Stop wrestling with fragile pipelines. Inno AI Software gives you a single platform to train, test and ship machine-learning models — all without writing boilerplate infrastructure code. We handle orchestration, versioning and monitoring so your analysts can focus on insight, not plumbing.
Build your first model freeSix core modules, each designed to eliminate a specific bottleneck in the machine-learning lifecycle. Use them individually or chain them together for end-to-end automation.
Connect to databases, cloud buckets, streaming feeds or flat files. Our pipeline detects schema drift, fills missing values and flags anomalies before they reach your model. Supported formats include Parquet, CSV, JSON, Avro and direct JDBC connections to most relational stores.
The platform generates candidate features from raw columns, scores them with mutual-information and permutation-importance tests, then prunes redundant signals. You review a ranked list and approve — no manual Pandas wrangling required.
Every training run is versioned automatically — hyperparameters, metrics, artefacts and even the exact dataset snapshot. Compare runs side-by-side with interactive charts and roll back to any previous state in one click.
Deploy models as low-latency REST or gRPC endpoints with auto-scaling. The serving layer handles batching, request queuing and graceful version swaps so you can push updates without downtime or client-side changes.
Statistical monitors watch incoming prediction requests for distribution shifts. When feature drift or concept drift exceeds your threshold, the system triggers a Slack or email alert and optionally queues a retraining job.
Every model decision is logged with the input vector, output probability and the model version that produced it. Export audit bundles for regulatory review or plug into your existing compliance tooling via webhooks.
No rip-and-replace. Our connectors work alongside your current data warehouse, orchestrator and BI layer.
Most teams go live within a single sprint. Here is the typical path from first conversation to a model serving real traffic.
We review your existing data sources, business objectives and technical constraints. The output is a scoping document that maps each objective to a concrete model type — classification, regression, ranking or anomaly detection — along with estimated accuracy baselines.
Your team gets a private workspace pre-loaded with connectors to your data. We configure the feature store, set up access controls and import any historical labels you already have. Analysts can start exploring immediately through our notebook interface or the visual query builder.
Run experiments against multiple algorithms in parallel. The platform logs every parameter combination, surfaces the best performer and generates a model card documenting accuracy, fairness metrics and known limitations. Stakeholders review results via a shareable dashboard.
Promote the winning model to a production endpoint with a single click. We configure alerting thresholds, set up a retraining schedule and train your team on the monitoring dashboard. From here on, the platform runs autonomously — you only intervene when the business question changes.
Answers to the things prospective customers ask most often during evaluation.
Not necessarily. The visual interface is designed for analysts who are comfortable with spreadsheets and basic SQL. That said, experienced data scientists benefit too — the platform eliminates infrastructure chores so they can spend more time on feature engineering and model design rather than DevOps.
Data never leaves your cloud account. The platform runs inside your VPC (AWS, Azure or GCP) and connects to your existing storage. We do not copy, cache or exfiltrate any customer data. All processing happens in-region, and encryption at rest and in transit is enforced by default.
We charge a flat monthly platform fee based on the number of active model endpoints, plus a small per-prediction cost for inference at scale. There are no charges for data ingestion, storage or experimentation — only production usage counts. Volume discounts apply above 50 million predictions per month.
Most customers have a sandbox running within two business days. End-to-end deployment — including data connection, model training and production serving — usually takes two to four weeks depending on data complexity and internal approval processes.
Yes. Every trained model can be exported as an ONNX, PMML or native framework artefact (PyTorch, TensorFlow, scikit-learn). You own the weights and the training code — there is no vendor lock-in on the model itself.
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95 Harber Hill, Nether Boyleford, Northern Ireland, YD67 2HT, United Kingdom
