Enterprise Architecture
Overview
Enterprise AI architecture layers experience apps, orchestration, model gateway, data/vector plane, and governance—deployed on hybrid cloud with tenant isolation.
Technical Deep Dive
Reference: Gartner AI TRiSM (trust, risk, security management) woven through each layer. API-first; no direct model access from frontends.
Cell architecture for blast-radius containment; service mesh for mTLS between agent services.
Practical Use Case
Retail conglomerate: regional model endpoints (EU/US), central governance catalog, local vector indices per banner brand.
MLOps & LLMOps
Overview
LLMOps extends MLOps with prompt versioning, eval harnesses for text quality, and routing policies—release artifacts are prompts + models + retrieval indices, not weights alone.
Technical Deep Dive
Pipeline: data ingest → index build → prompt PR → offline eval → canary → prod. Track: prompt hash, index version, model ID jointly in traces.
Rollback: revert prompt registry pointer without redeploying apps.
Practical Use Case
Nightly job rebuilds vector index from warehouse; eval gates block deploy if faithfulness score drops >2%.
Governance & Compliance
Overview
AI governance inventories models, classifies risk, enforces policies, and maintains audit evidence for regulators and internal audit.
Technical Deep Dive
Artifacts: model cards, DPIA, bias test reports, incident logs. High-risk use cases (credit, hiring, health) require human oversight by law in EU.
Model registry ties each production endpoint to owner, training data summary, eval results.
Practical Use Case
EU bank maintains AI register for supervisors; every customer-facing agent mapped to risk tier with mandatory annual review.
Security
Overview
Enterprise AI security covers prompt injection, tool abuse, data leakage via RAG, model theft, and supply-chain risks in third-party APIs.
Technical Deep Dive
Controls: VPC endpoints for models, secrets in vault (never in prompts), output DLP scanning, adversarial testing quarterly, SBOM for agent dependencies.
Assume compromised retrieval chunk—design zero-trust inside agent loop.
Practical Use Case
Zero-trust agent mesh: mTLS between services, SPIFFE identities, tools scoped per workload—not shared admin API keys.
Cost Optimization
Overview
AI cost optimization routes by task complexity, caches embeddings and completions, batches inference, and caps agent step budgets.
Technical Deep Dive
Tactics: semantic cache (similar queries hit cached answer), distill to smaller models, prompt compression, reserved GPU capacity vs. on-demand API.
Chargeback dashboards per business unit drive behavioral change.
Practical Use Case
Support platform saves 55% inference cost: FAQ → 8B local model; complex → frontier; cache hits 22% of volume.
Model Benchmarking & Adoption Strategy
Overview
Enterprise adoption must be grounded in reproducible model evaluation—not vendor marketing. The AI Model Benchmarking 2026 Edition provides a performance matrix of 16 frontier, open-source, and Perplexity search-augmented models across GPQA Diamond, SWE-bench Verified, MMLU-Pro, and AIME 2026.
Technical Deep Dive
The 2026 report documents MMLU parity: frontier vs. open-source gap fell from 17.5 percentage points (2024) to 0.3 pp. Task-specific leaders diverge—Claude Opus 4.6 leads coding arena ELO (1548); Gemini 3.1 Pro leads GPQA Diamond; DeepSeek V4 leads SWE-bench Verified at 83.7%. Search-augmented models (Perplexity Sonar Huge) eliminate static knowledge cutoffs with real-time cited retrieval.
Procurement teams should require benchmark evidence per use case: legal/RAG workflows need MMLU-Pro + IFEval; coding agents need SWE-bench; research workflows need live-search accuracy with citation fidelity.
Practical Use Case
A platform team uses the benchmark report's hybrid routing framework: complex reasoning → frontier API; bulk coding → DeepSeek V4; live market research → Perplexity Sonar—cutting inference cost 60–70% while meeting SLA quality bars. Full comparison tables, ELO trends, paradigm analysis, and glossary: prateekdutta2001.github.io/AI-Benchmarking.
Open AI Benchmarking Report → View Adoption Dashboard on Home