Renewable energy production changes constantly, and grid operators need faster ways to understand what is happening across load, generation, storage, and market conditions.
Gridora explores a SaaS dashboard experience built for energy forecasting and balancing.
The dashboard surfaces forecasted peak load, curtailment risk, available storage, spot market price, generation sources, and recommended next actions in one clear workspace.
Natural imagery, soft neutral surfaces, and green accents connect the interface to the renewable energy context while keeping the product practical and easy to scan.
What if anyone on your team could ask your data a question and get a live dashboard back in under a minute?
That was the brief. Business users were locked out of their own data. Every question waited on someone who could write SQL, data sat across disconnected systems, and security teams refused to send sensitive records to third-party AI tools.
So we flipped the model. Instead of moving enterprise data to an AI product, we moved the AI analytics product into the customer's AWS account.
What we built:
⢠Natural-language querying that turns plain-English questions into governed dashboards in under 60 seconds
⢠Federated queries across SQL and NoSQL sources through Trino
⢠AI query generation on AWS Bedrock Agents, with Bedrock Guardrails keeping model output in bounds
⢠Dashboards generated with Apache Superset, plus proactive anomaly and trend alerts
⢠A white-label React widget that embeds in any product
⢠Role-based access, row-level security and enterprise SSO enforced at every layer
The result:
Zero data egress. The whole platform deploys inside the customer's VPC and is live on AWS Marketplace.
Building AI features for a data-heavy or regulated product? Let's talk about doing it without your data leaving your cloud.