79% of Enterprises Are Deploying AI Agents. Only One Thing Is Missing.
STEnterprises Are Learning What Works With AI—And It's Not What They Expected
A fascinating pattern is emerging from the data: the enterprises succeeding with AI aren't the ones with the most powerful models. They're the ones that figured out how to make organizational knowledge compound.
Recent research reveals why:
As enterprises move to agentic AI (79% already adopting, per PwC), agents are working in isolation. Your expert's agent solves a complex problem on Monday. Your new hire's agent faces the same problem on Tuesday and hallucinates an answer because there's no shared memory of Monday's validated solution.
The cost? MIT found 95% of AI pilots fail to increase productivity. Not because AI can't help—but because organizations are running the same experiments thousands of times in parallel with no way to share what actually works.
Here's what we're discovering with our enterprise partners:
The value isn't in individual AI conversations. It's in private shared memory networks where validated solutions propagate instantly across the organization.
When your senior engineer solves a thorny infrastructure issue, that validated pattern becomes immediately available to every other employee's AI agent. When your compliance officer identifies the correct interpretation of a regulation, that knowledge compounds across legal, finance, and operations.
This is organizational learning at machine speed.
What this means in practice:
Solutions don't leave when employees leave Junior employees get senior-level AI assistance from day one Expensive mistakes get solved once, not thousands of times in parallel Small models perform like flagship models (10x cost reduction) because they have organizational context On-prem deployment keeps sensitive data inside the firewall
Whether you're 50 people or 50,000, the principle is the same: each validated solution multiplies across your entire organization instead of staying locked in one person's chat history.
The architectural breakthrough:
Current AI memory is single-player—ChatGPT remembers your preferences, Claude remembers your context. This creates linear value.
Private shared memory networks create exponential value. When one person validates a solution, everyone in your organization benefits. When everyone contributes validated patterns, the entire organization gets smarter together.
This is Memco.
We're building what doesn't exist yet: multiplayer learning infrastructure designed specifically for enterprise organizations.
No one else is doing this. ChatGPT and Claude offer individual memory—your AI remembers your preferences. That's single-player. That's linear value.
We're building the first private shared memory network with outcome-based learning:
Private Subnets: Your organizational memory stays entirely on your infrastructure. Not mixed with public data. Not accessible to competitors. Not used to train foundation models.
Outcome-Based Learning: We only capture what actually worked. Failed attempts and hallucinations are filtered out automatically based on validation signals—did tests pass? Did the user move on? Did the solution actually solve the problem?
Model-Agnostic: Works with Claude, GPT, Gemini, open source models, on-prem deployments—whatever your security and compliance policies require.
Vendor Neutrality: CTOs told us explicitly: they're hedging across multiple model providers and won't accept vendor lock-in. Being "Switzerland" is the unlock.
Why this doesn't exist yet:
Foundation labs can't build it. Their business model requires vendor lock-in to expensive flagship models. Building infrastructure that makes all models better—including competitors—would cannibalize their core revenue.
Our entire business model is the opposite: make every model more valuable by giving it access to your organization's collective intelligence.
We're not a feature that could be copied. We're fundamentally different architecture with opposite incentives.
The data shows enterprises are getting smarter about AI adoption:
Not blindly deploying. Not accepting hallucinations. But also not walking away.
They're looking for architecture that turns AI from isolated experiments into organizational learning infrastructure.
Memory shouldn't be trapped in individual conversations or provider silos.
It should be shared within your organization, validated by outcomes, and working across whatever tools your teams choose.
That's the architecture gap we're filling.
