Backed by Y CombinatorAI ML EngineersShip more of the roadmap
Hire AI ML engineers that take the pipeline and production work off your team, so the roadmap ships faster.
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How Jarmin Works For You
Operates like a
full-time ML EngineerWorks alongside your team, takes the volume off their plate, improves with their feedback, and keeps your engineers up to speed along the way.
Ramp-up in days,
knows your stackLearns your data, your pipelines, your conventions, and the standards your team holds, so what it ships looks like your team wrote it.
Works inside
your systemsConnects to your data warehouse, feature store, experiment tracking, model registry, serving, and monitoring, and the rest of the stack the work runs on.
Visible to your
whole teamSends status updates, takes feedback from anyone on the team, and escalates anything that needs an engineer's call instead of deciding on its own.
ML Engineering handled for you
Data and feature pipelines
Does the data work that eats most of the week, and keeps features consistent from training through serving.
- Raw data profiled, cleaned, validated against your schema, and turned into features
- Feature pipelines kept consistent between training and serving, so training-serving skew does not quietly cost you accuracy
- Backfills and point-in-time joins handled, with the silent data issues flagged before they reach a model
Experimentation and training
Runs the experiment loop end to end and tracks every run so a result is reproducible and the impact is measured, not assumed.
- Baselines stood up first, with hyperparameter sweeps run and ranked, so you know whether the heavier model earns its cost
- An eval harness with held-out and sliced metrics, so a strong average does not hide a weak segment
- Champion-challenger comparisons set up so a new model has to beat what is live before it ships
RL environments
Builds the environments to train and evaluate agents and models against your own tasks, with the rewards grounded in what counts as correct for you.
- Task suites built from your workflows, with verifiers and rubrics that score against your ground truth
- Verifiable rewards so a model is reinforced on outcomes that actually check out, not on gaming a score
- Pass@k evals and ablations to show whether the training moved the metric before anything ships
Deployment and serving
Takes a trained model and makes it production-grade, served, and rolled out without surprises.
- Models packaged and served behind a reliable, observable endpoint
- Shadow and canary rollouts so a new model proves itself before it takes real traffic
- Latency and cost tuned so serving holds up under load and inside your p99 budget
Monitoring and observability
Watches production for the silent failures that do not throw errors, and keeps models current instead of quietly stale.
- Data drift, concept drift, and silent failures watched continuously, not just at a morning check
- Observability that surfaces why a model moved, not just that a number dropped
- Retraining run on your cadence and your triggers, with promotion held for your team's approval
Platform and on-call
Carries the glue work and the on-call load that pulls senior engineers off the roadmap.
- Pipeline plumbing, boilerplate, and the orchestration glue that nobody wants to own
- Reproducible, versioned pipelines, so a model in production traces back to the data and code that made it
- The repeatable on-call load handled, so your engineers are interrupted less and ship more
Managed AI & ML engineering
Ship more AI features with Jarmin. From messy data to production models, experiments, evals, and ongoing improvements.
ML Engineering
Models
Trained and fine-tuned on your data, from messy inputs all the way to production.
RL Environments
Domain-specific environments and verifiable rewards to improve model performance.
Evals
Measures model quality and performance against benchmarks.
Agentic systems
Agents
Production-ready agents that Jarmin runs, maintains, and improves over time.
Task Automation
Repetitive workflows your team runs, automated start to finish.
Search
Agentic search that reasons over your data to find exactly what's needed.
Connecting it all
Integrations
Connections to the data and systems the work needs, with the ability to operate inside them.
Production ready ML models
Production models, ready to ship
Work comes back as reviewable changes that fit your patterns, your tests, and your CI, so it merges like a strong engineer's would. It is built to maintain and extend, not to rewrite a quarter later.
Reproducible and observable
Every run is versioned, so a model traces back to the exact data and code behind it, and what ships is monitored. You get lineage and observability built in, not a black box you have to take on faith.
Runs inside your platform
Works against your stack, your governance, and your deployment path instead of around them. Your data stays in your environment and is never used to train shared models.
Your engineers stay in control
It carries the repeatable work and frees your team for the modeling and the judgment. Architecture, model risk, and the go-ahead to production stay with your engineers, and nothing ships because an AI decided it should.