rapidfire.

Converge: Your FDE as an Agent

Give it your agent, your evaluation data, and your limits. Converge runs the experiment, stops what isn't working, clones what is, and explains every decision it makes.

01

Build

Write your agent with the frameworks and models you already use.

Converge works here

Configure

Find the combination of data, retrieval, models, and prompts that works best.

03

Deploy

Ship the winning configuration, backed by evidence.

04

Monitor

Watch it in production with your existing tools.

Define it once. Converge runs the loop.

  1. 01DefinePoint Converge at your agent, your evaluation examples, the metrics that matter, and your limits.
  2. 02Run and scoreEvery version runs at once on your hardware and is scored on each slice of examples as it goes.
  3. 03AdaptWeak versions stop. Strong ones are cloned, and new variants launch from what is working.
  4. 04ConvergeThe loop repeats until nothing is left to gain or your limits are reached, then hands you the top versions.
Runevery version at onceScoreon each slice of examplesAdaptstop · clone · launch newREPEATS UNTILnothing is left to gainDefineTop versionshanded to you

As hands-off as you want. Never a black box.

Let Converge run until it finds nothing left to improve, or hold it to the time and spend you set. Every stop, clone, and new variant is logged with the reason behind it.

Fully automatedRuns until it finds nothing left to improve.
Hard limitsNever exceeds the time or spend you set.
Soft goalsOptimizes the metrics you care about, in the order you rank them.
Step in any timeStop, clone, modify, or warm-start any run yourself with Interactive Control (IC Ops).

Two layers. The engine runs the versions. Converge decides what runs next.

Converge · Your FDE as an Agent · RapidFire AIINPUTSOUTPUTSDocs and tablesUnstructured and structuredRetrieval sourcesIndexes, knowledge graphsModel endpointsAPIs, open-weight, fine-tunedEvalsReferences and metricsRAPIDFIRE AIAUTOMATION LAYERDashboardWatch and steerevery runAutomation agentStops, clones andlaunches versionsGuardrails, goalsTime and spendlimits, metricsInterim metricsIC OpsStop · clone · modifyEXECUTION ENGINESingle-machine version is open sourceConfig generatorEvery combination of knobsData shard managerSplits evals into slicesHyperparallel executorRay (optional)HARDWAREOn-prem GPUsCloud GPUsHosted by RapidFireResults and artifactsEvery version, score, costSummary reportTop versions vs your goalsAudit logEvery decision, with its reason
Automation layerThe dashboard, the automation agent, and your guardrails. Reads interim metrics and sends IC Ops back down: stop, clone, or modify a run.
Execution engineGenerates every combination, shards your eval data, and runs versions side by side on the same GPUs. The single-machine version is open source.
Your hardwareOn-prem GPUs, your cloud, or hosted by RapidFire, with Ray as an option. Data, indexes, and models stay where they are.

Works with your stack

  • OpenAIModel provider
  • ClaudeModel provider
  • GeminiModel provider
  • MistralModel provider
  • QwenModel provider
  • DeepSeekModel provider
  • Hugging FaceFramework
  • PyTorchFramework
  • RayInfrastructure
  • MLflowTracking

“It is not apples to apples, because we were not able to try these many configurations before.”

Same four GPUs
Before
5–6 days
per sweep, for a few dozen configurations run one after another
With RapidFire
2,000+
configurations, with weak runs stopped and strong ones cloned mid-flight

Let Converge run your next experiment.

Run your first experiment free on Colab, or talk to us about your agent.