Pre Stilla AI
Pre Stilla AI is positioning as a pre seed horizontal AI infrastructure play, building foundational capabilities around agentic architectures.
As agentic architectures emerge as the dominant build pattern, Pre Stilla AI is positioned to benefit from enterprise demand for autonomous workflow solutions. The timing aligns with broader market readiness for AI systems that can execute multi-step tasks without human intervention.
Stilla is an AI agent that quietly keeps track of everything — what was said, what needs doing, and what’s already in motion.
Stilla's unique advantage is its ability to maintain and orchestrate shared context across an organization's tools, enabling seamless collaboration and automated action from meetings to execution—without bots joining calls.
Agentic Architectures
Stilla is described as an autonomous agent that coordinates actions between humans and other software tools, acting as a teammate that performs multi-step reasoning and orchestration across meetings and integrations.
Full workflow automation across legal, finance, and operations. Creates new category of "AI employees" that handle complex multi-step tasks.
Knowledge Graphs
Stilla appears to maintain a permission-aware, shared context graph across tools and meetings, supporting RBAC and entity relationships for collaborative intelligence.
Emerging pattern with potential to unlock new application categories.
Vertical Data Moats
While Stilla emphasizes privacy and does not train on user data, the platform's focus on team collaboration and integration with enterprise tools suggests the potential for vertical data moats, especially if proprietary workflows or metadata are used for model improvement.
Unlocks AI applications in regulated industries where generic models fail. Creates acquisition targets for incumbents.
Agentic Architectures
Stilla is described as an autonomous agent that coordinates actions between humans and other software tools, acting as a teammate that performs multi-step reasoning and orchestration across meetings and integrations.
Full workflow automation across legal, finance, and operations. Creates new category of "AI employees" that handle complex multi-step tasks.
Pre Stilla AI operates in a competitive landscape that includes Otter.ai, Fireflies.ai, Supernormal.
Differentiation: Stilla emphasizes real-time collaborative notes, deep integrations with productivity tools (Linear, GitHub, Notion, Slack), and operates without joining meetings as a bot. Stilla also positions itself as an 'AI teammate' that orchestrates actions post-meeting, not just note-taking.
Differentiation: Stilla does not require a bot to join calls, offers live collaborative editing, and focuses on syncing context and decisions across multiple tools automatically. It also claims stronger privacy (no training on user data) and SOC2 Type 2 compliance.
Differentiation: Stilla's differentiation is in its 'multiplayer' approach—shared live docs, team collaboration during meetings, and persistent context across tools and threads. It also highlights more advanced post-meeting automation (e.g., updating Linear, drafting PRs, updating Notion, etc.).
Device-level meeting capture: Stilla listens on your device rather than joining calls as a bot. This avoids the common 'ghost participant' problem, improves privacy, and likely requires deep OS-level integration for audio capture and context awareness across all video platforms.
Real-time, collaborative AI notes: Notes are editable live by the team, with Stilla refining them in real time. This suggests a sophisticated backend for multi-user document synchronization and AI-driven summarization, blending collaborative editing (like Google Docs) with AI augmentation.
Persistent, cross-tool context: Stilla claims to 'remember what you decided across meetings and tools' and updates issues, drafts code, and keeps docs in sync. This implies a context engine that unifies data and decisions from disparate sources (Slack, Linear, GitHub, Notion, etc.), which is technically challenging due to data heterogeneity and the need for accurate, persistent memory.
No data training on user content: Explicitly stating that neither Stilla nor subprocessors train on user data is a privacy-forward stance, requiring a technical architecture that isolates user data from model training pipelines—potentially limiting some AI capabilities but increasing trust.
Automated action orchestration: Beyond note-taking, Stilla automates downstream actions (e.g., drafting PRs from conversations, syncing Linear issues, updating Notion, launching Cursor). This is more than summarization—it's workflow automation driven by semantic understanding of meetings, which is a step beyond most AI meeting tools.
There is no evidence of proprietary LLMs or unique AI infrastructure; the tech stack is unknown and may rely on third-party APIs, suggesting a potential thin wrapper over existing AI services.
The platform's described value—maintaining shared context and orchestrating actions between humans and AI agents—could be implemented as a feature within larger platforms (e.g., Slack, Notion, Microsoft Teams) rather than a standalone product.
There is no clear data advantage, proprietary technology, or unique technical differentiation visible. The stated 'vertical data moats' are not substantiated with examples or evidence.
If Pre Stilla AI achieves its technical roadmap, it could become foundational infrastructure for the next generation of AI applications. Success here would accelerate the timeline for downstream companies to build reliable, production-grade AI products. Failure or pivot would signal continued fragmentation in the AI tooling landscape.
Source Evidence(12 quotes)
"The multiplayer AI for teams"
"Stilla takes meeting notes. And does the work."
"Collaborative AI notes"
"Chat with Stilla in a private sidebar"
"Stilla refines as you go"
"Real time shared transcript"