Groq
Software development
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
- Developer feedback loops cut from 15 mins to 30 secs
Weeks tracing legacy RTL to customize a single subsystem. ChipAgents automated the groundwork—8.5 weeks to six, months to weeks.
A leading supplier of RISC-V processor IP with over 20 billion chips shipped globally, building increasingly specialized processors for AI, edge computing, and domain-specific applications.
Modifying complex processor subsystems like the Memory Load-Store Unit required engineers to spend weeks tracing legacy RTL and validating...
“The semiconductor industry is entering an era where customization is becoming a competitive advantage, but customization traditionally comes at the cost of engineering time. As companies build more specialized processors for AI, edge computing, and domain-specific applications, engineering teams are being asked to deliver more innovation without extending development schedules. ChipAgents is fundamentally changing that equation by helping engineers move faster through some of the most time-intensive parts of the design and verification process.”
RISC-V processor IP provider for system-on-chip designs.
Provided an Agentic AI platform to accelerate custom processor design and verification workflows.
Andes Technology's Processor development is part of this use case:
Related implementations across industries and use cases
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Talent shortages held back delivery. Engineers now use AI to review code 90% faster and cut 24-hour tasks to one hour.
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Weeks tracing legacy RTL to customize a single subsystem. ChipAgents automated the groundwork—8.5 weeks to six, months to weeks.
A leading supplier of RISC-V processor IP with over 20 billion chips shipped globally, building increasingly specialized processors for AI, edge computing, and domain-specific applications.
Modifying complex processor subsystems like the Memory Load-Store Unit required engineers to spend weeks tracing legacy RTL and validating...
“The semiconductor industry is entering an era where customization is becoming a competitive advantage, but customization traditionally comes at the cost of engineering time. As companies build more specialized processors for AI, edge computing, and domain-specific applications, engineering teams are being asked to deliver more innovation without extending development schedules. ChipAgents is fundamentally changing that equation by helping engineers move faster through some of the most time-intensive parts of the design and verification process.”
RISC-V processor IP provider for system-on-chip designs.
Provided an Agentic AI platform to accelerate custom processor design and verification workflows.
Andes Technology's Processor development is part of this use case:
Related implementations across industries and use cases
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Talent shortages held back delivery. Engineers now use AI to review code 90% faster and cut 24-hour tasks to one hour.
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.