SUSAN: Redefining Human–AI Collaboration through Agentics
Transformational Thesis: SUSAN is a next-generation AI Agentics platform built to convert passive automation into active, self-directed intelligence. It elevates AI from a reactive interface to a proactive decision-making layer.
Human-in-the-Loop Design: Blends multimodal reasoning (text, voice, data) with continuous learning loops, enabling agents that observe, reason, and act alongside human teams.
Strategic Positioning: Acts as the cognitive operating system linking data, tools, and people—forming the backbone of what Bain defines as the Agentic Economy.
Investment Narrative: Positions SUSAN as an early mover in the infrastructure layer for enterprise-grade autonomy—an emerging category expected to underpin the next decade of digital productivity growth.
The Shift from Chatbots to Cognitive Agents
Market Evolution: AI 1.0 delivered chatbots; AI 2.0 introduces cognitive agents that understand context, manage memory, and execute tasks independently.
Productivity Delta: Cognitive agents reduce decision latency and unlock cross-functional efficiency—addressing the current plateau in automation ROI.
Scale Opportunity: Global AI-software spend is projected to exceed $900 B by 2030; the fastest-growing segment is agentic systems orchestrating reasoning across APIs and enterprise data.
SUSAN’s Edge: Built from inception as a reasoning framework, not a prompt wrapper—enabling deep contextual awareness, multimodal interaction, and continuous learning.
Investor Implication: Represents the natural progression from tools to infrastructure—where every enterprise will require an Agentics layer to remain competitive.
The Opportunity: AI Agentics as the Next $ Trillion Frontier
Macro Trend: Just as SaaS redefined enterprise software, Agentics will redefine digital operations—shifting value from applications to orchestration.
Economic Impact: Analysts forecast >$ 1 T in productivity gains by 2032 as agentic systems compress decision cycles and scale cognition across industries.
Vertical Focus: Education, recruitment, and knowledge work represent early beachheads for SUSAN—sectors characterized by high cognitive load and process fragmentation.
Scalable Moat: Every deployed agent becomes a data asset—enhancing Susan’s contextual graph and creating a self-reinforcing learning network.
BlackRock Relevance: The emergent “middleware of intelligence” layer is under-institutionalized; SUSAN offers an early-stage entry into the AI infrastructure that will power future returns.
Problem Statement: Fragmented AI, Shallow Automation, and Lost Productivity
Enterprise Reality: Despite record AI investment, most systems remain siloed and reactive—LLMs without memory, chatbots without reasoning, automation without context.
Operational Cost: Fragmentation creates redundant data flows, inconsistent experiences, and knowledge loss—eroding both efficiency and trust in AI initiatives.
Strategic Gap: The issue is not technology capability but integration—organizations lack a unified framework to turn disparate AI tools into coordinated intelligence.
SUSAN’s Solution: A modular Agentic core that fuses reasoning (RAG + LangGraph), retrieval, and voice interaction to create continuous cognitive systems.
Investor Takeaway: SUSAN solves the last mile of AI productivity—transforming isolated models into an enterprise-wide intelligence network with compounding ROI.
What Is Susan: An AI Agentics Operating System
Core Definition: Susan is an AI Agentics Operating System—a unifying intelligence layer that connects data, humans, and tools through autonomous cognitive agents.
Value Proposition: Converts fragmented workflows into self-orchestrating systems capable of contextual reasoning, adaptive learning, and continuous optimization.
Strategic Role: Functions as a “control plane” for organizational intelligence—governing how models, APIs, and users interact.
Commercial Differentiator: While most firms sell models, Susan sells capability: the infrastructure that enables scalable, compliant, and brand-aligned autonomy.
Investor Relevance: Represents a horizontal platform play—foundational, recurring, and defensible across verticals.
Core Architecture: MCP + RAG + Voice Intelligence Stack
Modular Cognitive Pipeline (MCP): Separates reasoning, retrieval, and action into self-contained modules—enabling elastic scaling and low-latency orchestration.
RAG Engine: Proprietary retrieval-augmented generation pipeline using ChromaDB + LangGraph to deliver grounded, verifiable outputs across enterprise knowledge bases.
Voice Layer: Real-time STT / TTS integration through VAPI, Google TTS, and AssemblyAI, creating multimodal human–agent interaction.
