AI MVP Development for Energy & ClimateTech

Energy and climatetech is not a vertical you can approach with a generic "chat-with-your-data" wrapper. A working product has to live inside the grid's rulebook: FERC Order 2222 opening wholesale markets to aggregated DERs, NERC CIP controls on anything that touches the bulk power system, IEEE 1547 and IEEE 2030.5 (SEP2) for how distributed resources interconnect and communicate, and the Inflation Reduction Act credit stack (ITC/PTC, 45Q for carbon capture, 45V for clean hydrogen) that dictates which projects even pencil out. We build AI MVPs for founders and utilities who already understand this world and need software that respects it from day one, not a demo that quietly ignores the ISO market and the compliance surface underneath it.

The highest-leverage first product in this space is almost always a forecasting engine, because everything downstream — dispatch, bidding, procurement — is priced off it. We build net-load and renewable generation forecasters that fuse historical SCADA telemetry with numerical weather prediction feeds (NOAA HRRR/GFS, Solcast or DTN irradiance and wind data) and calendar/outage signals, using probabilistic time-series models that emit P10/P50/P90 bands rather than a single fragile point estimate. That distribution matters: it is what lets a battery operator or a trading desk size positions against day-ahead and real-time LMP volatility. This is the same constraint-under-uncertainty optimization discipline behind our ai-logistics-optimizer build, retargeted from routes and load boards to megawatts, weather, and locational marginal prices.

Once forecasts exist, the money is in the dispatch decision. We build battery and DER dispatch optimizers that solve for energy arbitrage across CAISO, ERCOT, PJM or MISO price curves while co-optimizing for ancillary services, demand-charge avoidance, and state-of-charge and cycle-life constraints from the BMS. On the grid-services side that means speaking OpenADR 2.0b for automated demand response and IEEE 2030.5 for utility signaling, and structuring the aggregation logic so a virtual power plant or DERMS can enroll assets without violating FERC 2222 telemetry and settlement requirements. Like our logistics optimizer, the hard part is not the solver — it is encoding real operating constraints so the recommendation is one an operator can actually execute and defend.

Carbon and ESG is the other half of climatetech, and it is mostly a messy-data problem that LLMs are genuinely good at. We build carbon accounting and MRV copilots that ingest utility bills, fuel invoices, and supplier data, then use retrieval-augmented extraction to map line items to GHG Protocol Scope 1/2/3 categories and emission factors, with every figure traced back to its source document for audit. That auditability is non-negotiable now that CSRD/ESRS, California SB 253/261, and the SEC climate rules put these numbers in front of assurance providers. For nature-based and removal credits we layer remote-sensing MRV — Sentinel-2 and commercial imagery with change-detection models — against Verra VCS and Gold Standard methodologies. This is the enterprise-copilot pattern from our case-study-enterprise-analytics-copilot work, hardened for reporting that has to survive a third-party audit.

None of this ships without solving the boring, decisive integration layer. Operational data in energy lives in SCADA and historians over DNP3, Modbus, and IEC 61850, inverter fleets expose SunSpec Modbus registers, meters arrive as Green Button XML or through UtilityAPI/Arcadia, and EV charging runs on OCPP and OCPP/OCPI with ISO 15118 plug-and-charge. We build the adapters, normalize the telemetry into clean time-series stores, and put monitoring and anomaly views on top — the same real-time telemetry-dashboard craft behind our case-study-analytics-dashboard-mvp, applied to feeders, sites, and asset fleets. Where systems touch regulated grid infrastructure we design to the NERC CIP posture (segmentation, no unvetted egress from the OT boundary) instead of retrofitting security after launch.

Computer vision is the fourth pillar, and it turns physical asset inspection into a data pipeline. We build defect-detection models on drone and handheld imagery of PV arrays — thermal hotspots, electroluminescence cracks, soiling and string outages — and on wind-turbine blade imagery for leading-edge erosion and lightning damage, feeding a prioritized work-order queue instead of a folder of photos. The same approach extends to methane and flare monitoring from fixed cameras or aerial passes, which ties directly into OGMP 2.0 and EPA Subpart W reporting. Paired with vibration and SCADA anomaly detection, this gives operators a predictive-maintenance MVP that pays for itself in avoided downtime rather than another dashboard nobody opens.

We keep the SpeedMVPs model deliberately narrow: pick one wedge — a forecaster, a dispatch optimizer, an MRV copilot — and get it into real operators' hands in 2-3 weeks with 100% code ownership, no black-box lock-in. Our 15+ engineers have shipped 18+ AI MVPs across data-heavy, regulated domains, so the forecasting, optimization, retrieval, and telemetry-integration muscles transfer directly here. You leave with the trained models, the pipelines, and the infrastructure in your own accounts — which matters when your next round of diligence, or your ISO's, asks exactly how the numbers are produced.