Custom Models & ML
Some problems need a model built for your data. Many don't. We help you tell the difference.
When a general-purpose model connected to your information isn't enough, a fine-tuned or task-specific model may be. We start by assessing whether custom modeling is justified, then build, evaluate, and monitor it properly. We do not train frontier foundation models from scratch.
Custom Models & Machine Learning
What we can build
Model necessity assessment
An honest look at whether a custom model will outperform a simpler approach for your task.
Fine-tuning existing models
Adapting a suitable existing model to your terminology, formats, or style using your examples.
Task-specific predictive models
Purpose-built models for scoring, ranking, or predicting outcomes from your historical data.
Demand and operational forecasting
Forecasts for volume, inventory, staffing, or other operational measures.
Classification and extraction
Models that sort documents and pull out fields consistently at volume.
Evaluation and monitoring
Clear quality measures, holdout testing, and ongoing checks for drift.
Three different approaches
These are often confused. Most projects only need the first.
- Approach 1
Connect an existing model to your information
A capable general model reads from your documents or systems at the time of each request. No training involved. Fastest to deliver and easiest to update.
- Approach 2
Fine-tune an existing model
An existing model is further trained on your examples so it follows your formats, terms, or judgments more reliably. Requires quality example data.
- Approach 3
Develop a task-specific ML model
A dedicated model, often a traditional machine learning model, built for one prediction or classification task from your historical data.
Illustrative use cases
Where this tends to help
Examples of the kinds of problems this service addresses. They are not client case studies.
Document classification
Sorting incoming documents by type and urgency to feed downstream workflows.
Demand forecasting
Projecting order volume by product line to inform purchasing and staffing.
Quote outcome prediction
Estimating which open quotes are likely to close based on historical patterns.
Typical deliverables
- Feasibility memo with a recommended approach
- Data preparation and labeling plan
- Trained or fine-tuned model with documented evaluation
- Deployment as a service or integration into a workflow
- Monitoring for quality and drift
- Retraining guidance
What we need to assess first
- Whether a simpler approach would meet the goal
- How much relevant, labeled historical data exists
- How quality will be measured and what level is acceptable
- How predictions will be used in a real decision
- Who owns the model and its upkeep after launch
Questions
Do we need a custom model?
Often not. Many business problems are well served by connecting a capable existing model to your information. We'll recommend custom modeling only when the evidence supports it.
How much data do we need?
It depends on the task. We review what you have during assessment and tell you plainly whether it's enough.
Will the model be 100% accurate?
No model is. We agree on quality measures up front, test against held-out data, and design the surrounding process to handle errors.
Do you build foundation models?
No. We fine-tune and build task-specific models. Training large foundation models from scratch is outside our scope and rarely sensible for a business.
Have a process in mind?
Describe it in your own words. We'll review it and respond with practical next steps, not a sales script.