Partnered with Fairbuild • Trusted by Meta Reality Labs

Production-ready AI for
Manufacturing & Grid Operations

Risk detection, quality control, and time-series forecasting—delivered as production systems with observability, security, and integration-first APIs.

15+
Years AI/ML Experience
9.70%
Improvement vs reference baseline
4.11%
Day-Ahead MAPE
<50ms
Inference Latency
Request Demo View API Docs

From pilot to production: deploy models with versioning, monitoring, and integration patterns that fit enterprise workflows.

Getting Started
Built on Proven Foundations
Enterprise Security
California-Based
99.9% Uptime SLA
Open Source Core
Partner: Fairbuild
Trusted: Meta Reality Labs
API: CAISO-style patterns
Ops: Monitoring & SLAs
Our Solutions

Three Pillars of Operational Intelligence

End-to-end ML solutions designed for mission-critical applications where accuracy and reliability are non-negotiable.

ML-Based Risk Identification

Proactive anomaly detection and predictive risk assessment powered by state-of-the-art deep learning architectures.

  • Real-time anomaly detection
  • Tail error prediction
  • Explainable AI outputs

Quality Management Systems

Automated quality control and continuous improvement systems for manufacturing and operational excellence.

  • Sub-pixel defect detection
  • Process optimization
  • Real-time dashboards

Time-Series Forecasting

State-of-the-art Mamba architecture delivering 9.7% improvement over industry baselines for energy and demand forecasting.

  • 4.11% MAPE day-ahead
  • Multi-horizon (1h to 48h)
  • Uncertainty quantification
Examples

Simple, concrete outcomes

A quick view of the context, what changes, and the measurable impact.

Grid Operations
CAISO load forecasting with multimodal models

We deliver an OASIS-style forecast API that integrates satellite imagery, weather, and historical patterns to improve accuracy and reduce high-impact errors.

Before
Baseline forecasting
Harder to anticipate large swings and tail events.
After
9.7% better than CAISO
Day-ahead MAPE: 4.11% with <50ms inference latency.
Manufacturing
Risk detection + quality control as a production workflow

We turn noisy test signals into actionable alerts: anomaly detection, triage signals, and dashboards that help teams respond faster and reduce rework.

Before
Manual triage
Engineers spend cycles finding root cause across logs and tests.
After
Automated detection
Prioritized incidents with explainability and auditable outputs.
For Teams

Clarity for every stakeholder

One platform—communicated in the language executives, engineers, and data scientists need.

Business impact you can measure

Reduce downtime, improve yield, and make operations more predictable with AI systems designed for reliability and change management.

  • Faster detection and response for high-severity issues
  • Governance: versioned models, audit trails, and performance reporting
  • Deployment that fits existing workflows and security requirements
Typical deliverables

Business-ready dashboards, SLA-backed services, and operational metrics that track accuracy, drift, and incident outcomes over time.

Integration-first engineering

REST APIs, predictable schemas, and observability hooks—so deployment and maintenance are as clean as model training.

  • OASIS-style parameters and response formats (XML/CSV/JSON)
  • Low-latency inference with versioned models and rollbacks
  • Monitoring: uptime, latency, and quality metrics
Developer resources

Start with `api-docs.html` for endpoint details, then follow the Getting Started steps for auth, environments, and rollout.

Performance, benchmarks, and model lifecycle

Evaluate models with the metrics that matter to your domain, and ship improvements with traceability and safe iteration.

  • Clear metrics: MAPE/RMSE/MAE and tail-error reporting
  • Uncertainty bands and explainability where applicable
  • Drift tracking and model version comparisons over time
What you get

Benchmarks, model cards, and a deployment pipeline that keeps evaluation and production aligned.

Featured Project

California Grid Forecasting

Our Mamba-based state space models deliver state-of-the-art accuracy for California load forecasting in the CAISO market context. The Multimodal Explainable Grid Forecasting System (MEGFS) integrates satellite imagery, weather data, and historical patterns to provide transparent, actionable intelligence for grid operators.

