Training data lock-in
Every hour a company spends training their Neuralgents agents makes those agents more valuable — and harder to replace. The switching cost compounds over time. You can't export institutional memory to a generic chatbot.
Investors
The $200B AI problem isn't the models. It's everything else.
Every organization is racing to deploy AI. Almost none of them can maintain it. Neuralgents is the infrastructure layer that makes AI employees real — persistent, trainable, autonomous, and fully owned by the organization that deploys them.
The problem
AI is powerful. Keeping it useful is the hard part.
The models are extraordinary. GPT-4, Claude, Gemini — they can reason, write, analyze, and synthesize at a level that would have seemed impossible five years ago. But deploying AI that stays useful for a specific organization requires something none of those models provide out of the box: memory, continuity, initiative, and ownership.
Most companies trying to deploy AI today are paying engineers six figures to maintain prompts. They're rebuilding context every time someone new joins the team. They're watching AI implementations rot when the person who set them up leaves. The gap isn't capability — it's infrastructure.
~73%
of enterprise AI projects
stall before reaching production — not because the models fail, but because the infrastructure around them does.
$400K+
avg. annual cost
for a mid-size company to staff and maintain an AI implementation with in-house engineering talent.
Zero
cross-session memory
in every major AI assistant today. Every new conversation starts from scratch. Context is gone. Knowledge is gone. Training is gone.
Market shift
The era of AI tools is ending. The era of AI employees is beginning.
AI tools respond to prompts. AI employees remember context, take initiative, and get better over time. The difference isn't the model — it's the wrapper, the memory, and the execution layer built around it.
This shift is happening now. Knowledge workers are asking for AI that acts like a colleague, not a search bar. Executives are asking for AI that reduces headcount pressure, not just writes copy faster. The market is ready for a new category — and that category doesn't have a dominant player yet.
AI tools
- Respond to prompts
- No memory between sessions
- Require constant supervision
- Knowledge owned by the vendor
- Generic — not trained on your organization
AI employees
- Take initiative and act autonomously
- Persistent memory across all sessions
- Learn and improve over time
- Knowledge owned by the organization
- Trained specifically on your processes
The solution
Neuralgents: the operating layer for AI employees
We've built the platform that makes AI employees real. Not AI assistants. Not chatbots with a better UI. Actual AI employees — trained through conversation, equipped with memory, given specific roles, and deployed to take autonomous initiative on the things that matter to your organization.
The training experience feels like onboarding a new hire. You talk to the agent. You walk it through your processes. You correct it. It remembers. That memory persists across every session, every team member, and every future task the agent handles.
The Pulse System gives agents a heartbeat: they don't wait to be asked. They monitor, execute, and flag what they can't handle alone — with a full audit trail of every action they take.
And because we're self-hosted, the organization owns everything. The agents. The memory. The training data. If they outgrow us, they take their workforce with them. That trust is rare in the current AI landscape. It's also a significant commercial advantage.
Competitive moat
Our advantages deepen with every deployment
Data sovereignty as a feature
Enterprises are done trusting SaaS vendors with their data. Self-hosting is not a compromise for us — it's our pitch. We give organizations AI power with complete data control. That's a moat that gets stronger as regulatory pressure increases.
The Pulse System
Proactive execution is the difference between AI you talk to and AI that works for you. The Pulse System is our proprietary framework for giving agents initiative — scheduled tasks, event-driven triggers, approval gates. It's not available anywhere else.
Role-based architecture
We built for organizational structure from day one. Roles, departments, access scopes, multi-agent collaboration. The category of "AI employee" requires infrastructure designed around how teams actually function — not around chat interfaces.
Market & timing
The window to define this category is open
LLMs reached a capability threshold in 2024 that makes persistent, trainable AI employees possible. The timing aligns with a wave of enterprise demand that existing tools cannot satisfy. The category of "AI employee infrastructure" doesn't have a dominant player. We're moving to become it.
Every company with knowledge workers is a potential customer. The serviceable market spans from SMBs looking to punch above their weight, to enterprises trying to redeploy headcount without losing institutional knowledge.
$200B+
Global AI market by 2030
Growing at ~40% CAGR
$47B+
Enterprise AI software segment
Our primary addressable market
Early
Stage of the AI employee category
No clear infrastructure winner yet
We're 18 months into a category shift that will be worth $50B in five years. The infrastructure layer of the AI workforce era is being built right now. We're building it.
Interested in learning more?
We're building something that matters — and we're looking for partners who see the same shift we do. Reach out to request a deck or schedule a conversation.