Workflow · Production
Ramp acquires Jolt team to scale internal AI platform and developer productivity
The problem
Ramp's incremental AI investments — prompt engineering, caching, and autonomous agents — paid off individually, but the company now needs to reimagine foundational systems to support reliable AI at scale, requiring new architectures, guardrails, and eval frameworks.
Workflow diagram · grounded in source
1
Prompt engineering reduces errors
ai_action
“prompt engineering to reduce error rates”
2
Request caching and batching
integration
“caching and batching requests for cost and latency”
3
Autonomous agents for engineering tasks
ai_action
“deploying autonomous agents that take on engineering tasks”
4
Codebase understanding and code generation
ai_action
“They built technology that can understand giant codebases and generate context-relevant code”
Reported outcome
The Jolt team will improve Ramp's core AI platform, supercharge the internal developer experience, and help engineers build products with AI at higher velocity.
Reported metrics
Error ratereduce error rates
Request cost and latencycost and latency
Frequently asked questions
What did this team achieve with this AI workflow?
The Jolt team will improve Ramp's core AI platform, supercharge the internal developer experience, and help engineers build products with AI at higher velocity.
What results were reported?
Error rate: reduce error rates; Request cost and latency: cost and latency (source-reported, not independently verified).
How is this workflow AI workflow structured?
Prompt engineering reduces errors → Request caching and batching → Autonomous agents for engineering tasks → Codebase understanding and code generation.