We build forecasting, early-warning and scenario-planning systems on clean, versioned data. Each model is tested against past results before anyone relies on it, then monitored once live, so your team can trust the numbers it plans with.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
Forecast demand, capacity, revenue and throughput, with backtesting and scenario overlays.
Versioned datasets with quality checks and a record of where each field comes from, so model inputs stay stable.
Spot drift, breakdowns and unusual activity early, with alerts and triage workflows.
Turn predictions into actions such as reorder points, staffing and routing, within your constraints.
Scheduled training, a model registry and CI/CD, so new model versions are tested and released the same way every time.
Decision logs, reproducible runs and access controls that make every output easy to check.
Our approach
The usual way
Model inputs break quietly when a schema changes, a data feed goes missing or a pipeline fails.
How we do it
Data quality gates, lineage and versioned datasets keep the inputs to every model stable.
The usual way
Forecasts look good in slides but are never tested against reality.
How we do it
Rolling backtests, benchmarks and scenario tools show how a model performs before anyone relies on it.
The usual way
Predictions sit in a dashboard and never reach planning or operations.
How we do it
Outputs feed into your workflows and systems, with drift alerts and activity records.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
Versioned datasets, quality checks and schema contracts keep production inputs stable.
Features built only from data that was available at the time, so the model isn't trained on information it won't have when live.
Models tested on past periods and compared with simple baselines, plus scenario tools planners can try before they commit.
Serving, drift detection, alerts, decision logs and retraining triggers that keep the system accurate.
How we deliver
4 phases, each ending with an output you can review.
We agree which decisions the model should support, check your data and set the baselines it has to beat.
Output: Predictive feasibility blueprint
We build features, test models on past periods against those baselines and check them under different scenarios.
Output: Tested model baseline
We ship model serving, alerts, drift monitoring and activity records, connected to your workflows.
Output: Production predictive stack
We improve the models using monitoring signals and retraining triggers, then extend them to more decisions.
Output: Measurable decision impact
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Designs forecasting and scenario systems: evaluation, baselines, time alignment and decision outputs.
Builds data contracts, quality gates, lineage and reliable features that survive schema changes.
Sets up CI/CD, a model registry, scheduled training and stable serving for predictable deployments.
Runs monitoring, drift detection and alert triage, and tracks how predictions are used in decisions.
Trust and control
Rolling evaluations and baselines are checked before any output is used in planning.
Contracts and quality checks stop bad or incomplete data from reaching the models.
Role-based access, lineage and reproducible runs to meet your security and compliance needs.
Decision logs, data quality alerts and retraining triggers, all fully traceable.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We built an ML forecasting tool for a hospital pharmacy network that uses patient volumes and procedure schedules to stock costly drugs with a short shelf life. Waste fell 18%, and the fill rate rose 22%.
We built an AI staffing tool for a hotel operator that combines live bookings with flight delays and local events to recommend shifts before demand arrives. Overtime fell 30%, and forecasts are 92% accurate.
We built a workforce planning platform for a supply chain operator that models growth scenarios from ERP and HR data to plan hiring years ahead. Hiring costs fell 22%, and forecasts are 94% accurate.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
Predictive analytics forecasts demand, capacity, revenue or staffing, flags unusual activity early and tests what-if scenarios before you commit. Our forecasting tool for a hospital pharmacy network cut inventory waste by 18%. A staffing tool we built for a hotel operator reached 92% forecast accuracy and reduced overtime by 30%.
We run rolling backtests: the model forecasts past periods it hasn't seen, and we compare the results with what actually happened and with simple baselines. Planners can also try scenarios to see how forecasts change. A model is used in planning only after it has been checked against those baselines.
You need historical records of what you want to predict, such as sales, bookings or patient volumes, plus the signals that drive it. Our workforce planning platform modelled growth from ERP and HR data, and our hotel staffing tool combined live bookings with flight delays and local events. The data audit shows whether you have enough.
Monitoring tracks data quality and model accuracy once the model is live, and alerts the team when either drifts. Retraining triggers can refresh the model on newer data, and decision logs show which predictions were used. Problems are caught by monitoring instead of being discovered by your operations team.
Predictions go to the places your team already works, such as planning tools, operational systems or alert thresholds, not just a dashboard. Outputs can become actions like reorder points, staffing levels or routing, within your constraints. Every output is logged, so you can trace what the model recommended and when.
You own 100% of the code, models and IP, and this is written into the service agreement. Your data stays yours. Training runs are reproducible and models are kept in a registry, so your own team can retrain, audit or move them without depending on us.
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