Machine Learning · San Francisco, California
Machine learning aimed at business metrics, not benchmarks.
From data strategy to deployed models — forecasting, recommendations, and classification built around the metrics your business actually moves on.
What it means
Pragmatic ML that ships and earns its keep.
Good machine learning is not the model with the best benchmark — it is the one that moves a number your business cares about and survives contact with production. We start from the metric, work back to the data and model, and stop when it is deployed and demonstrably better than what you had.
We apply ML across our own products — recommendations in StreamScanner, AI scoring and ranking in IdeasBerg, and anomaly detection in log.fail — so the recommendations you get are battle-tested.
Once models are live, keeping them healthy is MLOps; building on language models specifically is LLM development.
Scope
What ML consulting covers.
Data Strategy
Assess the data you have, what is missing, and the shortest path from it to a useful model.
Forecasting
Demand, usage, and time-series forecasting tied to the decisions they inform.
Recommendations
Recommendation and ranking systems that lift engagement and conversion.
Classification
Classification and scoring models for the judgements your product makes at scale.
Model Deployment
Getting models off the laptop and into production behind real APIs and surfaces.
Monitoring
Tracking model performance against business metrics, not just offline accuracy.
How we work
Three steps, no theatre.
Call
A short scoping call. You describe the problem and constraints; we tell you honestly whether and how AI helps — and whether we are the right team.
Scope
A concrete plan: what we build, how we measure it, the timeline, and the path to production. No open-ended retainers dressed up as strategy.
Ship
We build, evaluate, and deploy — then hand over a running system with the monitoring and docs to operate it. We can keep running it if you want us to.
FAQ
Questions, answered.
What is machine learning consulting?
It is help turning a business problem into a deployed ML solution — data strategy, model development, and deployment for things like forecasting, recommendations, and classification — measured against business outcomes.
Do you start from scratch or improve existing models?
Both. We build new models from your data and also improve, retrain, or productionise models you already have.
How do you measure success?
Against the business metric the model is meant to move — conversion, retention, cost, accuracy of a decision — not just offline benchmark scores.
Do you deploy the models or just advise?
We deploy. The deliverable is a model running behind a real API or product surface, with monitoring, not a notebook and a recommendation.
How do we get started?
Email info@squidcode.com with the problem and the data you have. We will reply with next steps and a scoping call.
More services
Related AI consulting services.
Ready to put ML to work?
Tell us what you are building. You will hear back from an engineer, not a funnel.
info@squidcode.com