Capabilities
Everything between raw data and a better decision.
Each of these can stand alone. Most clients start with one and find the others follow, because a good forecast needs a good pipeline, and a good pipeline needs to know what the model downstream expects.
01 / DATA PIPELINES AND FUSION
One trustworthy view of many messy sources.
Real data arrives at different rates, on different clocks, with gaps, duplicates and silent unit changes. We build pipelines that treat that as normal. Every record is validated against a contract, every transformation is versioned, and sources are fused into a single estimate of state with its uncertainty carried through.
For high dimensional problems, with thousands of correlated channels, we reduce dimension where it helps and keep it where it matters, so models downstream see the structure in your data rather than the noise.
What you get
- Pipelines that fail loudly and early, never silently
- Full lineage from any output back to the raw records behind it
- Point in time correct features, so models never see the future
- Data quality dashboards your team can read at a glance
Methods we use
- Kalman, unscented and particle filters
- Multi rate alignment and asynchronous fusion
- Tensor decomposition, PCA, robust PCA
- Schema registries and data contracts
- Change data capture from operational stores
02 / STREAMING ARCHITECTURE
Real time that’s actually real time.
We design event driven systems around the latency your decisions need, then prove it under load. Windowing, watermarks and late arriving data are handled explicitly. State is checkpointed, so a failure costs seconds rather than a day of reprocessing.
Because every event is kept in an immutable log, you can replay history through a new model and see exactly how it would have behaved. That turns a risky deployment into a measured one.
What you get
- Latency and throughput targets agreed in writing, then load tested
- Exactly once processing where correctness depends on it
- Back pressure and graceful degradation, not dropped data
- Replay of any period through any model version
Methods we use
- Kafka, Redpanda, Kinesis, Pub/Sub
- Flink, Spark Structured Streaming
- Event time windows and watermarks
- Online and incremental learning
- Streaming sketches for high cardinality data
03 / FORECASTING AND MARKET SIMULATION
Plan for the range, not the guess.
A single forecast line hides the thing you most need to know: how wrong it might be. We build probabilistic forecasts that give you the whole distribution, and we check that the intervals are honest. A ninety percent interval should contain the truth about ninety percent of the time, and we show you that it does.
For markets and complex systems we use stochastic simulation. Thousands of plausible futures, generated from models fitted to your data, let you stress test a strategy, price an option or size a reserve against the scenarios that would really hurt.
What you get
- Forecasts with calibrated intervals at every horizon
- Scenario engines for stress tests and what if questions
- Backtests against your current method, done properly
- Tail risk you can explain to a board or a regulator
Methods we use
- Stochastic differential equations, jump diffusion
- Monte Carlo with variance reduction, quasi Monte Carlo
- Bayesian hierarchical and state space models
- Regime switching, GARCH family, Hawkes processes
- Quantile and conformal prediction
04 / PREDICTIVE MAINTENANCE AND ANOMALY DETECTION
Fix it before it breaks. Ignore it when it’s fine.
The hard part of anomaly detection isn’t finding anomalies. It’s not drowning your engineers in false ones. We learn what normal looks like for each asset, under each operating condition, and only flag the departures that matter. Each alert says what changed, where and by how much.
For predictive maintenance we estimate remaining useful life with its uncertainty, so you can schedule work by condition and risk rather than by calendar, and keep spares where they’ll actually be needed.
What you get
- Alerts ranked by risk, each with a plain English reason
- Remaining useful life estimates with confidence bounds
- False alarm rates measured and tuned with your engineers
- Feedback loops so every confirmed fault improves the model
Methods we use
- Bayesian online change point detection
- Robust statistics, isolation forests, autoencoders
- Spectral and envelope analysis for vibration
- Survival analysis, Weibull and Cox models
- Multivariate control charts
05 / TIME SERIES AND PHYSICS MODELLING
Models that respect the laws of nature.
Pure machine learning struggles when conditions move beyond its training data, which is exactly when you need it most. Where the governing physics is known, we build it in. Hybrid models use differential equations for the parts we understand and learned components for the parts we don’t.
The result needs less data, extrapolates more safely, and produces parameters your engineers recognise. We also build fast surrogate models of expensive simulations, so a calculation that took hours can run in milliseconds inside a control loop.
What you get
- Models that stay physically plausible in new conditions
- Digital twins calibrated to live data
- Surrogates that replace slow simulations in real time
- Parameters with physical meaning, not just weights
Methods we use
- Physics informed neural networks
- Neural and universal differential equations
- Gaussian processes, Koopman operator methods
- System identification, sparse regression
- Reduced order and surrogate modelling
Tools
We work with your stack, not around it.
We choose tools for the problem and for the team that will own the system after us. These are the ones we reach for most.
- Python
- Rust
- C++
- Julia
- JAX
- PyTorch
- Stan
- PyMC
- NumPyro
- Kafka
- Flink
- Spark
- Arrow
- Polars
- DuckDB
- PostgreSQL
- TimescaleDB
- ClickHouse
- Kubernetes
- Terraform
- AWS
- Azure
- Google Cloud
- On premises
Next step
Not sure which of these you need?
That’s normal. Tell us the decision you’re trying to improve and we’ll tell you honestly where we’d start, even if the answer is somewhere else.