Custom Machine Learning & Predictive Analytics
Your Data Is Your Unfair Advantage. Use It.
Why It Matters: Off-the-shelf software gives you off-the-shelf results. Your competitors use the same tools. Custom machine learning models built from your unique business data create intellectual property that only you own. Your data is an asset you're probably wasting. Standard BI dashboards show you what happened yesterday. Predictive analytics shows you what's likely to happen tomorrow. Most AI projects die from bad data, fuzzy objectives, un-shippable code, runaway cloud bills, or lawyers waving new rules.
What We Do:
- Data Reality Check & Quality Engineering: AI runs on data; most firms' data is a dumpster fire. Surveys put "data readiness" as the #1 blocker. We inventory every meaningful table, log, and blob store; kill the noise. Health-scan quality, freshness, lineage. Gartner pegs the average annual cost of poor data quality at $12.9M per company.
- Proof-of-Value Sprint: Boards want results in quarters, not years. 65% of companies now pilot GenAI but can't prove ROI. One business case, one model, four weeks. Demo on live data, KPI delta calculated.
- Model Development: We select and fine-tune the right algorithms—think of these as different types of pattern-recognition engines—to solve your specific problem with maximum accuracy
- MLOps & Production Rescue: Only a third of models ever leave the lab. We harden code, add tests, CI/CD, feature store, monitoring. Roll to prod behind dark-launch flags; rollback plan included. The MLOps market is growing 38% annually toward $79B because keeping models running is the #1 enterprise blocker.
- Cost Detox & FinOps: GenAI inflates cloud bills by ~30%; 72% of CIOs say costs are "unmanageable". We profile token, GPU, and storage burn. Swap oversized models, implement cache/quantization.
- Vendor-Stack Vetting: Tool sprawl kills momentum and budgets. We score AWS, Azure, GCP, Snowflake, Databricks, plus open-source. Model TCO over 3 years—including exit costs.
Example Use Cases:
- Fraud Detection & Risk Analytics: Block crooks, not customers. US fraud losses hit $12.5B in 2024. Real-time scoring systems that decide in under 50 milliseconds whether a transaction is legitimate. Example: Ticket marketplace recovered $3M in 3 months after switching to adaptive AI checkout.
- Personalization & Recommendation Engines: Show each user what they'll buy next. Personalized experiences lift conversion 10-15% and revenue 6-10%. Example: Fashion e-commerce saw 8% average order value bump after 30-day pilot.
- Predictive Maintenance & Quality Control: Know when machines will break before they do. Analyze sensor data, maintenance logs, and environmental factors to predict equipment failures weeks in advance. Example: Manufacturer reduced stock-count errors 70% after implementing hard quality gates.
- Customer Churn & Lifetime Value Prediction: Identify your best customers and save the ones about to leave. Models that score every customer on their likelihood to churn and their predicted lifetime value. Example: Telecommunications company cut monthly churn by 2% by identifying at-risk subscribers.