Custom machine learning
built for real operating conditions.
Predictive models, recommendations, forecasting, and vision — validated on real deployment conditions, with drift monitored after launch, not assumed away.
Machine learning
for real operating conditions.
Predictive models, recommendation systems, classification, NLP, forecasting, anomaly detection, and computer vision — built and deployed against the conditions the model will actually run in, not just the conditions it was trained on.
Different tools,
different problems.
| Problem shape | Right fit |
|---|---|
| Predict a number or category from structured data with labeled history | Machine learning (this page) |
| Generate or reason over unstructured text and conversation | Generative AI |
| Rank or recommend items from historical behavior | Machine learning (this page) |
| Both — e.g. an ML risk score feeding an LLM-generated explanation | Combined, scoped together |
From raw data
to a monitored model.
| Data-readiness check | What we're actually looking for |
|---|---|
| Volume | Enough historical examples per class/outcome to learn a reliable pattern, not just "a lot of rows" |
| Label quality | Labels that are consistent and correct — a noisy label is worse than a missing one |
| Recency | Historical data that still reflects current conditions, not a pattern that shifted two years ago |
| Class balance | Enough examples of rare-but-important outcomes (fraud, defects, churn) to learn from, not just the common case |
Set per problem type,
before training starts.
Precision and recall per class, not just overall accuracy.
RMSE or MAPE against a realistic baseline, not a naive comparison.
Precision@k and measured business impact, not just offline ranking metrics.
False-positive rate your team can actually tolerate in practice.
Accuracy measured on real field conditions, not just the lab test set.
Where lab accuracy
met the real line.
A vision model that hit 94% accuracy in testing dropped to 61% on the production line — the gap we closed by training and validating against real deployment conditions, not the studio test set.
lower per-document inference cost on a fintech underwriting pipeline, through routing and caching that kept accuracy constant while cutting spend.
Where structured-data
ML earns its keep.
Fintech risk and fraud scoring, e-commerce recommendation and demand forecasting, manufacturing visual inspection and predictive maintenance — see e-commerce for one live example, manufacturing & agritech for predictive maintenance and defect detection specifically, with more industries in development on our Industries hub. A meaningful share of this work is delivered by our India-based engineering team.
No sales pitch,
just how we work.
Not just a flattering lab test set.
Acceptance criteria agreed up front, per problem type.
Month-six accuracy is measured, not taken on faith.
You own the model, pipeline, and configuration at completion.
Tools we've shipped
in real projects.
Before you
book a call.
When should we use ML instead of generative AI?
When the problem is prediction, classification, ranking, or anomaly detection over structured data with a clear label to learn from. Generative AI earns its place on unstructured text and conversation.
What data do we need to get started?
Historical examples with outcomes you can learn from. We assess data readiness honestly before committing to a build.
How do you know if the model is actually good enough?
Acceptance metrics are set per problem type before training starts, and validated on held-out data your team agrees represents reality.
What happens after the model is deployed?
Drift monitoring and a retraining cadence, covered under MLOps.
What specifically makes data "not ready" for an ML project?
Most often one of four things: not enough historical examples of the outcome you're predicting, inconsistent or incorrect labels, data that's stale relative to current conditions, or too few examples of a rare-but-important outcome like fraud or defects. We check all four honestly before committing to a build.
What actually triggers a model retrain once it's live?
A measured condition, not a calendar guess — input drift past a threshold, performance decay below an agreed floor, or a fixed schedule for slower-moving domains. See our MLOps page for how these thresholds are set.
Tell us what
you're trying to predict.
Book a 30-minute call — we'll tell you honestly whether your data is ready for it yet.