Why GenericML
Most AI/ML projects fail not because of algorithms — but because iteration is slow, integration is brittle, humans are out of the loop, and trust/governance arrive too late.
The 5 failure modes (and the fixes)
Human Out‑of‑the‑Loop
Business experts can’t iterate or test models.
Fix: HITL review/approval loops baked in.
Python‑Stack Friction
MLOps + container pipelines are slow and brittle.
Fix: C# micro‑models in real apps (no handoff).
Overfitting & Leakage
Poor domain boundaries + weak context inflate offline metrics.
Fix: DDD‑aligned vectors + lineage + leak checks.
Explainability & Trust Gaps
Stakeholders can’t validate predictions.
Fix: evidence bundles + decision logs + provenance graph.
Cost Spiral
Infra/ops costs outweigh business value.
Fix: CPU‑first, fast retraining, many more experiments.
The shift
From “notebook science” → to repeatable model generation inside product code.
Result: models actually ship and get used.
Reality check: time + cost
| Approach | Avg. time to production | Typical cost range | Common delays |
|---|---|---|---|
| Classical ML (Python / R / MLOps) | 6–12 months | $250k–$2M | Multi‑team handoffs, infra setup, compliance reviews |
| Enterprise AutoML | 3–6 months | $100k–$500k | Retraining friction, transparency, compute cost |
| GenAI (LLM + RAG) | 2–5 months MVP, 6–12 months prod | $500k–$5M | Governance, hallucination testing, ops & licensing |
| GenericML (C#, local CPU) | Days → Weeks | <$5k | Domain experts can train/test directly |
Proof point you can use: Time Weeks → Days → Hours, and Cost $500k → <$5k (per micro‑model).
What you actually buy
Repeatable model generation
Auto feature engineering + fast model selection, optimized for iteration.
Type‑safe deployment
REST / gRPC / serverless APIs are first‑class, not an afterthought.
Audit‑ready decisions
Vectors + model packs + ensembles + evidence bundles stored with provenance.