Every industry gets rebuilt, function by function.Until none of the original planks remain.

A $40B energy conglomerate needed sub-4% MAPE on ERCOT day-ahead load forecasts to run profitable congestion revenue rights. Legacy vendor models sat at 5-7% error — enough to turn winning bids into losses.

We deployed TS2Vec temporal embeddings paired with hybrid CNN+LSTM ensembles. The architecture treats each node as a state-space model where load, weather, and price interact as hidden variables. 200+ models went live within 90 days, each tuned to a specific congestion point.

The same forecasting engine now extends into industrial demand response and datacenter load intelligence, where sub-hour accuracy drives curtailment timing and capacity planning.

3.21% MAPE on day-ahead forecasts. The spread between forecast accuracy and bid profitability compounds daily.

Healthcare revenue cycle management leaks 3-5% of net revenue through denials, underpayments, and manual claim statusing. We structured a joint venture with a national RCM operator to deploy AI across the full cycle — eligibility verification and prior authorization, claim submission, denial prediction, and appeals.

The model maps 112 discrete processes, scores each for automation readiness, and sequences deployment by revenue impact. Denial prediction alone catches 34% of preventable denials before submission. Claim statusing — the largest labor cost in RCM — runs 41% faster with structured extraction.

JV structure means our upside compounds with theirs. 2x EBITDA at the 3-year mark, 3x terminal value on exit.

Equipment lessors price residual values using depreciation tables and analyst judgment. The spread between predicted and realized residual is where margin lives — or dies. A mid-market lessor with $2.8B under management needed better residual estimation across construction, medical, and transportation fleets.

We deployed state-space models as hidden-variable estimators — treating equipment condition, utilization cycles, and secondary market liquidity as latent states inferred from observable transaction data. The model ingests auction results, OEM lifecycle data, and macroeconomic signals to produce residual distributions, not point estimates.

The same state-space architecture powers our energy price forecasting and healthcare process models — hidden-variable estimation applied to different domains.

Underwriting dropped from 9 days to 40 minutes. The model doesn't replace the credit committee — it arms them with distributions instead of guesses.

A $250M listed paper mill runs recovery boilers, lime kilns, and digesters — each a continuous process where energy consumption drifts with feedstock variability, ambient conditions, and equipment degradation. Traditional process control optimizes each unit locally. The savings live in cross-unit coordination.

We deployed graph neural networks for trim optimization and digital twins for recovery boiler operation. The state-space model treats the entire mill as a coupled system — steam header pressure, black liquor solids concentration, and electrical load interact as latent variables. The optimizer recommends setpoint adjustments every 15 minutes.

This is the same industrial power cost optimization framework we deploy across energy-intensive verticals — the physics changes, the architecture doesn't.

8% energy savings verified at scale. In a $250M mill, energy is the second-largest cost line. The math writes itself.