Stackit.ai
Stackit.ai is a true product-creation story: Mass Ideation incubated the treasury strategy by hand, launched it as an application, served hundreds of customers throughout a market cycle, and learned from operating a real customer business. Stack has a blockchain-confirmable live date in February 2023 and is now applying those accumulated lessons as it expands from human treasury owners to AI agents. The operating experience behind Stack led directly to Mass Ideation's AI Growth Engine and AI Readiness program.
Why it mattered
Stack had to solve three connected problems across three market cycles: prove the treasury strategy under real market pressure, turn those lessons into an application that real customers could use, and expand a human-first product to AI agents. Hundreds of customers used the application throughout the cycle, creating extensive product, customer, risk, growth, and operating learning. Stack also had to grow in a blockchain and crypto category where conventional paid advertising was restricted or unavailable.
Starting condition
Before Stack was an application, Mass Ideation operated and tested the strategy manually. The work began with financial rules, protection logic, and real market exposure—not a software interface. The product was built for humans first, while AI agents had not yet arrived as a real customer market.
The challenge
Help people and AI agents accumulate, borrow against, and protect long-term crypto holdings; grow despite blockchain and crypto advertising restrictions; and diversify beyond human-only revenue during the bear market.
What Mass Ideation built
A production treasury platform for humans and AI agents with customer workflows, automated protection rules, MCP, REST, x402, sandbox tools, and unsigned transactions, plus the Growth Engine and AI Readiness practices developed from operating it.
A true product-creation story
Incubated. Launched. Learned. Now expanding to AI agents.
First market cycle
Incubated and tested by hand
Mass Ideation began testing the treasury strategy manually around the middle of the cycle, continued toward the market top, and kept testing as prices fell into the bear market. That decline put the rules and protection logic under real pressure and showed that the strategy worked.
Second market cycle
Launched, served hundreds, and learned
The first cycle's manual results became the Stack application. Stack has been live since February 2023, a date that is blockchain-confirmable through publicly verifiable on-chain fee activity. Hundreds of customers used the application throughout the cycle, producing extensive learning about the product, customers, risk, growth, and day-to-day operations.
Third market cycle
Applying the learning to AI agents
In the third cycle's bear market, Stack is applying what it learned from incubation and hundreds of customers while expanding from a human-first product to one built for humans and AI agents. Agents are already using Stack, with policies, permissions, limits, and human control around high-impact financial actions.
What Stack proves for business
A live case study in AI business growth and agent readiness.
Stack connects product development, customer acquisition, operating learning, and AI-agent adoption. These are the same connected challenges businesses face when they want AI to help attract customers, improve operations, and prepare for new machine-driven buying journeys.
AI Growth Engine
Stack shows why sustainable AI business growth needs more than isolated automations. Awareness, lead capture, consultative sales, customer experience, product learning, and optimization work better as one connected growth system.
Explore the AI Growth Engine →AI Readiness
Preparing Stack for AI agents required clear public facts, trustworthy documentation, machine-readable interfaces, scoped permissions, and human control. Those operating lessons now help other businesses prepare for the AI Agent Economy.
Prepare your business for AI agents →AI Product Incubation
Stack was tested manually before its rules became software. That approach reduces guesswork when a company needs to research, prototype, and build a custom AI product around a real business problem.
See AI strategy and R&D →From human customers to humans + agents
AI Readiness grew out of a real product's accumulated learning.
Stack did not begin with an AI-agent feature list. It began with a manually operated strategy, became an application, and served hundreds of customers throughout a market cycle. That created deep learning about customer questions, trust, onboarding, product behavior, risk, growth constraints, and operating controls.
During the third market cycle's bear market, Stack began applying those accumulated lessons to a second customer class: AI agents. That meant adapting the website, knowledge, documentation, interfaces, permissions, payments, and operating controls so an agent could discover, understand, trust, and safely use the product.
Stack continues to work with humans, and AI agents are already using the platform. The strategy is not humans or agents. It is one business prepared to serve both, with policies and human control around high-impact financial actions.
Hundreds of customers used Stack throughout the application cycle
Human customers remain an active part of the business
AI agents are already using Stack
MCP, REST, x402, sandbox tools, and unsigned transactions support agent workflows
AI Readiness translates Stack's operating lessons into a program other businesses can use