Most platforms promise intelligence and deliver dashboards. Ours ships models that run in production on day one — trained on your data, governed by your rules, measured by outcomes you actually care about.
Run your first model free
Every item below maps a real pain point we hear in discovery calls to the specific mechanism our AI software uses to resolve it.
Teams export CSVs, paste into spreadsheets and email results around. By the time a decision is made the numbers are stale. Our connectors ingest from over forty sources in real time, so every model trains on a unified, current data layer without manual wrangling.
A single pipeline normalises schemas, handles missing values and versions every snapshot automatically. Data engineers save roughly twelve hours a week on preparation alone, freeing capacity for higher-value feature engineering.
Accuracy drifts as customer behaviour changes, yet most platforms lack automated retraining triggers. Our drift-detection module monitors feature distributions and fires retraining jobs the moment statistical thresholds are breached.
Shadow models train in the background and promote themselves when they beat the incumbent on your chosen metric — precision, recall, cost-weighted F1 or a custom objective. No manual model swaps, no downtime.
Regulated industries need audit trails, explainability reports and bias checks before any model goes live. Our governance layer generates these artefacts automatically, compressing approval cycles from weeks to hours.
Every prediction is logged with feature-level SHAP explanations. Fairness metrics across protected attributes are computed per training run, and policy gates can block deployment if thresholds are not met.
Three layers, one coherent experience. Click a tab to explore each component of our AI software architecture.
Connect databases, warehouses, streaming queues and flat files through a declarative YAML config. The engine auto-detects column types, flags anomalies and creates feature stores that downstream models reference by name rather than query. Incremental loads keep costs predictable even at terabyte scale.
Supported sources include PostgreSQL, BigQuery, Snowflake, Kafka, S3, Azure Blob, REST endpoints and SFTP drops. Custom adapters can be written in Python and registered through a plugin API.
Define an objective, point at a feature store and let the engine search across gradient-boosted trees, neural architectures and linear baselines. Bayesian hyper-parameter optimisation runs in parallel on spot instances, keeping cloud spend low while exploring a wide search space.
Experiment tracking is built in — every run logs parameters, metrics, artefacts and environment hashes so results are reproducible months later without container archaeology.
Promote a model to a versioned REST endpoint with canary traffic splitting. The runtime auto-scales horizontally, serves predictions in under fifty milliseconds at the 99th percentile and rolls back automatically if error rates spike.
Batch scoring is equally straightforward — schedule nightly runs against a warehouse table and write results back with zero glue code. Monitoring dashboards surface latency, throughput and drift metrics in real time.
Our AI software speaks the protocols your infrastructure already uses — no rip-and-replace required.
Each number below comes from a production deployment audited by the client's own analytics team.
Finsbury Capital Group
Reduced false-positive fraud alerts by 34 % within the first quarter of deployment, saving the compliance team an estimated 1,200 review hours and improving customer experience scores by nine points.
Northgate Logistics
Doubled route-optimisation throughput while cutting fuel spend by 18 %. The predictive demand model now processes 4 million parcel-level forecasts nightly, replacing a legacy rules engine that took six hours to run.
Heathbridge NHS Trust
Annual cost avoidance through early sepsis detection. The model flags at-risk patients an average of four hours earlier than the previous protocol, improving intervention windows and reducing ICU admissions.
Straight answers to the things prospective customers ask most often about our AI software.
Most teams have a first model in production within two weeks. The ingestion layer can be configured in a day if your data lives in a supported source, and automated training typically converges within 24 to 48 hours depending on dataset size. Enterprise roll-outs with custom governance gates average six weeks end to end.
No. The platform is designed so that analysts comfortable with SQL can define objectives and launch training runs. That said, experienced ML engineers will find escape hatches for custom feature pipelines, bespoke loss functions and notebook-driven experimentation when they need finer control.
You choose. We offer managed cloud tenancies in AWS eu-west-2 (London) and Azure UK South, both ISO 27001 certified. For organisations that require on-premises hosting, the platform ships as a Helm chart deployable on any Kubernetes cluster with a minimum of three nodes.
Pricing is based on compute hours consumed during training plus a flat per-endpoint fee for serving. There are no per-seat charges, so your entire organisation can access dashboards and results without incremental cost. Volume discounts apply above fifty endpoints.
Absolutely. Every trained model can be exported as an ONNX artefact or a containerised microservice. We believe in zero lock-in — if you outgrow us or want to self-host a specific model, the artefact is yours, including all metadata and lineage records.
Whether you have a well-scoped ML project or just a hunch that your data could work harder, we are happy to explore it with you — no obligation, no slide deck.
Address: 726 Joanne Square, Quigley-Lockmanham, Scotland, NS5 4BQ, United Kingdom
Phone: +44 7476 868014
Email: [email protected]