Machine Learning Development
Our machine learning development takes models out of notebooks and into production — LLM-powered features, predictive systems, and intelligent automation built into real products with the engineering rigor they need to be trusted.
The hard part of machine learning was never the demo — it's everything around it: data pipelines, evaluation, deployment, monitoring, and the product experience that makes a model's output actually useful. That's where a software studio with 13+ years of shipping discipline earns its keep.
We work across the practical ML spectrum: large language models for understanding and generating text, retrieval systems that ground answers in your data, classification and prediction models for business decisions, and computer-vision or OCR pipelines for documents and images. We favor the simplest approach that meets the accuracy bar — sometimes that's a fine-tuned model, often it's a well-orchestrated API, occasionally it's classic ML that runs for pennies.
Every ML engagement includes honest feasibility assessment up front. If your problem doesn't need machine learning — or your data can't support it yet — we'll say so, and usually propose a cheaper path to the same business outcome.
Machine learning services
- LLM application development
- Retrieval-augmented generation (RAG)
- Predictive and classification models
- Recommendation systems
- Computer vision and OCR pipelines
- ML model deployment and MLOps
- Evaluation harnesses and monitoring
- Data pipeline engineering
Why teams choose Wholly Software
Production is the goal
Deployment, monitoring, and cost control designed in from the start — not bolted on after the demo.
Right-sized solutions
We choose between APIs, open-source models, and classic ML based on your accuracy, privacy, and budget — not hype.
Evaluation you can trust
Measurable quality baselines before launch, so 'the model seems good' becomes 'the model is 94% accurate on our test set.'
Product thinking included
ML output is only valuable inside a usable product — and building products is what we've always done.
Frequently asked questions
Do we need a lot of data to start?
Less than you might think. Modern foundation models perform well with careful prompting and retrieval over modest datasets. Where training data is needed, we help you assess and prepare what you have.
How do you handle data privacy?
We design for your constraints — from provider APIs with strict data agreements to fully self-hosted open-source models running inside your infrastructure.
What does an ML project cost?
Most engagements start with a scoped pilot that proves value on one workflow before wider investment. Reach out with your use case and we'll outline a staged plan.
Have a product in mind? Let's build it together.
Tell us about your idea and timeline — we'll get back to you with next steps within one business day.