Models built on your data.
Custom machine learning when a general-purpose LLM is not enough: domain-tuned speech-to-text, document AI, scoring, classification, ranking, recommendation, forecasting and decision models. Trained on your data, measured against what you do today and run in production.
Tell us what you want to predict- For
- Teams with a specific, repeated decision to automate
- Starts with
- A data review and a baseline model
- Then
- Models in production, with monitoring and retraining
- Use cases
- Speech, documents, scoring, ranking, forecasting
When an LLM
is not enough.
We build the model that fits the problem, and the pipeline that keeps it accurate.
A machine learning model learns patterns from examples, usually historical data labelled by your team. It makes sense when the task is specific, measurable and repeated thousands of times: transcribing calls with your industry’s vocabulary, scoring a risk, ranking candidates or forecasting demand. There, a general-purpose LLM is usually more expensive, slower and harder to explain.
- DataWhat history and labels you have, and what is missing to train.
- BaselineRules or a simple model first, so we know what has to be beaten.
- TrainingSupervised, ranking or forecasting models, or speech-to-text tuned to your domain.
- EvaluationOn data the model has never seen, with metrics agreed with the business. This is where most ML projects fail.
- ExplainWhy the model decides what it decides, case by case.
- MLOpsDeployment, drift monitoring and retraining, in your cloud.
- Python
- Supervised ML
- Fine-tuned ASR
- Keras
- NLP
- SHAP
- AWS SageMaker
- Vertex AI
- Azure
- AWS Kinesis
The terms, in one sentence
- Supervised learning
- The model learns from examples with the right answer, such as calls your QA team has already reviewed.
- Fine-tuned ASR
- A speech-to-text model retrained on your audio so it understands your terminology and your customers’ accents.
- Ranking model
- Orders options (experts, products, cases) from best to worst fit for each situation.
- Drift
- When real data moves away from the training data and the model quietly gets less accurate.
- SHAP
- A technique that shows how much each input weighed in a specific decision of the model.
- MLOps
- The practices and tools for deploying, monitoring and retraining models the same way you manage the rest of your software.
What you get.
- Data reviewWhich data is usable, what is missing and how to label it, written down.
- Trained modelsVersioned, with the training code and the evaluation data.
- Evaluation reportMetrics on unseen data and an error analysis, compared with the baseline.
- MLOps pipelineDeployment, drift monitoring and retraining on AWS SageMaker, Vertex AI or Azure.
- ExplainabilityEvery decision the model makes can be explained and reviewed.
- You own the systemCode, infrastructure and documentation, in your repository and your cloud account from day one.
What people ask us.
When do we need custom machine learning instead of an LLM?
When the task is narrow, repeated and measurable: scoring, classifying, ranking, forecasting or transcribing with a very specific vocabulary. There, a custom model is usually more accurate, cheaper per use and easier to explain. For understanding or generating free text, an LLM is usually the better starting point.
How much data do we need to train a custom model?
It depends on the task, and we check it early with a real sample. The data often already exists: at a US contact center, the scoring model learned from the reviews the QA team was already doing by hand.
Can you fine-tune speech-to-text for our industry?
Yes. For that same contact center we fine-tuned the speech model on its audio, its terminology and its customers’ accents, reaching over 95% transcription accuracy.
How do you explain a model’s decisions?
With techniques such as SHAP, which show which inputs weighed in each decision, and by combining the model with deterministic business rules where the result has to be exact. That way every decision can be reviewed and audited.
Where do the models run?
In your cloud: AWS SageMaker, Google Cloud (Vertex AI) or Azure, or on your own infrastructure. Projects such as BCN by Bike and Sync Health already ran models on AWS SageMaker.
Do we own the system?
Yes. The code, infrastructure and documentation live in your repository and your cloud account from the first sprint. If you want, we keep running and evolving it with you.