AI Autopilot isn’t a separate product you buy — it’s the intelligence inside GravOS Charge, Electron and Edge. It observes how your sites actually behave, proves itself against your team’s decisions, and then takes over the work you’d rather not do by hand.
| Charger load balancing | Manage | 97% |
| Peak-demand shaving | Manage | 95% |
| Fleet departure sequencing | Manage | 93% |
| Battery arbitrage | Manage | 89% |
| Market & DR bidding | Recommend | 82% |
| V2G export windows | Recommend | 76% |
| Session pricing changes | Observe | 61% |
One console for how much autonomy you’ve granted, per decision type — and the confidence Autopilot has earned for each.
The same intelligence runs inside every GravOS product — which means it sees your chargers, your batteries and your sites as one operation instead of three disconnected tools.
Intelligence compounds when it isn’t siloed. What Autopilot learns in one product immediately improves the others.
Autopilot doesn’t arrive claiming to know your operation. It watches, proposes, gets scored against reality, and only takes the wheel once it has proven itself — typically within 90–120 days of operational data.
Connects to your assets and builds a baseline: load shapes, session patterns, tariffs, asset behaviour, how your team actually operates. It takes no action.
You get: visibility, benchmarks and a measured baseline to judge everything else against.Autopilot forecasts and decides in the background, then compares its choices to what actually happened and what your operators did. Every miss is a training signal.
You get: an accuracy record — evidence, not promises.Once accuracy clears your threshold per decision type, Autopilot starts recommending — with the reasoning and expected value attached. Your team approves or rejects.
You get: fewer decisions to make from scratch, and a record of what you accepted.You promote the decision types you trust to Manage. Autopilot executes continuously inside your guardrails and escalates only genuine exceptions.
You get: autonomous operations — your team sets policy instead of clicking buttons.Timelines vary with data density, seasonality and site complexity. A high-throughput charging network reaches confidence faster than a site with a handful of monthly peak events.
Autonomy isn’t all-or-nothing, and it isn’t permanent. You choose the mode per decision type, and you can change it at any time.
Run load balancing autonomously while market bidding still needs a human. One operation, different levels of trust.
Promote your best-understood sites to Manage first, and keep a new or unusual site in Recommend until it settles.
Autonomous below a value or risk threshold, human approval above it. You define where the line sits.
Drop any decision type back to Recommend or Observe instantly. Autonomy is a setting, not a commitment.
An alert after a charger failed, a peak after it set your bill, a missed event after the window closed. Autopilot is built to act before those become facts.
Drifting connector resistance, repeated handshake errors, comms degradation — surfaced days before the hardware actually fails a customer.
Sees the peak forming hours ahead and charges storage in advance, instead of reacting once the threshold is already breached.
When a demand-response event looks likely, it holds capacity back — trading small certain value for larger expected value.
Detects that a vehicle won’t reach target charge in time and re-sequences the depot before anyone notices a problem.
Utilization drifting at one site, a battery aging faster than its peers — patterns too slow for a human to catch in a dashboard.
When something genuinely needs a decision, it arrives with context and a recommendation — not a raw alarm at 3am.
Handing operations to software is a serious decision. Autopilot is built so that decision stays reversible, auditable and bounded.
Intelligence and authority are deliberately separated. Autopilot decides what should happen; deterministic, safety-coded logic decides what is allowed to happen.
Autopilot has to earn every increment of autonomy — and you can take it back in one click.
No. Autopilot is useful from day one in Observe and Recommend modes — forecasting, benchmarking and proposing actions. Manage mode is optional, granted per decision type, and reversible at any time.
That's typically how long it takes to see enough operational variety — weekday and weekend patterns, weather swings, tariff periods, real peak events — for confidence to be meaningful rather than a small-sample illusion. Data-dense operations get there faster; sites with rare events take longer.
You get the platform value immediately: unified visibility, rule-based automation, alerts and reporting. Autopilot's learning runs alongside that from the first day of data.
Edge runs models locally, so operational control doesn't depend on sending data anywhere. Cloud learning is used for cross-site improvement and can be scoped to your tenant.
Every decision is logged with its reasoning and expected value, and measured against the baseline established in the first phase. Accept rate, forecast accuracy and realized value are all reported.
It can't breach a hard constraint — those are enforced outside the model. For anything softer, exceptions are logged and escalated, thresholds can be tightened, and the decision type can be dropped back to Recommend immediately.
It's the intelligence layer inside GravOS Charge, Electron and Edge, not a separate SKU you bolt on. Talk to us about how it's packaged for your deployment.
Connect your assets, let Autopilot learn your operation, and hand over only what it earns. No leap of faith required.