Building Enterprise Superintelligence
Pavo builds systems-first intelligence for the enterprise—self-evolving knowledge graphs, agent societies, and world models that learn from experience to optimize business outcomes.
Our Mission
Civilization‑scale progress relies on enterprises. Our mission is to build Enterprise Superintelligence—systems that take responsibility for business outcomes and work alongside humans to deliver them.
Foundation models are necessary but not sufficient. The hard problem is systems intelligence: end‑to‑end architectures that understand a company's code, data, and decisions, and improve themselves through experience.
Human-only feed optimization
AI employee
Self-evolving systems + Cross-surface optimizer
System homogenization
Research at Pavo
We turn commodity models into compounding systems that understand, operate, and evolve the core machinery of the enterprise: its engagement systems, decision workflows, and growth engines.
Tribal Knowledge Systems
Self-evolving knowledge graphs that capture the knowledge fabric of a company—code, data, dashboards, experiments, and tasks—turning them into artifacts that update through experience.
Societies of Agents
Architectures for multi-agent "orgs in a box"—analysts, data scientists, ML engineers—that coordinate to move real metrics. We study judgement, delegation, and communication protocols at scale.
Enterprise World Models
High-fidelity simulation environments where agents practice, generate experience, and improve via reinforcement learning without touching production.
Backpropagation Through Knowledge
New representations and learning algorithms that let us "backprop" through knowledge systems, updating not just weights but the mental artifacts agents use to reason.
Judgement & Verification at Scale
Methods to evaluate complex, long-horizon behavior—from single decisions to full-system trajectories—closing the loop between economic outcomes and agent policy updates.
Experience at Scale
Research that ships. The architectures we study at Pavo Labs power real AI employees helping product & engineering teams move metrics at internet scale.
- 01400M+ user scale heritage Systems shipped across Spotify, ShareChat, Sourcegraph, and Uber-scale data platforms.
- 02Proof-of-life deployments AI teammates that autonomously run analyses, train models, and ship A/B-able changes.
- 03Value-accredited experiments e.g., an 8% uplift in M3 retention for a 100M+ user app within days.
Selected Work
Agentic Skill Escalation in Enterprise ML Systems
Framework for how agents progress from simple commands to autonomous innovation inside data/ML stacks via repeated action–feedback loops.
Why Systems Win Over Models
Argues that the real moat in enterprise AI is experiential systems—tribal knowledge, world models, and feedback loops—not raw model IQ.
Tribal Knowledge Systems in the Wild
Building a "GitHub for knowledge" from code, data, dashboards, and task histories; lessons from deploying across multiple enterprises.
Our Principles
Systems over Models
We design compounding systems—tribal knowledge, agents, and world models—and treat foundation models as interchangeable components.
Ship to Learn, Research to Win
Every deployment is an experiment. We balance fast shipping with deep research to make the system better on every iteration.
Experiential First
We believe real intelligence comes from agents learning through interaction with complex environments, not static benchmarks.
Knowledge as a First-Class Artifact
Tribal knowledge is not documentation; it's the living substrate humans and agents share. We design systems where knowledge can be inspected, updated, and back-propagated.
Antifragility by Design
Every failure—bad model, broken pipeline, or wrong judgment—becomes training data that makes the system stronger.
Join Pavo Labs
We're assembling a small, senior team of researchers and engineers obsessed with systems-first intelligence: RL, agents, knowledge representation, large-scale data/ML systems, and enterprise product surfaces.
If you've shipped at scale and want to work on the next layer of the stack, we'd love to talk.