From warehouse to production intelligence
Lake and Lab prepare tables for ML and Forecast. Loop and Resolve start with no dataset, including ticket routing and urgency scoring.
Data warehouse
Ingest, explore, and publish datasets with Lake and Lab before training models.
Multi-Source Consolidation
Unify CSV exports, Postgres tables, and warehouse snapshots into one bronze catalog — no ETL scripts to maintain.
Read the multi-source consolidation use caseUnstructured Archive Ingest
Drop zip archives of PDFs, images, and documents into bronze storage for downstream structuring and ML pipelines.
Read the unstructured archive ingest use caseGold Feature Publishing
Curate silver views, publish gold datasets, and connect them to ML and Forecast training jobs.
Read the gold feature publishing use caseLineage & Audit Trail
Track bronze → silver → gold promotion with a catalog your compliance team can inspect before models go live.
Read the lineage & audit trail use caseExploratory Profiling
Profile bronze tables with pandas, visualize distributions in Lab, and decide what to promote to silver before modeling.
Read the exploratory profiling use caseAd-Hoc Transforms
Run Python transforms on Lake tables and save results as new silver datasets — without overwriting source tables.
Read the ad-hoc transforms use caseFrom your data
ML and Forecast train on gold tables from Lake.
Ticket Demand
Predict event ticket sales and optimize pricing strategies
Read the ticket demand use caseReal Estate Prices
Predict home prices based on historical sales prices and home details.
Read the real estate prices use caseWeather Predictions
Advanced meteorological forecasting and climate analysis
Read the weather predictions use caseEmployee Attrition
Identify at-risk employees and improve retention rates
Read the employee attrition use caseFire Prediction
Early wildfire detection and risk assessment systems
Read the fire prediction use caseNo dataset
Loop learns from live feedback. Resolve routes a ticket or scores how urgent it is from answers you write.
Feed Curation
Personalize RSS feed articles with adaptive recommendation systems
Read the feed curation use caseTicket Routing
Send each incoming message to the right queue from answers you define. No labeled history.
Read the ticket routing use caseUrgency Scoring
Place a ticket on a scale you write, from can-wait to needs-someone-now.
Read the urgency scoring use caseUse cases FAQ
- What can you build with Heimdall?
- Teams use Heimdall for data warehouse workflows (multi-source consolidation, unstructured archive ingest, gold feature publishing, lineage audit) and applied intelligence (real estate pricing, employee attrition, fire prediction, ticket demand forecasting, weather prediction, personalized feed curation with Loop).
- Who is Heimdall designed for?
- Heimdall is designed for B2B teams — startups, SMBs, and departments inside larger companies — that need production ML and data curation without building a custom data lake or hiring a full MLOps team.
- What is the typical Heimdall workflow?
- Ingest data into Lake bronze → curate silver views and explore in Lab → publish gold datasets → train with ML, Forecast, or Loop, or answer text with Resolve → deploy REST APIs with monitoring. Each step stays inside one platform.
Bring data, or start without it
Publish gold for ML and Forecast, or start with no dataset: Loop for live learning, Resolve for ticket routing and urgency scoring.