Generic AI tools give you features designed for the average business. If your competitive advantage depends on proprietary data or domain-specific prediction, you need custom-built ML systems.

What’s included:

• AI use case discovery & feasibility assessment — ROI opportunities ranked by data readiness

• Custom ML model development — classification, regression, NLP, or computer vision

• LLM integration & prompt engineering — GPT-4, Claude, or open-source LLMs

• Predictive analytics dashboard — real-time or batch KPI forecasting

• Data pipeline architecture — ETL/ELT from raw sources to model-ready datasets

• Model deployment & monitoring — drift detection and retraining triggers

Engagement model:

90-min technical discovery → use case doc in 3 days → model development (3–8 weeks) → staged deployment with UAT → knowledge transfer

Who this is for:

SaaS, fintech, healthcare, and e-commerce operators with 12+ months of proprietary data. Engineering teams of 3–100.

Guarantee:

We deliver a written AI use case assessment before any development. If it doesn’t surface at least 2 implementable AI opportunities, you pay nothing.

TechSci Inc., Delaware C-Corp | 8+ yrs | 12 certs | rihan.cloud