Machine Learning · Production Models

ML models that ship to production — or honest advice that you don't need them

We build custom ML — forecasting, scoring, vision — deployable on your own cloud or on-prem. And we'll tell you honestly when a well-built RAG system or AI agent solves your problem cheaper than a custom model. Fixed scope, $10k–$25k.

Scope your ML project →See our AI work
Production MLforecasting, scoring, vision
Your infraown cloud or on-prem
Honest scopingML only when it's the right tool
$10k–$25kPoC, fixed scope

What we build

ML for the problems that actually need it

Plain list. Custom ML where it earns its cost — and a straight answer where it doesn't.

ForecastingDemand, revenue, and time-series prediction models that inform real decisions.
Scoring & classificationRisk scores, lead scores, fraud flags — models that rank and categorize at scale.
Computer visionImage classification, detection, and extraction for real production use.
Recommendation enginesPersonalization models that lift engagement and conversion.
MLOpsDeployment, monitoring, and retraining pipelines so models stay accurate over time.
Honest alternativesWhen an LLM with good retrieval beats a custom model, we say so — before you spend.

Proof

We build ML — and know when not to

Half our ML inquiries are better served by an AI agent or RAG. We tell you which, honestly.

Custom ML

Forecasting, scoring, vision

Models built and deployed on your infrastructure when off-the-shelf AI isn't enough.

Production-deployed.
Or RAG/agents

Often the cheaper answer

For document Q&A and knowledge tasks, retrieval beats custom ML — we build both and recommend honestly.

We tell you which.
MLOps

Models that stay accurate

Deployment, monitoring, and retraining so accuracy doesn't quietly decay.

Built to last.

Know the number before you commit

ML PoCs run $10k–$25k fixed scope (2–6 weeks): data assessment, model development, and a production deployment path. Data readiness is the biggest cost driver, so we assess it first — you know the real scope before committing.

Talk through your problem →

How we build

Data first, honesty first

Most failed ML projects failed at the data, not the model. We start there.

Assess the data

We look at your actual data before quoting — if ML won't work on it, we say so.

Recommend honestly

Custom ML, RAG, or an agent — whichever genuinely fits, even if it's the cheaper one.

Build & measure

Accuracy tested against your real cases and reported plainly.

Deploy & maintain

On your infrastructure, with monitoring and retraining built in.

How we work & why it matters

We combine process, technology, and expertise so your product gets built right—from idea to launch.

We use proven stacks and clear delivery so you get results you can measure.

Technologies we use

We build with the stacks and tools your product needs.

PythonPython / Mojo
TensorflowTensorflow / Keras
PyTorchPyTorch / Lightning
⚙️MLFlow / Kubeflow
🐳Docker / K8s
📈Weights & Biases

Common questions

What is machine learning development? →

Machine learning development at Essen Software covers predictive models, MLOps pipelines, and production ML systems—from data audit and training to deployment and continuous learning—for automated, data-driven decisions.

How do you handle ML model drift? +

We implement automated monitoring that tracks drift in data distributions and decay in accuracy, triggering retraining alerts and validation checks so models stay reliable in production.

Can we run ML on our own cloud? +

Yes. We are cloud-neutral and deploy on AWS, Azure, GCP, or on-premise Kubernetes. Data stays in your environment; we use federated or encrypted training where required.

How much does a machine learning project cost? +

ML PoCs at Essen Software run $10k–$25k fixed scope (2–6 weeks): data assessment, model development, and a production deployment path. Data readiness is the biggest cost driver, so we assess it first — you know the real scope before committing.

Do we need custom ML, or would an LLM or AI agent be enough? +

Honest answer: often an LLM with good retrieval solves what used to need custom ML — cheaper and faster. Custom ML still wins for forecasting, scoring, and vision at scale. Roughly half our ML inquiries end up better served by AI agents or RAG — we tell you which in the first call.

Why choose Essen for machine learning

🛠️
Pragmatic ML
We focus on business ROI
⚡
Fast Inference
<1s latency guaranteed
🛡️
Explainable AI
No black boxes, only logic
📊
Managed MLOps
Continuous scaling & support
Optimize Your Model →

Not sure if you need custom ML or something simpler?

Thirty minutes. Describe the problem and your data. We'll tell you honestly whether ML is the right tool — and if a cheaper approach solves it, we'll say that too.

Scope your ML project