Investors

Backing the agentic layer for adaptive, empathetic AI.

Investment Thesis

Traditional AI stacks optimize predictions; The Hunger Engine optimizes behavior. Our agents model human-like motivation loops (hunger–satiation, joy/trauma memory, valence), enabling systems that adapt in real time across cloud, automation, and data workflows. We believe this shift—from static models to agentic ecosystems—will define the next decade of AI infrastructure.

The Problem

  • Enterprises stitch together ML, rules, and RPA that break under real-world change.
  • Decisioning is brittle; context, emotion, and memory are ignored.
  • APIs and data pipelines lack adaptive orchestration across domains.

Our Opportunity

  • Unified agentic layer that learns from outcomes, not just inputs.
  • Empathetic algorithms to weight risk, reward, and user experience.
  • Drop-in “Agentic Toolbox” for cloud, monitoring, and analytics platforms.

Product Snapshot

Agentic Core

Cascading hungers, memory/valence, multi-objective policies; orchestrates tools, APIs, and data flows for autonomous decisions and actions.

Empathy Layer

Emotion-aware scoring (joy ↔ disgust), safety bounds, and preference learning for human-centered outcomes and safer autonomy.

Cloud-Native APIs

Agentic Toolbox for AWS/Azure/GCP; rapid API design, agile data mapping, monitoring hooks, and analytics adapters.

Go-to-Market

Beachheads

  • Automation monitoring & alerting (agentic escalation, self-healing playbooks).
  • Cloud SaaS integrations (Agentic Toolbox embedded into existing platforms).
  • Adaptive analytics (closed-loop insights → recommendations → actions).

Business Model

  • SaaS: tiered agent seats, usage-based orchestration.
  • Enterprise: annual license + premium support.
  • OEM/Platform: revenue-share for embedded Agentic Toolbox.

Roadmap

12–18 Month Milestones

  1. MVP v1: Agent orchestration, memory/valence, API adapters.
  2. Design-partner pilots (automation monitoring, cloud SaaS, analytics).
  3. Agentic Toolbox SDK for AWS/Azure/GCP marketplaces.
  4. Compliance & observability: guardrails, audit logs, policy packs.
  5. v2: Multi-agent collaboration, reward shaping, human-feedback tools.
  6. Scale GTM: OEM partnerships, solution integrators, co-sell motions.

Key Advantages

  • Agentic layer that’s domain-agnostic and tool-friendly.
  • Empathy-aware decisioning aligned with outcomes and safety.
  • API-first design for fast partner integrations and OEM embeds.

The Raise

What Investors Get

  • Early position in agentic infrastructure with OEM potential.
  • Access to product roadmap and pilot insights.
  • Co-design opportunities on vertical solutions.
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FAQ & Risks

How is this different from typical LLM apps?

LLMs generate text; our agents generate decisions and actions with memory, valence, and guardrails. The engine orchestrates tools, data, and policies to deliver outcomes—not just outputs.

Top Risks & Mitigations

  • Integration complexity: API-first SDKs and “Agentic Toolbox” reduce lift.
  • Safety & governance: policy packs, audit logs, human-in-the-loop controls.
  • Competition: focus on agentic behavior + empathy layer + OEM strategy.

Interested in partnering as an investor?

We’re speaking with a limited number of investors to align capital with mission and velocity.

Contact the Founder

This page is for informational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any securities. Any offering will be made only to qualified investors and in compliance with applicable laws.