The AI runtime
for pro developers who care about craftsmanship.
The only AI meta-harness built on a C++ runtime. Not a Python workflow wrapper with a cloud orchestration UI bolted on.
RocketRide AIDLC: develop, stage, test, release, maintain. Build and harness your AI at rocket speed.
Sound familiar?
#1 AI Tech Debt
AI code bloat and tech debt are compounding faster than feature velocity.
One declarative pipeline file instead of a sprawl of framework glue code. Less to maintain, less to break, less to hand off, less attack surface.
One declarative pipeline file instead of a sprawl of framework glue code. Less to maintain, less to break, less to hand off, less attack surface.
#2 Employee Turnover
Your lead developer left. They took their custom AI stack with them.
RocketRide pipelines are portable JSON artifacts, not tribal knowledge. Any engineer on the team can open, read, modify, and deploy them. The work survives the person.
RocketRide pipelines are portable JSON artifacts, not tribal knowledge. Any engineer on the team can open, read, modify, and deploy them. The work survives the person.
#3 Observability & Traceability
You're running 30 AI agents across multiple teams. Nobody can tell you which ones are working, which ones are failing, or what any of them cost.
Every agent, every run; traced end to end: tokens, latency, cost; per node, per user, per team. Full process isolation means one failure doesn't cascade across the fleet. Observability console shows what's running, what's broken, and what it costs, before anyone has to ask.
Every agent, every run; traced end to end: tokens, latency, cost; per node, per user, per team. Full process isolation means one failure doesn't cascade across the fleet. Observability console shows what's running, what's broken, and what it costs, before anyone has to ask.
#4 Cost Control & Attribution
Shadow AI is running up your bill. Finance wants answers you don't have.
Spend per pipeline, per team, per run. Visibility into token spend within seconds to avert surprise end-of-month bills. Governed and reportable, with guardrails against non-work usage.
Spend per pipeline, per team, per run. Visibility into token spend within seconds to avert surprise end-of-month bills. Governed and reportable, with guardrails against non-work usage.
#5 Scaling in Production
It worked on your laptop. It melted under heavy load doing real work.
The C++ runtime handles highly concurrent workloads where TypeScript and Python-based harnesses silently drop data or crash. In benchmarks: 0 lost, 0 errors, fault-isolated, every run.
The C++ runtime handles highly concurrent workloads where TypeScript and Python-based harnesses silently drop data or crash. In benchmarks: 0 lost, 0 errors, fault-isolated, every run.
#6 Lock-in
Switching model versions or providers means refactoring your stack.
Swap a provider the way you swap a node. RocketRide's substrate and node API abstraction means portable JSON in, portable JSON out. No rewrite; a config change.
Swap a provider the way you swap a node. RocketRide's substrate and node API abstraction means portable JSON in, portable JSON out. No rewrite; a config change.
The Software Development Lifecycle for the Agentic AI Era
Own and control your AI solutions across the whole lifecycle, with the visibility to prove ROI and keep token costs down.
Problem
One artifact, authored any way. Every provider, including your own.
Agentic coding runs on hope: hope it understood you, hope the architecture is right, hope the code holds up. Then come the days lost to finding the right tools, wiring them together by hand, and moving data between stages yourself.
Solution
- 01Describe what you want, not how to do it
- 02Everything is a node: models, data stores, tools, your own business logic
- 03Substrate and glue code fully abstracted
- 04Author your way: agentic, composed, or hand coded, in your IDE or CLI
Problem
"It works on my laptop" is where most AI work stops. One button is where ours ships.
Deploying off a laptop means requesting a server and waiting in the queue, picking between Docker, Kubernetes, and cloud images, negotiating runtime and module versions, and rebuilding the whole pipeline for production.
Solution
- 01One Deploy button is the entire handoff
- 02The same runtime runs on the laptop and in production
- 03Nothing rebuilt for prod, no server requested, no module list negotiated
Problem
You never test an approximation of production. You test the thing that is released.
Laptop, staging, and production each run different runtimes, versions, and hardware, so what you test is never what actually gets released.
Solution
- 01Only the hardware changes; the runtime stays the same
- 02Every node, token, and call is traced
- 03See where it failed, what it cost, and how long it took
Problem
Going live is one button. So is going back.
There is no agreed definition of ready: approval happens in chat, Ops inherits something it cannot inspect, nobody records who released what or when, and rolling back means rebuilding.
Solution
- 01Push Deploy: ship exactly what you staged, every time
- 02Every release on record
- 03Going back is the same button
Problem
Most pipelines in production never change again. These change the same way they released.
Nobody remembers how the pipeline was built, changing a model means changing code, and token spend cannot be attributed to anyone. So it stays as it is, quietly getting worse.
Solution
- 01Token spend per user, per team, per org
- 02Keep models up to date without rewrites
- 03Replay, rewind, and fast forward any run, fully traced
- 04Edit nodes in place for side-by-side comparison on real data