Interoperability: Built on FastAPI + Node.js microservices—cloud-agnostic and containerized for AWS, Azure, or on-prem.
Strategic Outcome: Enables sub-second reasoning at enterprise scale—balancing cognitive depth with operational performance.
Key Components: Chat, Voice, Knowledge, Analytics, Human Loop
Chat & Voice Interfaces: Intuitive multimodal UX supporting text, voice, and mixed-mode dialogue—bridging human intent and agent action.
Knowledge Fabric: Integrates structured + unstructured data (docs, URLs, databases) via embeddings to create persistent, contextual memory.
Analytics Core: Provides behavioral telemetry, token-usage economics, and ROI dashboards for organizational insight.
Human-in-Loop Governance: Ensures transparent oversight and reinforcement learning from human feedback (RLHF).
Investor Angle: Delivers full-stack defensibility—from front-end experience to back-end intelligence—positioning Susan as a system-of-record for cognition.
The Agent Framework: Autonomous, Context-Aware, Multi-Modal Agents
Autonomous Agents: Task-oriented micro-intelligences capable of executing workflows, querying data, and escalating to humans when uncertainty exceeds threshold.
Contextual Persistence: Each agent maintains situational awareness—memory across sessions, users, and organizational context.
Multi-Modal Capability: Processes voice, text, images, and data streams—allowing domain-specific adaptation (e.g., education, recruitment, analytics).
Governed Autonomy: Agents operate within compliance, privacy, and brand guidelines; policy is codified at orchestration level.
Strategic Implication: Positions Susan as the “operating system for cognitive labor”—a scalable way to replicate human expertise in digital form.
Differentiation: How Susan Moves Beyond LLM Wrappers
Architectural Depth: Unlike typical GPT-wrappers, Susan embeds reasoning + memory + action orchestration—creating persistent intelligence, not transient chat.
Data Ownership & Control: Enterprise-grade governance ensuring that client data remains segregated, encrypted, and auditable.
Pedagogical and Operational IP: Unique reinforcement loops optimized for applied domains (learning outcomes, candidate evaluation, workflow efficiency).
Moat Mechanics: Every interaction strengthens Susan’s domain models—creating compounding differentiation through proprietary context graphs.
Investor Message: Susan is not another AI interface—it is the infrastructure layer where durable enterprise value and margins will accrue.
Target Verticals: Education, Recruitment, and Enterprise AI Assistants
Strategic Focus: Susan targets three scalable, high-margin verticals where cognitive automation delivers measurable ROI—Education, Recruitment, and Enterprise Productivity.
Education: Powers adaptive tutoring, curriculum alignment, and voice-first learning—reducing instructor workload by up to 60%.
Recruitment & HR: Enables end-to-end candidate evaluation, AI-led interviews, and automated job-matching, cutting hiring cycles by >40%.
Enterprise AI Assistants: Embeds within corporate workflows to provide reasoning-based task support, document intelligence, and decision analytics.
Portfolio Strategy: Multi-vertical entry builds optionality while maintaining a shared platform backbone—ensuring efficient capital deployment.
Investor Insight: Each vertical represents a multi-billion TAM; Susan’s agentic architecture allows horizontal expansion with minimal incremental cost.
Revenue Model: SaaS + Usage-Based + White-Label Licensing
Core Model: Hybrid SaaS + consumption-based structure—driving predictable ARR and upside from enterprise usage scaling.
SaaS Tiers: Subscription pricing for institutions and enterprises based on seat count, number of active agents, and API calls.
Usage-Based Add-On: Voice minutes, LLM token consumption, and data retrieval billed per unit—mirroring cloud economics.
White-Label Licensing: Enables large partners to deploy Susan under their brand—creating high-margin B2B revenue without customer acquisition cost.
Investor Lens: Blends recurring revenue stability with scalable usage growth—a structure aligned with infrastructure-class valuation multiples.
Partnership Flywheel: Integrations with AWS, OpenAI, Google, and EdTechs
Ecosystem Strategy: Susan integrates directly with global AI and cloud ecosystems—expanding reach while reducing development overhead.
Platform Alliances: Deep technical integrations with AWS (EC2, RDS), OpenAI, and Google Cloud for compute, models, and data services.
Education Partnerships: Collaborations with institutions (Lincoln School, Talent Bridge) validating product–market fit across learning environments.
