sensitive data."

Key Cogniscient Features:

• Hybrid graph + vector architecture
• Cross-source synthesis (connect dots across systems)
• Permission-aware retrieval (respects source access controls)
• Context assembly for specific tasks

Strongest Funder Fit:

• AWS Imagine Grant
• Salesforce Accelerator
• GitLab Foundation

Funding Potential: $400K–$800K

Grant Messaging: "Cogniscient powers [USE CASE]'s ability to assemble relevant context from fragmented systems, giving staff the information they need without requiring data centralization."

Angle 4: Novel Cognitive Architecture (Research Angle)

The Pitch:

"Cogniscient implements experience-based memory that mirrors human cognition—storing the 'gist' rather than verbatim content, understanding relationships rather than just data, and enabling contextual recall rather than keyword matching. This has implications for how we build AI systems that truly learn and remember."

Key Cogniscient Features:

• Gist-based encoding (10x more efficient than verbatim)
• Relationship inference (discovering implicit connections)
• Memory consolidation (similar experiences → patterns)
• Importance weighting and decay (not all information is equal)

Strongest Funder Fit:

• NSF CISE Core Programs (with academic partner)
• NSF Convergence Accelerator (with academic partner)
• Schmidt Sciences AI2050 (nomination-based)

Funding Potential: $750K–$2M (requires academic partnership)

Grant Messaging: "Our research formalizes experience-based memory architectures for AI systems, drawing on cognitive science principles of gist-based encoding and relationship-aware recall."

Part II: Use Case Alignment

Cogniscient is infrastructure—it enables use cases. Here's how it maps:

COMPASS (Nonprofit Outcome Measurement)

Cogniscient Role: Store and relate program activities, beneficiaries, interventions, and outcomes across time.

Grant Targets: McGovern ($200K–$500K), Google.org ($500K–$2M), AWS ($300K)

NEXUS (Nonprofit Executive Support)

Cogniscient Role: Institutional memory for the ED—relationships, decisions, organizational context.

Grant Targets: McGovern ($200K–$500K), AWS ($300K), Humanity AI (from $500M)

BRIDGE (Grantmaking Effectiveness)

Cogniscient Role: Portfolio knowledge graph—grantees, interventions, outcomes, patterns across years.

Grant Targets: McGovern ($200K–$500K), Siegel ($200K–$500K)

SCHOLAR (Student Outcome Navigation)

Cogniscient Role: Knowledge graph of courses, credentials, employers, and career trajectories.

Grant Targets: Siegel ($200K–$500K), GitLab ($250K), Google.org ($500K–$2M)

STEWARD (Climate Adaptation Tracking)

Cogniscient Role: Unified view across fragmented municipal systems—transportation, utilities, permitting, budget.

Grant Targets: McGovern ($200K–$500K), NSF CISE ($600K–$1.2M with academic)

Part III: Grant Landscape

Tier 1: High Fit, No Academic Partner Required

Tier 1 Potential: $1.55M–$4.5M

Tier 2: Requires Academic Partnership

Tier 2 Additional Potential: $1.6M–$6.7M (with academic partnerships)

Tier 3: Longer-Term / Specialized

Part IV: IP Strategy

The Model A Approach

Cogniscient follows the "C-Corp Owns IP, Nonprofit Deploys" model:

VeritexAI (C-Corp)
  │
  │ Owns Cogniscient IP
  │ Develops core technology
  │
  └──────────────────────────────────────┐
                                         │
                                         ▼
                              OOI (501c3) uses Cogniscient
                              to power open use cases:
                              COMPASS, NEXUS, BRIDGE, etc.
                                         │
                                         │ Grant funds go to OOI
                                         │ for deployment, not
                                         │ Cogniscient development
                                         ▼
                              Use case code is open
                              Cogniscient remains proprietary

Grant Messaging by Funder Type

What's Fundable vs. What's Not

Part V: Development Phases & Grant Alignment

Phase 1: Foundation (Q1–Q2 2026)

Cogniscient Capabilities: Basic entity CRUD, core relationships, experience storage, simple semantic search

Phase 1 Positioning (McGovern - NEXUS):

"NEXUS provides nonprofit executive directors with an AI chief-of-staff that remembers organizational context—board relationships, funder history, strategic decisions—so EDs can focus on judgment rather than information assembly. The underlying knowledge infrastructure preserves institutional memory that would otherwise be lost to staff transitions."

