Good to know

Turbit is a Software as a Service Product that is based on a per module and per MW monitored pricing. Ask our experts to get a tailored offer.

Turbit's AI runs on your SCADA data, learns each turbine's individual normal behaviour, and flags anomalies — power curve drift, bearing temperature drift, generator faults, gearbox issues — months before they become failures. Operators reduce unplanned downtime by ~60% in the first year, recover lost production, and turn surprise repairs into planned maintenance. Customers across 3,500+ turbines and 40+ portfolios use it today.

Most operators see payback within 12–18 months. Two compounding sources: (1) avoided component failures and unplanned downtime — a single prevented main-bearing exchange can pay for the year; (2) better insurance terms when Turbit Blue is included, since the insurer counts the AI monitoring as risk reduction. Use our ROI calculators (password-gated; ask your Turbit contact) to model your fleet specifically.

Yes — pricing scales per turbine. Smaller operators typically start with one or two parks, see results within a quarter, and expand. There's no minimum portfolio size and no setup fee beyond the data check.

Yes — through a backtest. We run Turbit's AI on 3+ years of your historical SCADA data (5+ ideal) for as many turbines as you bring, on a one-time fee with no commitment. You see exactly when Turbit would have flagged the failures you actually had, what the alarm would have said, and which actions would have been justified. It's the cleanest way to validate the system on your own assets before signing a monitoring contract.

We train an individual neural network per turbine on your historical data, replay the time window, and show every detection event the system would have produced — severity, root-cause prediction, the date it would have triggered. We then walk through the events with your team: which were already known, which were missed, and what an O&M team could have done differently. The output is a decision document, not a marketing demo.

3 years of SCADA history per turbine is the minimum; 5+ years is ideal. We accept exports from all major OEMs (Vestas, Siemens Gamesa, Nordex, Enercon, GE) and from AMS platforms like Bazefield and Greenbyte. If your data has gaps, we'll tell you up front whether the result will still be conclusive.

Two weeks from data hand-over: ~1 week of data prep + per-turbine training, then a walkthrough call with our team. Send fleet data and the incidents you want to see flagged; we come back with a fixed price and a target evaluation date.

Turbit trains an individual neural network per turbine on its historical SCADA data — Wind speed, temperatures, power, direction. The model learns each machine's normal behaviour, then flags deviations in real time. A second AI layer classifies each anomaly by likely root cause and predicts its relevance. Customer feedback retrains the models, so detection improves with every confirmed alarm. Read the deep dive on our AI Infrastructure page.

More than 35 distinct failure modes — main bearing damage, generator winding issues, gearbox lubrication problems, frequency converter faults, blade-pitch misalignment, yaw misalignment, soiling-driven power curve drift, control-system curtailment, clogged oil filters, broken cooling fans, and more. Coverage depends on your data quality; we run a free data check before any engagement to set expectations.

About 5 alerts per 100 turbines per week on a typical fleet, with a false-positive rate under 10%. A team can review 300 turbines in roughly 30 minutes per week. Detection latency is under 4 hours from when the anomaly occurs.

Three things. First, individual neural networks per turbine — not fixed thresholds or fleet-average models — so we catch deviations that average-based systems miss. Second, AI-driven root-cause prediction and relevance scoring on every alarm, not just a flag. Third, the only platform on the market with integrated insurance via Turbit Blue (HDI Global) — your monitoring and risk-coverage stack on the same data layer.

Training Turbit's neural networks takes under a day per turbine. Once data flow and signal-mapping checks are complete, the entire fleet is online within a week.

Ideally 24 months of SCADA per turbine. With less, transfer learning lets us start from one month — slightly lower coverage initially, refined as more data arrives. The data-requirements page lists the exact signals each module needs.

No — Turbit is software-only and works on the SCADA data your turbines already produce. We can optionally ingest CMS / blade-vibration data for richer coverage, but no extra hardware is required to start.

No — the EventCard view is built for O&M teams, not data scientists. Every alarm is presented with a plain-English description, the relevant plot, the AI's root-cause prediction, and recommended next steps. We also run a kickoff workshop and Customer Success calls so your team has direct access to a Turbit engineer.

Mapping is critical — wrong signals mean wrong models. Turbit uses statistical analytics and language models to score each signal's likely identity, plus manual checks where confidence is low. Mappings often change over time at the asset; Turbit detects the resulting anomalies automatically and flags frozen or misnamed sensors.

Using AI and machine-learning models to watch each turbine's actual operating data in real time, learn its normal patterns, and predict component failures before they happen. The result is fewer surprise breakdowns, better-timed maintenance, and longer asset life.

Standard SCADA — power, wind speed, temperatures, pressures, status codes — is the baseline. Vibration / CMS data, blade-sensor data, weather data, and maintenance history all sharpen the picture. Turbit works with whatever you have, with coverage scaled to data quality.

Curtailment is a known challenge — a model that hasn't seen reduced operation can confuse it with a fault. Turbit's per-turbine models learn from your turbines' actual curtailment patterns and treat verified curtailment events as valid (not anomalies). For long offline periods, models pause and resume training with the new operating window.

FSAs cap liability — historically at numbers that haven't kept pace with turbine sizes. A 7 MW turbine's 12-month outage now exceeds typical liability caps several times over. Turbit's monitoring catches issues months before they hit the FSA's intervention threshold, lets your team verify the OEM's response, and (with Turbit Blue) closes the residual liability gap. The math typically holds even alongside an existing FSA.

Both. The same model that flags developing failures also surfaces underperformance — power-curve drift, yaw misalignment, pitch errors, soiling — that costs you production. We don't issue control commands ourselves; we hand the operator a quantified opportunity to act on with the OEM or service provider.