Network Effect: Each integration strengthens platform defensibility—embedding Susan within existing digital infrastructure.
Investor Takeaway: The partnership flywheel compounds growth through interoperability and brand trust—key signals for institutional scalability.
Scale Strategy: Multi-Tenant, Multi-Brand Expansion (MARIA, Glowie, etc.)
Multi-Tenant Core: Single codebase serving multiple organizations securely—allowing Susan to scale across clients with near-zero marginal cost.
Brand Extensions: MARIA (AI Tutor), Glowie (Youth Engagement), and Susan ERP (Enterprise Agentics) demonstrate vertical-specific brand strategy built on the same core platform.
Operational Efficiency: Shared infrastructure minimizes duplication in model training, orchestration, and compliance management.
Market Diversification: Reduces dependency on any one vertical while maximizing platform utilization and revenue density.
Investor Relevance: This “platform federation” model mirrors early-stage Salesforce—diversified growth with unified IP control and predictable margins.
Tech Stack Overview: Real-Time Voice and Agentic Infrastructure
System Design: Susan’s architecture delivers real-time cognition and human-level responsiveness across voice, text, and multimodal interfaces.
Voice Intelligence Pipeline: WebRTC powers live bi-directional audio; VAD (Voice Activity Detection) and Whisper / Assembly AI optimize streaming, transcription accuracy, and energy efficiency.
Speech Output Excellence: Dual-engine TTS (Google TTS + OpenAI TTS) supported by a large multilingual voice pool with 70 + languages ensures natural, localized, and inclusive engagement.
Reasoning Layer: Multi-LLM orchestration (GPT, Gemini, Claude, LLaMA, Groq) provides model resilience, reasoning diversity, and vendor independence.
Global Performance: End-to-end latency maintained below 500 ms — enabling synchronous, life-like interaction at enterprise scale.
Investor Insight: A purpose-built, voice-native Agentics platform engineered for speed, stability, and scale — differentiating Susan as infrastructure-class AI, not an application.
Defensibility: Performance Moat and Data Governance
Latency Leadership: Real-time performance (< 500 ms) forms a perceptible human-interaction moat — the key barrier to replication in agentic AI.
Model Redundancy: Multi-LLM mesh ensures continuity, cost optimization, and domain-specific reasoning — eliminating single-vendor dependency.
Data Governance and Sovereignty: All sessions secured through Cloudflare Zero-Trust infrastructure; voice streams isolated via TURN servers and end-to-end encryption.
Contextual Memory: Proprietary context-persistence system enables agents to learn from interactions and build institutional knowledge over time.
Investor Angle: Susan’s defensibility compounds through technical differentiation (speed + accuracy) and trust in data handling — a dual moat aligned with institutional adoption criteria.
Compliance & Trust: Enterprise-Grade Security and Regulatory Alignment
Regulatory Readiness: Fully aligned with GDPR, SOC 2 Type II, and MiCA — positioning Susan for deployment across highly regulated markets.
Security Infrastructure: Cloudflare TURN servers and Zero-Trust architecture secure global traffic, prevent DDoS, and maintain data residency compliance.
Privacy by Design: No retention of voice logs; anonymized session IDs; per-tenant encryption create transparent, auditable data flows.
AI Governance: Agent decisions and outputs are traceable, meeting emerging EU AI Act standards for responsible autonomy.
Investor Takeaway: Built for trust at scale — Susan combines institutional-grade security with consumer-grade experience, making it one of the few AI platforms ready for enterprise and public sector integration.
Adoption & Pilots: Lincoln School, Talent Bridge, City of Riihimäki
Early Adopters: Susan is already live across education, recruitment, and civic-AI pilots—validating use cases at enterprise and institutional scale.
Education Impact: Lincoln School Oy (Finland) uses Susan as a voice-based AI tutor; pilot results show +52 % engagement and –40 % teacher workload.
Recruitment Efficiency: Talent Bridge Oy integrates Susan’s agentic interviews, cutting candidate-screening time by 43 % and improving placement match quality.
Public Sector Validation: City of Riihimäki pilot focuses on GDPR-compliant citizen chatbots; highlights Susan’s readiness for regulated environments.
Investor Signal: Early traction demonstrates commercial credibility, policy alignment, and readiness for institutional scale-up.
