SheJane — A general-purpose AI agent

ColdFlameUs LLC is an early-stage company building the data and machine-learning infrastructure behind smarter travel decisions. We were founded by engineers who believe that flight pricing — long opaque, volatile, and frustrating for travelers — is fundamentally a data problem.

Our team applies modern machine learning, time-series forecasting, and large-scale data engineering to turn billions of fare signals into clear, actionable recommendations. Every product decision we make is data-driven, every model we ship is measured against real bookings, and every line of infrastructure is designed to scale with the global travel market.

Two products, one mission — turning real-time data and modern AI into tools that quietly make everyday decisions easier.

ColdFlameUs runs on AWS end-to-end. We rely on managed cloud services to ingest high-volume travel data, train and serve machine-learning models, and deliver low-latency predictions to users worldwide. AWS lets a small, focused team operate infrastructure that would otherwise require a much larger ops organization.

• Amazon S3

  Durable storage for raw fare feeds, curated training datasets, model artifacts, and feature snapshots used across the platform.

• AWS Lambda

  Event-driven ingestion of fare updates, fare-change triggers, lightweight transformations, and serverless API endpoints powering the product.

• Amazon SageMaker

  Model training, hyperparameter tuning, experiment tracking, and hosted real-time inference endpoints for price prediction at scale.

Surrounding these core services, we operate streaming data pipelines, a feature store for ML training and serving, automated model evaluation, and end-to-end observability — all hosted on AWS so we can scale capacity up and down with demand.

Travel pricing is a high-velocity, high-volume problem. To predict it accurately we process large datasets in real time and run continuous model training and inference workloads — workloads that depend heavily on elastic AWS infrastructure.

Routes monitored

10,000+

Daily fare events

50M+

Inference target

<100ms

AWS regions

Multi-region

• Real-time data processing

  We continuously ingest fare updates, schedule changes, and market signals across thousands of global routes and hundreds of carriers — normalized and routed into our feature store within seconds.

• Large-scale datasets

  Our historical pricing corpus already spans hundreds of millions of fare observations and grows daily. Datasets are partitioned in S3 and queried by training and analytics workloads in parallel.

• Model training & inference

  We train forecasting and classification models in Amazon SageMaker, run hyperparameter sweeps, and serve real-time inference behind low-latency endpoints with autoscaling for peak traffic.

• Expected growth & scalability

  Over the next 12–24 months we expect dataset volume, query throughput, and inference traffic to grow by an order of magnitude as we expand coverage and onboard partners.

Key engineering challenges we are actively solving include sub-second prediction serving, cost-efficient GPU and CPU training cycles, multi-region data residency, and keeping model accuracy high as market conditions shift. AWS — particularly S3, Lambda, and SageMaker — is core to how we meet these challenges as we scale.

We are building the intelligence layer for global travel: a platform that turns real-time market data into clear, trustworthy guidance for every traveler. In the near term, that means flight price prediction and deal detection. Over time, it means a unified optimization layer across flights, accommodation, ground transport, and itineraries.

Our long-term goal is simple: every traveler, anywhere in the world, should be able to make smarter, cheaper, and faster decisions — powered by AI they can trust.