Operationalizing AI GovOps, AI FinOps and AI Performance Engineering
Amalfi AI is a premier consultancy for AI that is governed, cost-controlled and performant — in production, not on paper. We embed the controls, the metering and the engineering directly into the infrastructure you already run.
3
Core Competencies
88
Governance Controls
5
Standards Mapped
F500
Enterprise Clients
Three disciplines. One operating model.
Governance, cost and performance are usually owned by three different teams, reviewed on three different calendars, and enforced by none of them. We make all three continuous, measurable and embedded in the request path.
AI GovOps
Real-time risk classification, automated guardrails and audit evidence generated at the point of every AI interaction — mapped to ISO 42001, NIST AI RMF, the EU AI Act and HIPAA.
Explore AI GovOps →AI FinOps
Metering, attribution and a reconcilable ledger for AI spend, measured from the traffic you already run. Then costs come down in-line, on the fly — waste out, budgets in.
Explore AI FinOps →AI Performance Engineering
Latency, throughput and resilience engineered into the AI path — routing, caching, capacity and provider failover — and held to SLOs with real telemetry.
Explore Performance Engineering →AI GovOps
The operational discipline for embedding governance controls directly into AI infrastructure — transforming compliance from a periodic audit into a continuous, automated flow.
Continuous Over Periodic
Governance controls execute in real-time alongside model inference, not in quarterly review cycles. Every prompt is classified, every response is monitored, every risk is scored at the point of interaction.
Embedded Over Bolted-On
Controls live inside the API gateway, the data pipeline, and the deployment workflow — not in a separate compliance portal. Governance becomes an engineering concern, not a legal afterthought.
Auditable by Design
Every decision, classification, and override generates an immutable audit trail. When regulators ask for evidence, the system produces it automatically — no scramble, no reconstruction.
Prompt risk classification is delivered by PromptClassify, Amalfi AI's classification engine — a 0–100 risk score and regulatory flags for every prompt, running inside your own perimeter.
AI Governance Hardening Framework
Audit-ready, standards-mapped, operationally embedded across five pillars of organizational AI maturity.
Governance & Accountability
Executive oversight structures, RACI matrices, and policy frameworks for AI decision-making authority.
Risk Management
Continuous risk identification, classification, and mitigation workflows integrated into the development lifecycle.
Data Integrity & Privacy
Data lineage, consent management, PII detection, and privacy-preserving architectures for regulated environments.
Model Lifecycle Security
Secure training, validation, deployment, and monitoring with champion/challenger patterns and drift detection.
Compliance & Audit
Automated evidence generation, immutable audit trails, and continuous mapping to ISO 42001, NIST, and EU AI Act.
AI FinOps
The operational discipline for knowing — and controlling — what AI actually costs. Every AI call — LLM, MCP, agent-to-agent — metered, attributed to an owner and reconciled in a ledger, with costs reduced in-line, on the fly, where the spend happens: in the request path.
Visibility Before Enforcement
The first deliverable is your real AI spend — per agent (coding agents such as Claude Code included), per team, per model, per provider — measured from the traffic you already run. No application changes, nothing blocked. You see the bill before anything changes.
Attribution You Can Reconcile
Every token is metered and priced against the provider's live price sheet and written to a ledger finance can reconcile against the invoice. AI spend becomes a line item with an owner, not a monthly surprise.
Waste Out, Budgets In
Uncompressed tool outputs, repeated prompts, missed cache alignment and over-powered models are where AI budgets leak. We eliminate the waste, then put per-team and per-session budgets and hard caps in the request path.
AI Performance Engineering
The engineering discipline for making AI fast and reliable in production. Latency, throughput and resilience are designed into the AI path, measured with real telemetry and held to service levels — across every model and provider you depend on.
Latency Is a Product Feature
Time to first token, end-to-end agent turn time and tail latency are engineered, measured and held to SLOs — not discovered from user complaints. Streaming, connection handling and the gateway itself — Nginx, OpenResty and Lua — are tuned as a system.
Right Model, Right Path
Not every request needs the frontier model — some belong on an open-weight model such as Gemma, running locally. Complexity-aware routing across frontier, open-weight and local models, semantic and prefix caching, and provisioned-versus-on-demand capacity balancing deliver the same answer faster — and cheaper.
Resilient by Default
Provider outages, rate limits and quota exhaustion are normal operating conditions. Health tracking, cascading fallbacks, session affinity and connection pooling keep agents running when a provider doesn't.
Engineering-Grade Infrastructure
The three disciplines live in the same three places: the gateway that carries AI traffic, the data platform that records it, and the observability stack that watches it.
AI Gateway Engineering
Kong AI Gateway and the Nginx / OpenResty layer beneath it as the enforcement point for all three disciplines — risk-aware LLM routing, prompt classification, spend metering and performance policies delivered as custom Lua plugins on the gateway you already run.
Lakehouse & MLOps
Databricks medallion architecture, MLflow and Unity Catalog as the system of record — where audit evidence, the AI cost ledger and performance telemetry land, governed, and reconcilable.
Observability & Monitoring
Dynatrace and OpenTelemetry instrumentation for AI systems — governance SLAs, spend per call and latency per model on one pane, with custom DQL for drift, error-rate and cost anomalies.
A few clients, deeply.
Amalfi AI works with a small number of enterprises as a near-partner on their AI operations — not as a vendor of slide decks. Every engagement starts with measurement and ends with controls running in your infrastructure.
Measure
A short baseline from your live AI traffic: what it costs, how it performs, and where governance exposure sits — with no application changes and nothing blocked.
Engineer
Controls, metering and performance policies embedded in the gateway, data pipeline and deployment workflow you already run — built as code, tested like software.
Operate
SLOs, budgets and audit evidence reviewed continuously, with Amalfi AI alongside your team as the AI estate grows — new models, new agents, new regulations.
Ready to operationalize
your AI?
Whether the question is governance, cost or performance — or all three — Amalfi AI brings the methodology and the engineering depth to make it real in production.
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