SenseLab Corp
SenseLab builds the cognitive layer for AI agent fleets, providing memory infrastructure that connects experiences with outcomes to enable continuous learning.
At a Glance
AI Tools by SenseLab Corp
(1)
SenseLab (AMFS)
Agent Memory File System OSS
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Products & Services
Memory infrastructure for AI agents designed to build shared knowledge across agents, models, and teams.
A platform that connects agent experience with real-world outcomes, allowing knowledge to compound based on what actually works.
Market Position
Positions as the 'cognition layer' rather than just a memory database. Unlike vector databases or simple memory modules, SenseLab focuses on validating knowledge through production outcomes and maintains neutrality across all models and frameworks.
Leadership
Founders
Bruno Andrade
Serial entrepreneur. Founder and CEO of HTBase (acquired by Juniper Networks) and Shipa (acquired by Mirantis). Previously Senior Director of Software Engineering at Juniper Networks and worked in the WebSphere group at IBM.
Daiani (Dai) Macedo
Serial entrepreneur and product leader. Previously Product Lead at Shipa, Product Director at HTBase, and Product Manager for Lens at Mirantis.
Executive Team
Bruno Andrade
CEO & Founder
Former CEO of Shipa and HTBase; Senior Director at Juniper Networks.
Daiani Macedo
Co-founder & Product Lead
Former Product leader at Shipa and HTBase; Product Manager at Mirantis.
Founding Story
The founders, having built and sold two companies together (HTBase and Shipa), realized that agent systems lack a way to validate knowledge. They started SenseLab to build infrastructure where knowledge compounds based on real-world success, moving beyond simple retrieval.
Business Model
Revenue Model
SaaS subscription with usage-based tiers (ops per month).
Pricing Tiers
1,000 ops / month, 1 seat, 1 API key, email support.
25,000 ops / month, 3 seats, 10 API keys, business hour support.
Target Markets
- Multi-agent fleet coordination
- Continual learning for AI systems
- Knowledge sharing across different AI models