Metrics: Engagement, Cost Efficiency, Learning Outcomes, Token Use
Operational Efficiency: Average cost per cognitive task reduced >65 % vs. traditional human-only workflows, with <1 s average agent response time.
Engagement Metrics: Voice-enabled sessions yield 2.3× longer interactions and 1.8× higher completion rates than text-only systems.
Learning Outcomes: In education pilots, adaptive agents improved concept retention by 34 % and assessment accuracy by 29 %.
Token Economics: Median token utilization per task down 42 % through proprietary RAG + context-reuse architecture—material margin improvement.
Investor Lens: Demonstrates both unit-economic optimization and user-behavior proof—critical for scaling toward sustainable ARR.
Testimonials & Case Studies: Transforming Learning and Hiring Pipelines
Educator Feedback: “Susan allows our teachers to focus on pedagogy while the AI handles personalization.” — Head of Digital Learning, Lincoln School Oy.
Recruiter Insight: “Our team closed roles 40 % faster with higher satisfaction scores—Susan became a team member, not a tool.” — Director, Talent Bridge.
Civic Partner View: “For the first time, we can deliver compliant digital services that feel human.” — Chief Data Officer, City of Riihimäki.
Quantitative Proof: 90 % of pilot users reported improved experience; 87 % would recommend Susan’s agentic assistants for institutional deployment.
Investor Takeaway: Strong customer advocacy and measurable ROI validate both market need and commercial defensibility.
Capital Ask: Strategic Partnership with BlackRock — Scaling Agentics Globally
Strategic Objective: Seek a structured investment and partnership with BlackRock to scale Susan as the foundational infrastructure for the global Agentic Economy.
Capital Requirement: USD 25–40 M Series A to accelerate enterprise productization, compliance certifications, and go-to-market expansion across the EU and North America.
Co-Investment Potential: Aligned with institutional LP mandates for AI infrastructure, education, and workforce transformation—offering both impact and alpha.
Governance Alignment: Bain retains operational leadership; BlackRock provides strategic oversight and ESG guidance—ensuring disciplined capital deployment.
Investor Thesis: Early exposure to a platform shaping the intelligence layer of enterprise software—a once-in-a-decade category-defining opportunity.
Use of Funds: Productization, Go-to-Market, and Compliance Scaling
Product Development (40 %): Expand the Agentics SDK, enterprise dashboards, and multi-agent orchestration engine to strengthen platform scalability.
Go-to-Market Acceleration (30 %): Target education, HR tech, and municipal digital services—leveraging partnerships with AWS, Google, and regional integrators.
Compliance & Infrastructure (20 %): Achieve SOC 2 Type II and ISO 27001 certification; expand cloud regions for GDPR and MiCA-aligned data residency.
Working Capital & Talent (10 %): Build cross-functional AI research and business-development teams to support rapid customer onboarding.
Investor Lens: Balanced deployment focused on revenue activation and risk insulation—designed to achieve operational breakeven within 24 months.
Five-Year Vision: From AI Agents to Cognitive Economies
Phase 1 (2025–2026): Institutionalize Susan as the default Agentics OS across education and recruitment, establishing recurring ARR in the EU market.
Phase 2 (2027–2028): Expand into enterprise verticals—finance, healthcare, and logistics—deploying domain-specific agent packs and analytics modules.
Phase 3 (2029 onward): Evolve toward Cognitive Economies—ecosystems where agents transact knowledge, time, and compute value autonomously.
Strategic Outcome: Position Susan as the infrastructure layer for human–AI symbiosis—a global standard for digital cognition.
Investor Relevance: Captures exponential compounding from recurring revenue, network data effects, and sovereign-AI compliance positioning.
The Call to Action: Investing in the Intelligence Infrastructure of the Future
Macro Imperative: The next decade’s winners will own the infrastructure of intelligence—where data, cognition, and human capital intersect.
Why Now: AI adoption has crossed the commercialization threshold; the market is seeking trusted, compliant, and scalable platforms to institutionalize it.
Why Susan: Proven architecture, regulatory readiness, and demonstrated vertical traction make Susan a rare investable asset in a crowded LLM market.
Strategic Fit: BlackRock’s capital, network, and ESG alignment perfectly complement Bain’s operational stewardship and Susan’s global ambition.
Closing Message: Together, we can build the foundation of the Agentic Era—where intelligence itself becomes a managed, measurable, and monetizable asset class.