Synthetic data
Generate, transform, validate, and refine datasets through explicit stages with separate quality checks.
Open source · CLI + library
More useful work from every model call.
Ploinky Worker turns repeatable LLM jobs into small, deliberate pipelines. Match every step with the right-sized model, run against local or remote infrastructure, and keep throughput, cost, and results visible.
# queue a repeatable LLM task $ pworker run ./profile.mjs \ --input '{"page":"report.html"}' \ --async { "status": "queued", "phase": "extract" } # inspect it without holding the process $ pworker --status 9f3… { "status": "running", "phase": "classify" }
Right-sized modelsChoose capability per pipeline phase
Local or remoteUse local servers and cloud providers
Batch-awareCombine compatible repetitive requests
ObservableTrack status, limits, traffic, and results
Built for agentic workloads
Repeatable work deserves a repeatable system.
Use Ploinky Worker when the shape of the task is known but the inputs keep changing. Each run follows an inspectable flow instead of starting an open-ended agent loop.
Tests and evaluations
Run LLM-backed test suites and evals as background jobs, then inspect the current phase and persisted result.
Marketing profiling
Extract signals, segment audiences, compare messages, and produce structured profiles across many inputs.
Large-page analysis
Break long documents or pages into controlled extraction, classification, synthesis, and validation steps.
Bulk processing
Queue many independent jobs, group compatible requests, and route each answer back to the correct task.
File workflows
Mix model calls with JavaScript steps that read, transform, and write files in a caller-selected directory.
Small pipelines, precise models
Spend intelligence where it matters.
A task is a readable sequence of phases. Each phase can request a logical capability tier, perform a deterministic JavaScript operation, or finish with a structured result. The actual model behind a tier is configured once and can point to a remote provider or local endpoint.
This lets routine extraction stay on a small model while nuanced synthesis or validation moves to a stronger one—without hard-coding a provider into every task.
01
Split and prepareDeterministic JavaScript
no model
02
Extract signalsFast, repeated model work
tiny tier
03
Classify and scoreStructured model output
small tier
04
Synthesize the resultNuanced final reasoning
good tier
05
Persist and reportStructured result + files
no model
Infrastructure efficiency
Feed the hardware, not the overhead.
Ploinky Worker is designed to keep useful work flowing through the model infrastructure you already have—from a developer machine to cloud or shared GPU-backed endpoints.
Pack eligible phase requests together while keeping every task ID, result, and continuation independent.
Route by tier, throttle requests, cache complete repeats, and observe request counts and transferred bytes.
Queue repeatable jobs so capable local or remote schedulers have a steady stream of appropriately sized work.
Clear boundary: prompt batching can reduce repetitive requests, but token savings, latency gains, and GPU saturation depend on the selected model server, provider, scheduler, and workload. Ploinky Worker supplies the task, queue, tier, and dispatch structure needed to optimize them deliberately.
Control and visibility
Know what is running—and why.
Submit work synchronously or return a task ID immediately. Check the active phase later, retrieve the final result, and tune the pipeline using observable runtime behavior.
Persistent background tasks
Queued work can continue independently while clients poll for status, completion, or a bounded error.
Provider flexibility
Connect provider APIs, OpenAI-compatible endpoints, or configured local models behind the same task format.
Inspectability by design
Readable task files and explicit phase transitions make workflows easier to review, test, and improve.
Open source, with help when you need it
Start with the code. Scale with a workload-specific design.
Ploinky Worker is open source. Explore the task format, CLI, provider routing, local models, batching, monitoring, and background execution today. For advanced deployments, the team offers consulting on pipeline design, provider topology, batching strategy, shared GPU infrastructure, and local inference integration.