Complex protocols blocked user adoption. AI agents now execute wallet setup and swaps from natural language, driving 330k signups.
A Canadian startup developing virtual environments where users build and explore 3D applications on blockchain networks.
First-time users struggled with technical barriers like wallet setup and navigating decentralized protocols. Existing AI platforms lacked the...
“We wanted to embed all these capabilities into our AI agents so that new users could cut through any complexity and begin to experience the value of a Web3 internet. While Web3 technologies promise to revolutionize digital ownership and decentralized applications, their complexity creates significant barriers to mainstream adoption.”
Matrix Labs's Web3 task automation is part of this use case:
Metaverse and blockchain development studio for multichain 3D applications.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Mapping workflow logic once took weeks. Now, users build autonomous Claude agents in minutes to reason through multi-step tasks.
Standard inference stalled at 1k tokens/sec. A custom engine hit 10k/sec, cutting 20-second refactors to under 400ms.
Model tuning took weeks, slowing market entry. On Bedrock, updates take days, cutting costs 50% and boosting retention 75% for telecom.
Complex tool calls broke earlier models. Claude now navigates nested data, enabling agents to drive 3-5x productivity gains for clients.
Detailed prompts exceeded model limits, forcing risky cuts. Agents now handle full context to synthesize secure, one-click plans.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Complex protocols blocked user adoption. AI agents now execute wallet setup and swaps from natural language, driving 330k signups.
A Canadian startup developing virtual environments where users build and explore 3D applications on blockchain networks.
First-time users struggled with technical barriers like wallet setup and navigating decentralized protocols. Existing AI platforms lacked the...
“We wanted to embed all these capabilities into our AI agents so that new users could cut through any complexity and begin to experience the value of a Web3 internet. While Web3 technologies promise to revolutionize digital ownership and decentralized applications, their complexity creates significant barriers to mainstream adoption.”
Matrix Labs's Web3 task automation is part of this use case:
Metaverse and blockchain development studio for multichain 3D applications.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Mapping workflow logic once took weeks. Now, users build autonomous Claude agents in minutes to reason through multi-step tasks.
Standard inference stalled at 1k tokens/sec. A custom engine hit 10k/sec, cutting 20-second refactors to under 400ms.
Model tuning took weeks, slowing market entry. On Bedrock, updates take days, cutting costs 50% and boosting retention 75% for telecom.
Complex tool calls broke earlier models. Claude now navigates nested data, enabling agents to drive 3-5x productivity gains for clients.
Detailed prompts exceeded model limits, forcing risky cuts. Agents now handle full context to synthesize secure, one-click plans.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.