This is the core advantage of per-turbine modelling. Each machine has its own normal-behaviour fingerprint — its history, site, components, control settings. Turbit's neural networks learn that fingerprint, so deviations stand out cleanly against an honest baseline rather than against an industry average that doesn't fit.

Two mechanisms. First, the relevance-prediction layer pre-filters — alarms below the priority threshold don't reach your inbox. Second, your feedback retrains the models: every confirmed-or-rejected alarm tunes sensitivity for similar situations. False-positive rate stays under 10% across our fleet.

Yes — most turbines built after 2005 have enough SCADA signals for component-level monitoring. Newer turbines with richer sensor packages produce sharper models, but temperature, pressure, and power signals alone reveal most developing problems.

Component-dependent. Main bearings: trend visible 1+ year before action is needed (see the VSB customer story). Generator winding issues: 3–6 months. Gearbox lubrication problems: weeks to months. Blade damage with CMS data: weeks. The longer the lead time, the cheaper the fix.

Different jobs. Weather forecasting maximises production from healthy turbines. Predictive maintenance keeps the turbines healthy. Both matter — high wind isn't useful if a gearbox fails in the middle of the season.

Three steps over ~2 weeks: (1) data check + signal mapping (we work with your IT or AMS provider; minimal effort from your team), (2) per-turbine model training and dry-run, (3) go-live workshop with your O&M team. After onboarding the typical commitment is 30 minutes per week per 100 turbines — review the alerts, mark relevance, schedule actions. Customer Success is on a weekly or bi-weekly call until your team is independent.

Live alarms in the Turbit web app, with EventCards (plot + plain-English narrative + root-cause prediction + recommended action). Email notifications for high-priority events. Monthly portfolio reports for asset managers. Per-turbine deep-dive reports on demand. API access for integration with your reporting stack. All outputs include the underlying data so your engineering team can verify findings.

Detection latency is under 4 hours from the data point that triggers it. Notifications go out via email by default; configurable per user, per severity, and per turbine. Alerts surface in the Turbit web portal in real time and are also exposed via API for integration with existing ticketing systems.

Yes — Vestas, Siemens Gamesa, Nordex, Enercon, GE, Senvion, Goldwind and others. Turbit only needs SCADA data, which is standardized across the industry. We've deployed across mixed-OEM portfolios; the same Turbit account handles them in one view.

No — Turbit is read-only on SCADA data and never issues control commands. There's no warranty implication. In practice, OEMs welcome it: clear evidence of a developing issue lets them schedule maintenance instead of getting paged for emergency repair. We've worked alongside Vestas, Siemens Gamesa, Nordex, Enercon, GE and SAB without a single warranty concern.

Each turbine gets its own neural network trained on its own data — OEM-agnostic. Turbit's portfolio view shows mixed fleets in a single dashboard. The signal-mapping layer handles OEM-specific tag naming so your team doesn't see the difference.

We pull data from any major AMS — Bazefield, Greenbyte, Bax Energy, Bazefield, WIS, Rotorsoft, WEO, and others — via standard interfaces (OPC, IEC 61400-25, REST, file-based exports). For direct turbine connections we support OPC DA/UA and IEC 61850/60870. Custom protocols are usually a few days of work.

An insurance integration product that uses Turbit's AI monitoring as the risk-reduction layer. It closes the gap between your full-service-agreement liability cap and the actual cost of a 12-month component outage on modern turbines — a gap that's grown from ~30k EUR per turbine in 1995 to over 1.3M EUR for 7 MW assets. Available with Turbit Monitoring; up to 30% lower total cost vs. the typical alternative.

FSAs cap liability — usually around 100–115% of the annual service fee. Modern turbines' outage costs blow past that cap on a single major component failure. Turbit Blue covers the residual exposure: the difference between what the FSA pays out and what a multi-month component outage actually costs you. Two product variants — AI Add-On (gap insurance on top of an FSA) and AI Full Coverage (alternative FSA + insurance bundle).

HDI Global SE underwrites turbines that have Turbit Blue. Turbit isn't a broker and doesn't sell insurance ourselves; we provide the AI monitoring layer that makes the insurance economically viable. HDI is a globally rated insurer with extensive renewable-energy underwriting experience.

Monitoring without insurance — yes, that's our most common deployment. Insurance without monitoring — no; the AI layer is what HDI counts as risk reduction in the underwriting, and it's how the math works out. Start with monitoring; add Turbit Blue when your FSA renewal is on the horizon.

Often, yes — even without Turbit Blue. Insurers increasingly offer better terms to operators with proven predictive monitoring; the demonstrated risk reduction lowers the risk and thus can increase coverage or lower the premium. Some operators find that the insurance savings alone offset their monitoring system cost and beyond that can save up to 30% of OPEX.

All data lives in EU-based ISO 27001-certified data centers (no hyperscaler lock-in; bare-metal hosting). Encrypted in transit (TLS) and at rest. Customer data is logically isolated per tenant and never combined for cross-customer model training without explicit opt-in. Full security overview is on our /compliance page.

Turbit is in the active ISO/IEC 27001 certification process, expected to complete in Q1 2026. Internal policies, controls, and audits are already in place to meet the standard. Most of our customers are themselves classified as critical infrastructure — we work to their requirements every day.

GDPR: we process operational and SCADA data, not personal data; customer business contacts are stored only as needed for support. Your data residency is EU-only. EU AI Act: Turbit Monitoring is classified minimal/limited risk (no autonomous safety-critical control), with transparency, explainability, and human oversight built into every detection. DPAs and security overviews are available on request.

Turbit is owned by its founders Michael Tegtmeier (CEO) and Christian Fontius (CRO), with backing from Vinci Venture Capital and known Business Angels. Independent — no operator, OEM or insurer holds a controlling stake.