9.7%
Better than reference baseline
17+ GW
BTM solar visibility
60%
Large error reduction
24-Hour Load Forecast (Duck Curve)
Forecast
Actual
Leadership

Experienced Team

Decades of combined experience in enterprise AI, production systems, and manufacturing test solutions for Meta Reality Labs and other industry leaders.

Jisoo Lee

Jisoo Lee, Ph.D.

CEO, Gramm AI • Fairbuild

Former ML ISP Lead at Qualcomm. 24 years in mobile imaging, image sensors, and camera testing. Lead architect of ML-based manufacturing test incidence response systems.

Ph.D. EE Qualcomm Stanford
David Yoon

David Yoon

Chief Strategy Officer

Former Senior Vice President at Samsung. Deep expertise in global manufacturing operations, supply chain strategy, and enterprise technology deployment.

Samsung SVP Strategy
Moondo SC Hong

Moondo SC Hong

Chief Technology Officer

Former CEO at isMedia Co Ltd. Expert in optical metrology, manufacturing test systems, and AR/VR device calibration for production environments.

isMedia CEO Optical Systems
Deborah Lim

Deborah Lim

Finance Lead • Fairbuild LLC

Financial management, compliance, and operations leadership. Expertise in federal/state grant compliance and audit-ready internal controls.

Finance Compliance
DEVELOPER ACCESS

Forecast API

OASIS-style REST API for seamless integration with existing utility workflows. Supports XML and JSON response formats.

Supported CAISO TAC Areas

⚡
PG&E Pacific Gas & Electric Northern & Central California
Active
⚡
SCE Southern California Edison Central & Southern California
Active
⚡
SDG&E San Diego Gas & Electric San Diego Region
Active
⚡
VEA Valley Electric Association Eastern CA / NV Border
Active

Quick Start

GET /forecast/v1/demand 
curl -X GET "https://api.gramm.ai/forecast/v1/demand?\
tac_area=PGE&\
startdatetime=20250101T00:00-0800&\
enddatetime=20250102T00:00-0800&\
market_run_id=DAM" \
-H "Authorization: Bearer YOUR_API_KEY"
Python 
import requests

response = requests.get(
    "https://api.gramm.ai/forecast/v1/demand",
    params={
        "tac_area": "PGE",
        "startdatetime": "20250101T00:00-0800",
        "enddatetime": "20250102T00:00-0800",
        "market_run_id": "DAM"
    },
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)

forecast = response.json()
print(f"Forecast points: {len(forecast['forecast'])}")
JSON Response 
{
  "meta": {
    "tac_area": "PGE",
    "utility": "Pacific Gas & Electric",
    "market_run_id": "DAM",
    "units": "MW",
    "model_version": "megfs-v2.1"
  },
  "forecast": [
    {"interval_start": "20250101T00:00-0800", "value": 18542.3, "lower": 17890.1, "upper": 19194.5},
    {"interval_start": "20250101T01:00-0800", "value": 17891.7, "lower": 17259.4, "upper": 18524.0}
  ]
}

Familiar API Design

Uses parameter conventions familiar to CAISO OASIS users for easy integration:

CAISO OASIS gramm API
queryname=SLD_FCST GET /forecast/v1/demand
market_run_id=DAM market_run_id=DAM
startdatetime startdatetime
resultformat=6 resultformat=6 (CSV)
View Full Documentation Request API Key
Developer Onboarding

Clear integration path

Start quickly with docs, then scale with production practices.

1

Review the API reference

Use the Forecast API docs for endpoints, parameters, and response formats.

2

Request credentials

Get an API key and choose your integration format (XML/CSV/JSON).

3

Ship with observability

Track latency, uptime, and accuracy drift with a rollout plan and versioning.

View Full Documentation Request API Key
We support standard enterprise deployment patterns (cloud, hybrid, and private). We align the integration to your security and networking requirements.
We track performance metrics (e.g., MAPE/RMSE/MAE) and drift signals with versioned models, so you can compare cohorts and roll forward safely.
The API follows parameter conventions and response patterns familiar to CAISO OASIS users, minimizing changes needed for existing workflows.

Ready to Transform Your Operations?

Partner with us to deploy production-grade AI solutions that deliver measurable results.

Schedule a Consultation