A unified Python library that takes a time-series model end to end, from research and AutoML through deployment and live incremental learning, in one codebase that handles batch and streaming data.
Most enterprise time-series ML stacks are stitched together from general-purpose components. The result is fragile, expensive, and slow. DeltaCore replaces that stack with one library. Narrower in scope, materially faster to deploy, and cheaper and more robust to run.
Go from idea to a working pipeline in an afternoon. Build by hand in code or visually with drag-and-drop, with multivariate forecasting and anomaly detection native rather than bolted on.
Hand off a deployable artifact, not a notebook. Export to YAML and deploy anywhere, with model changes you can review in a plain git diff.
Models that look after themselves in production. The same pipeline runs live, adapts to drift, and plugs into the stack you already run.
Watch streaming data against a learned model of normal behaviour. Flag deviations the moment they occur. Forecast forward with quantified uncertainty. One pipeline. Two modes.
Not feature parity. These are combinations no other vendor matches, designed in from the start rather than bolted on later.
AutoML that searches the pipeline architecture itself, choosing the models and steps, then tuning their hyperparameters together.
Data contracts validate every pipeline at build time, catching type and schema mismatches before any data flows.
Models learn incrementally as new data streams in, adapting to concept drift online without a full retrain.
Train on historical batches and serve on a live stream from the same pipeline definition, with no rewrite.
Multi-objective search tunes the whole pipeline end to end for the business metric you care about, like revenue, uptime, or yield.
Runs on-premise with fully offline licensing and a tamper-evident audit log. No telemetry, no call-home.
One import, one pipeline. Forecasting, anomaly detection, backtesting, and deployment, built from composable pieces that mix transforms, models, and validators. Optimise, train, and deploy from the drag-and-drop GUI, or work directly with the library in your IDE.
The same library covers six verticals. The underlying time-series problem is the same one: forecasting, anomaly detection, signal extraction.
Price forecasting, regime detection, latency/anomaly detection and trading signal extraction. CPU-first runtimes that meet air-gap requirements.
Fraud detection and churn modelling. Streaming anomaly detection on transaction flows. Air-gapped, on-premise, audit-ready.
National-scale anomaly detection and forecasting on water and electricity systems. Edge-capable deployment that runs where the sensors live, surfacing failures in minutes rather than at the next review cycle.
Predictive maintenance on heavy industrial hardware. Drift-aware models that adapt to changing operating conditions without manual retraining cycles.
Massive-scale inventory and retention forecasting. Multivariate models that handle seasonality, promotions, and exogenous signals without bespoke pipelines per SKU.
Deterioration alerts and admission forecasting. Explainable per-prediction attributions and audit traces aligned with regulated procurement requirements.
DeltaCore ships as a licensed library. Most customers integrate it themselves, and we license, support, and stay in their corner. Where the lift is heavier, our engineering team handles end-to-end implementation alongside your stakeholders.
Your data science team integrates DeltaCore directly. We license the library and provide architecture guidance, training, and SLA-backed support.
Use-case scoping, data audit, and feasibility. Pilot pipeline defined and success criteria agreed.
License activated. Your team trained on the library. Reference pipelines provided for your data shape.
Ongoing licensing, updates, model maintenance, retraining, and SLA-backed support. Roadmap input as a partner.
Our engineering team handles end-to-end implementation (discovery, build, deployment) alongside your stakeholders, with hand-off to your team when ready.
One-week scoped data audit and bottleneck identification. Use cases prioritised and success metrics agreed.
DeltaCore deployed into your environment, whether air-gapped, on-premise, or hybrid. First production pipelines live in 1–2 months.
Pipelines monitored, retrained, and extended. Additional use cases added as the platform proves itself against agreed metrics.
A senior team of researchers and engineers with roots in Stellenbosch University's ML research community. Production deployments across the Europe, Asia, and Africa.
Tell us a little about your team and what you're looking to build. We typically reply within one business day.
Unit 35, M5 Freeway Park
Uppercamp Rd, Maitland
Cape Town, 7405