Autonomous model engineering

Model engineering,
wired together.

PodTune turns MLOps into a schematic you can touch. Drag intelligent Pods onto a bench, wire them into a pipeline, and let them plan, train, and improve on their own - locally or in the cloud.

Request demo →Open the bench
Live preview - Data Sources → Training → Deployment
POD·01Data SourcesPOD·06Fine-tuningPOD·08EvaluationPOD·11Deployment
Manifesto

AI shouldn't be built from brittle scripts and disconnected tools stitched together by hand. Model engineering should be composable, visual, and autonomous.

Every capability lives in a Pod. Every Pod understands its job, talks to its neighbors, and keeps improving the result.

You stop assembling pipelines and start assembling systems. PodTune is where AI builds AI.

SYSTEMPodTune v0.1
RUNTIMEPodman containers
UNITPod - one capability, self-contained
WIRINGDrag + connect, no orchestration code
DECISIONSMade by the Pod, not a static DAG
TARGETSLocal machine, AWS, GCP, Azure
OUTPUTData → Model → Product
The bench

Snap pre-assembled Pods together. Skip the endless reprompting.

Skip the trial-and-error of prompting a model to guess your pipeline. Drag a Pod from the tray onto the bench, then drag from its right pin to the next Pod's left pin to wire them together. Hit Run to watch a signal move through your chain.

bench.podtune - untitled pipeline
Pod catalog - drag onto bench
Data Sources
Data Engineering
Data Cleaning
Synthetic Data Generation
Embeddings
Training & Fine-tuning
Reinforcement Learning
Evaluation
Benchmarking
Model Registry
Deployment
Serving
Monitoring
Continuous Learning
POD·01
Data Sources
POD·06
Training & Fine-tuning
POD·08
Evaluation
POD·11
Deployment
drag pins to wire · drag nodes to move · tap ✕ to remove
Pod catalog

Fourteen capabilities. One consistent shape.

Every Pod is a versioned, self-contained unit with its own objective, tools, and autonomy. Mix them into any pipeline shape you need.

POD·01DATA
Data Sources
Connect databases, APIs, files, and streams as raw input.
POD·02DATA
Data Engineering
Transform, join, and structure raw data into usable form.
POD·03DATA
Data Cleaning
Detect and repair missing, duplicate, or noisy records.
POD·04DATA
Synthetic Data Generation
Generate labeled data to fill gaps and scale training sets.
POD·05DATA
Embeddings
Convert data into vector representations for retrieval and training.
POD·06BUILD
Training & Fine-tuning
Run LoRA, full fine-tunes, or continued pretraining, chosen automatically.
POD·07BUILD
Reinforcement Learning
Align models with reward signals and human feedback.
POD·08VALIDATE
Evaluation
Score outputs against quality, safety, and task benchmarks.
POD·09VALIDATE
Benchmarking
Compare runs against baselines and prior checkpoints.
POD·10OPS
Model Registry
Version, tag, and store trained checkpoints.
POD·11OPS
Deployment
Package and ship models to a target environment.
POD·12OPS
Serving
Run low-latency inference endpoints.
POD·13OPS
Monitoring
Track drift, latency, and quality once in production.
POD·14OPS
Continuous Learning
Feed production signal back into the next training run.
Request demo
Open the bench
Deploy anywhere

Same Pods. Your laptop or your cloud account.

Pipelines built on the bench run identically wherever you point them - because every Pod is just a Podman container underneath.

Local bench

Train your own model, on your own machine.

Native on Linux, and fully supported on macOS through a lightweight Podman machine. Keep your data, your persona, and your checkpoints on hardware you control - personal or commercial.

macOSLinuxPodman
# one line to bring the bench online podman machine init && podtune bench up
Cloud fleet

Push the same pipeline to production infrastructure.

Export any bench as a portable Pod manifest and hand it to a managed cluster when you need scale, shared serving, or team-wide access.

AWSGoogle CloudAzure
# deploy the exported manifest podtune deploy ./bench.pod.yaml --target aws

Stop scripting pipelines. Start wiring Pods.

Request a demo, open the bench above, or bring PodTune to your own machine in one command.

Request demo →Open the bench