Phase 2: Intelligence Layer (Q2–Q3 2026)

Cogniscient Capabilities: Cross-source synthesis, ODIE integration, pattern recognition, context assembly

Phase 2 Positioning (Google.org - COMPASS):

"COMPASS transforms how nonprofits measure outcomes—not through retrospective reporting, but through continuous intelligence. By building a knowledge graph that connects every program activity to defined outcomes, COMPASS surfaces which interventions actually work, which beneficiary characteristics predict success, and where resources should be allocated. The semantic infrastructure learns from accumulated experience, getting smarter as organizations use it."

Phase 3: Advanced Capabilities (Q3–Q4 2026)

Cogniscient Capabilities: Relationship inference, contradiction detection, memory consolidation, temporal reasoning

Phase 3 Positioning (NSF - Research):

"We propose to formalize and evaluate experience-based memory architectures for AI systems. Drawing on cognitive science research on gist-based encoding, we will develop: (1) formal models of semantic compression that preserve decision-relevant information, (2) relationship inference algorithms that discover implicit connections, and (3) evaluation frameworks for contextual retrieval quality. Our approach has practical applications in knowledge management while contributing to fundamental understanding of AI memory systems."

Part VI: 12-Month Grant Calendar

January 2026 (NOW)

February 2026

March 2026

April–June 2026

Q3–Q4 2026

Part VII: Funding Scenarios

Conservative (No Academic Partners, 18 months)

Realistic Range: $750K–$1.5M

Moderate (Strategic Positioning, 24 months)

Add to conservative:

Cumulative Realistic Range: $1.5M–$2.5M

Aggressive (Academic Partners Secured, 24 months)

Add to moderate:

Cumulative Realistic Range: $2M–$3.5M

Part VIII: Academic Partnership Strategy

The "Novel Cognitive Architecture" angle unlocks significant federal funding but requires academic collaboration.

Research Questions Worth Formalizing

Potential Academic Partners

Timeline for Academic Track

Part IX: Key Messaging Templates

For Traditional Foundations (McGovern, Siegel)

"[USE CASE] is powered by semantic knowledge infrastructure that preserves and connects organizational knowledge. Unlike traditional databases that store facts, this infrastructure stores experiences—understanding not just what happened, but who was involved, what it meant, and how it relates to organizational outcomes. This enables [SPECIFIC BENEFIT: e.g., "nonprofits to preserve institutional memory across staff transitions" or "foundations to see patterns across their entire grantmaking portfolio"]."

For Tech Corporate (AWS, Google, Salesforce)

"[USE CASE] leverages a hybrid graph-vector architecture to create a unified semantic layer across fragmented data sources. The platform assembles relevant context for specific tasks without requiring data centralization, respecting source system permissions while enabling cross-system intelligence. [SPECIFIC TECHNICAL BENEFIT: e.g., "Built on AWS services for scalability and security" or "Integrates with Salesforce to enrich CRM data with relational context"]."

For Federal (NSF, NIST)

"We propose research on experience-based memory architectures for AI systems. Our approach draws on cognitive science principles of gist-based encoding—storing semantic essence rather than verbatim content—combined with graph-based relationship modeling. Research outputs including formal models, evaluation frameworks, and benchmark datasets will be published openly. The proposed work has applications in knowledge management, organizational learning, and AI system design."

Summary

Cogniscient is fundable through the deployment of solutions it enables, not direct infrastructure development.

Strongest Near-Term Opportunities:

1. McGovern Foundation – Lead with NEXUS (institutional memory for nonprofit EDs)
2. Siegel Family Endowment – Lead with SCHOLAR (career trajectory knowledge graph)
3. AWS Imagine – Lead with federated context assembly angle

Medium-Term Opportunities:

• Google.org Accelerator – COMPASS with full outcome-linked knowledge
• Salesforce Accelerator – CRM integration story

Long-Term (with Academic Partners):

• NSF CISE – Novel cognitive architecture research
• NSF Convergence – Workforce/education knowledge infrastructure

Total 24-Month Potential: $2M–$3.5M across diversified sources

Document prepared January 27, 2026 Open Outcomes Institute / VeritexAI