AI & IT Solutions
Models that make decisions, and keep making good ones.
Seaggle builds forecasting, classification, ranking, and anomaly-detection systems with the feature pipelines, calibration, and monitoring that decide whether a model is still trustworthy six months after launch.
When this fits
Signals that this is the right conversation
A model that scores well offline is not a system. The failure modes that matter appear later: features computed differently in training and serving, distributions that drift, probabilities that are ranked correctly but calibrated badly, and no one monitoring any of it. Those are the problems that make a good model quietly expensive.
- Decisions are made on rules or intuition where enough history exists to do better
- A model exists in a notebook but has never survived the path to production
- Forecasts drive planning and are wrong often enough to be routinely overridden
- You need scores that are not only ranked correctly but calibrated well enough to set thresholds
- A deployed model's performance has degraded and nobody noticed until the business did
What Seaggle delivers
Deliverables, arranged by when they arrive
Each of these is an artifact you receive and can use without us in the room.
Assess
- Problem framing — the decision the model informs, and the cost of each error direction
- Data readiness, leakage checks, and honest baseline from the current process
- Evaluation designed around the decision, not around a default metric
- Feasibility read: whether this is a modelling problem at all
Build
- Feature pipelines with train/serve parity so the model sees the same thing twice
- Model development with proper temporal or grouped validation
- Calibration and threshold selection tied to the business cost of each error
- Explainability appropriate to the decision and the people accountable for it
Operate
- Deployment as batch, streaming, or real-time service to match the decision cycle
- Monitoring for data quality, feature drift, and performance decay
- Retraining pipeline with promotion gates rather than automatic replacement
- Model registry, lineage, and documentation your team can maintain
Solution patterns
Shapes this work commonly takes
These are patterns rather than products. Select one to see how it works, where a person stays in the loop, and what we design against.
Demand and capacity forecasting
Planning runs on spreadsheets and judgement, and error is expensive in both directions.
- How it works
- Build hierarchical forecasts with proper backtesting on rolling origins, reconcile across levels, and publish prediction intervals rather than a single number.
- Human checkpoint
- Planners see intervals and drivers, and can override with the reason recorded.
- Risks we design against
- Overfitting to a stable recent period and silently failing at regime change. Watched with rolling backtests and drift monitoring.
Risk and propensity scoring
A threshold decision — approve, flag, prioritise — is made at volume.
- How it works
- Train on temporally split data, calibrate probabilities, and select thresholds against the actual cost of false positives versus false negatives rather than a default 0.5.
- Human checkpoint
- Threshold policy is a documented business decision, reviewed on a schedule.
- Risks we design against
- Proxy features that encode something you did not intend to model. Reviewed explicitly before deployment.
Ranking and recommendation
Relevance ordering drives engagement or conversion at scale.
- How it works
- Candidate generation then reranking, evaluated offline on ranking metrics and online through controlled experiments with guardrail metrics.
- Human checkpoint
- Business rules and exclusions applied as explicit policy, not buried in the model.
- Risks we design against
- Feedback loops that narrow what users ever see. Mitigated with exploration and diversity constraints.
Representative workflow
End to end, with the checkpoints visible
A worked example of how the pieces connect in production. The static sequence below is the whole content — nothing is hidden behind animation.
- 01
Frame the decision
What action follows the prediction, and what each error actually costs.
- 02
Establish the baseline
Measure the current process honestly — it is often better than assumed.
- 03
Build features once
Shared definitions used by both training and serving, so parity is structural.
- 04
Train and validate
Temporal or grouped splits that respect how the data is really generated.
- 05
Calibrate and threshold
Turn scores into decisions using the business cost of each error.
- 06
Deploy to the decision cycle
Batch, streaming, or real-time — whichever the decision actually needs.
- 07
Monitor and retrain
Drift and performance watched, retraining gated on evaluation rather than a calendar.
Representative example
Evaluation & controls
What separates production work from a demonstration
A demonstration proves something can happen once. These controls are how you know it keeps happening correctly.
Train/serve parity
Features are defined once and consumed by both paths. Training-serving skew is the most common cause of a model underperforming in production and it is preventable.
Leakage review
Every feature is checked for information that would not have been available at decision time. A model that looks excellent is usually leaking.
Calibration, not just ranking
Where a probability sets a threshold or a price, it is calibrated and the calibration is monitored — good ranking with bad calibration silently misprices decisions.
Drift and quality monitoring
Input distributions, feature health, and outcome performance are tracked with alerting, so decay is detected before the business notices.
Gated retraining
A retrained model is promoted only after passing evaluation against the incumbent. Automatic replacement on a schedule is how quiet regressions ship.
Documented human override
Where people can overrule the model, the path is designed and the overrides are captured as signal rather than lost.
Technology we work with
Named to explain the work rather than to imply endorsement. Tool choices follow the requirement, and we work with what you already run wherever that is sensible.
- Python
- scikit-learn
- XGBoost / LightGBM
- PyTorch
- Feast
- Airflow / Dagster
- MLflow
- Evidently
- Snowflake / BigQuery
- Ray
Getting started
Where a first engagement begins
A scoped modelling engagement: one decision framed, a baseline established, and a validated model with the deployment and monitoring path designed before anything is promoted.
Discuss a modelling problemQuestions
Do we need machine learning, or would rules do?
Frequently rules do, and we will say so. Machine learning earns its cost when the pattern is genuinely complex, changes over time, and there is enough history to learn from. A rules baseline is part of the assessment precisely so the comparison is honest.
How do you handle explainability?
Proportionate to the decision. Where a person must justify an outcome, we favour models that can be explained and add attribution appropriate to the method. Where the decision is low-stakes and reversible, we do not pay a large accuracy cost for interpretability nobody will use.
What happens when the model degrades?
Monitoring detects it, alerting surfaces it, and the retraining pipeline is already built and gated. The fallback behaviour is defined before launch rather than improvised during an incident.
Discuss a modelling problem
Bring the problem, the current environment, and what better should look like. We will tell you what is realistic before anyone signs anything.