Since early 2024, our work has centered on helping organizations build modern software, develop cutting-edge apps, and operationalize artificial intelligence. With 30+ client projects, across industries and team sizes, we kept noticing the same costly patterns:
- A) Companies were not launching products, services or software faster, despite 10x faster development of code.
- B) Companies weren't actually adopting AI as an organization. Instead, they had dozens, sometimes hundreds, of employees operating in complete isolation inside individual browser tabs.
Christian Merheim and I watched the same cycle play out week after week:
- The duplicate context: an engineer or product lead would spend twenty minutes carefully setting up prompt context, pasting product architecture diagrams, and explaining brand constraints to ChatGPT. Three desks away, another teammate would spend forty minutes typing out the exact same background context to Claude.
- Siloed breakthroughs: someone would unlock a brilliant prompt chain or generate an exceptional technical brief, but the resulting insight remained trapped inside their personal session history.
- Institutional amnesia: the moment an employee closed their tab, changed departments, or left the company, every shred of hard-earned prompt engineering, synthesized knowledge, and model interaction vanished with them.
- The IP leakage risk: to get relevant answers, employees inevitably fed proprietary roadmaps and internal datasets into consumer AI accounts with ambiguous retention boundaries.
Every single employee was running a private instance of an AI tool that forgot the company the second the session ended.
Companies were paying for team seats, but their organizational intelligence wasn't compounding. It was evaporating.
From frustration to architecture
We knew what had to change: enterprise AI needed to behave like collaborative software, not a collection of private disposable scratchpads.
Christian and I set out to build the platform we wished our clients had from day one. Later, when Michael Donofrio joined our founding team, we doubled down on turning this vision into an enterprise-ready reality.
We created p30.ai to fundamentally bridge the gap between frontier model capability and true organizational memory.
Individual isolated chat The p30.ai shared workspace
┌─────────────────────────┐ ┌───────────────────────────────────────────────┐
│ Employee A (ChatGPT) │ │ Shared workspaces & enterprise agents │
│ ├─ Retyping context │ │ ├─ Shared prompts & model comparison │
│ └─ Knowledge trapped │ │ ├─ Multi-model orchestration (Claude, GPT...)│
├─────────────────────────┤ ───► │ ├─ Centralized organizational memory │
│ Employee B (Claude) │ │ └─ Private cloud routing & zero IP leakage │
│ ├─ Redundant prompts │ │ │
│ └─ Leaves with insights │ │ [ Context compounds at company level ] │
└─────────────────────────┘ └───────────────────────────────────────────────┘
What changes when you own your AI layer
Instead of forcing your team to choose between a single vendor's walled garden or fragmented individual accounts, p30.ai brings the frontier models together under a single pane of glass.
1. Shared team workspaces
Prompt, tag, comment on, and fork high-performing model interactions. When one team member refines a strategic workflow or an intricate code review pattern, the entire organization benefits instantly.
2. Persistent organizational memory
Context lives at the project and company level, not inside ephemeral personal browser caches. New hires step directly into existing project knowledge graphs without needing to re-educate the model on day one.
3. Multi-model orchestration and stress testing
No single model excels at everything. In p30.ai, your team can seamlessly run tasks across Claude, GPT, Gemini, and Grok side by side: have one frontier model draft an architecture plan, and prompt another to red-team its security vulnerabilities.
4. Democratized agentic automations
Build, deploy, and share domain-specific agents without writing boilerplate code or managing complex backend infrastructure. Non-technical operators and engineers alike can deploy reliable workflows that adhere to company standards.
5. Absolute data sovereignty
Route sensitive workloads to dedicated, private cloud environments (such as AWS) with zero data retention, closed VPC perimeters, and immutable audit logs. You never have to worry about proprietary IP feeding external training runs.
The compounding advantage
The companies that win the next decade won't just be the ones using AI; they will be the ones whose institutional knowledge compounds through AI.
When your shared context stays within your organization, every interaction makes your team smarter, faster, and more aligned tomorrow than they were today.
What is the biggest operational barrier stopping your team from moving beyond isolated, single-user chat accounts?