Why Self-Managing Context Is the Path to AGI

We thought businesses wanted better AI optimization. After 200+ customer conversations, we learned they wanted something completely different: AI systems they could actually ship to production.

Today we're announcing our $2M oversubscribed pre-seed round led by Precursor Ventures, with participation from Alumni Ventures, Founders Edge, Rogue Women VC, South Loop, Zeal Capital, and angels who've scaled AI companies from zero to hundreds of millions in revenue.

The breakthrough came from my co-founder Dr. Sean Robinson, whose work on spatial matching filters for NASA led to a novel approach to AI optimization that achieves 98% accuracy by dynamically managing context rather than just scaling models.

The Discovery That Changed Everything

A year ago, Sean developed a technique that hits 98% accuracy, well above the 60-70% industry standard. We assumed SaaS companies would want to optimize their existing AI.

We were wrong.

After hundreds of customer conversations, we discovered they hadn't even started. They needed complete, accurate AI systems built from scratch—and more importantly, AI that could maintain accuracy in production without constant engineering intervention. (Read the full story of how we discovered this)

That led us to build Empromptu as an AI application platform. But as customers deployed these systems, a deeper pattern emerged: they all needed AI that could manage and improve itself in production environments.

Every project hit the same constraints:

• •Context windows that collapsed under real business data volumes
• •Applications that couldn't maintain state across meaningful exchanges
• •Static prompts unable to adapt to evolving usage patterns
• •No systematic way to detect accuracy drift before customer impact

We weren't just solving a technical challenge. We were building infrastructure to bridge the gap between today's scripted AI and tomorrow's self-managing systems.

What We Built: The Self-Managing Context Engine

Most AI today operates statically. You engineer a prompt, validate it, deploy it. When it fails, you manually intervene and redeploy. It's the software deployment model of the 1990s.

We built something different: AI that operates the way experienced engineers do. It recognizes failure patterns, learns from production usage, and improves iteratively without constant human intervention.

Here's what this looks like in practice:

A cybersecurity company integrated 300+ compliance documents into their AI support system. When a customer asks about GDPR requirements, the AI doesn't just cite one document. It synthesizes across their entire compliance library, company policies, and previous support tickets to give accurate, context-aware answers.

This works because of three technical components:

Infinite Memory: AI that operates with your complete business context (250+ documents, policy libraries, domain expertise) without degrading accuracy. Unlike RAG systems that struggle past 20-30 documents, ours maintains coherence at scale.

Adaptive Context Engine: The intelligence layer that knows which information matters for each query. It works like an expert consultant: pulling relevant details, making connections across domains, ignoring noise.

Custom Data Models: AI trained on your specific documentation and processes, not generic off-the-shelf responses.

Together, these create AI applications that understand business context at scale, learn from operational usage, and improve themselves in production.

Why Self-Managing Context Is a Step Toward AGI

Most AI companies are chasing AGI through bigger models. We think that's the wrong approach.

My co-founder Dr. Sean Robinson developed algorithms for NASA's Fermi Gamma-ray Space Telescope and spent 12 years doing machine learning research at Pacific Northwest National Laboratory. His insight: human intelligence isn't about processing power. It's about knowing what matters, when to focus, when to zoom out, and managing context intelligently at scale.

That's what led to his breakthrough optimization technique achieving 98% accuracy. And that's why we believe self-managing context is a foundational step toward AGI: not bigger models, but systems that manage their own understanding and improve autonomously.

What This Changes

Right now, adding AI to your product means one of three paths:

1. Hire an ML team (expensive, slow)
2. Use a generic AI wrapper (unreliable)
3. DIY with prompts (doesn't scale)

We're offering a fourth option: enterprise-grade AI infrastructure that learns from your data and improves itself in production. No ML team required.

Our customers are already using this to ship features that would have taken 6-12 months in weeks. AI that answers customer support queries, generates personalized content, analyzes user data, and automates internal workflows.

What We're Building Next

This funding lets us do three things:

1. Scale our Self-Managing Context Engine to handle enterprise workloads (we're already at 2,000+ businesses and growing fast)
2. Build deeper integrations with the tools SaaS companies already use
3. Expand our team to support enterprise deployments, not just demos

As Charles Hudson from Precursor put it: "The next generation of intelligence won't come from bigger models. It will come from systems that know when to narrow in and when to zoom out. That's how we move from static prompts to software that actually learns."

The Vision

The path to AGI isn't through incrementally larger models. It's through systems that exhibit the fundamental capabilities of intelligence: contextual understanding at scale, autonomous improvement, and awareness of their own limitations. That's what Self-Managing Context enables, and why it matters beyond just building better SaaS features.

I see infrastructure where a solo founder can integrate sophisticated AI features with the same capability as Fortune 500 engineering teams, where healthcare startups focus on patient outcomes instead of wrestling with AI reliability, and where breakthroughs in education, finance, or climate tech aren't constrained by AI that works in demos but fails with real users.

That's the future we're building: AI that actually understands instead of just generating, software that learns from experience instead of just executing commands, and production AI that manages itself instead of requiring specialized teams.

If you're a SaaS founder wondering how to ship AI features without hiring an ML team, email me at support@empromptu.xyz.

We're working with companies from 5-person startups to Series C companies with 50M+ users. The pattern is the same: they need AI that works in production, not demos. They need it shipped in weeks, not quarters.

That's what we built Empromptu to do.