AI Platforms: MLOps, LLMOps and the AI Control Layer

MLOps covers the lifecycle of models you train yourself: data and feature pipelines, experiment tracking, model registries, automated deployment and drift monitoring. LLMOps addresses applications built on large language models, which add new concerns such as prompt versioning, retrieval pipelines, evaluation of open-ended outputs, guardrails, tracing of multi-step agents and token cost management. A modern AI platform supports both, sharing common foundations like identity, observability and CI/CD.

An AI gateway is a central service through which all applications access language models and other AI services. It handles authentication, routes requests to approved providers, applies rate limits and budgets, caches repeated requests, enforces guardrails and records usage for cost attribution and audit. Without it, every application must solve these problems independently, and leadership cannot see or control AI usage across the organization.

Usually a combination. Cloud providers and vendors such as Databricks offer strong managed components, and open-source tools like MLflow and LiteLLM fill specific gaps. Building everything yourself is rarely justified, but neither is buying a single product that locks you in. We evaluate your requirements, existing investments and team skills, and recommend the mix that minimizes custom code while keeping you flexible.

A minimum viable platform supporting two or three lighthouse use cases is typically ready in eight to twelve weeks, depending on how mature your cloud foundation, networking and identity setup already are. We deliberately avoid building a large platform before anyone uses it. Further capabilities are added incrementally as teams onboard, guided by actual demand and measured adoption.

We apply defense in depth: private networking to model endpoints, centralized secrets management, identity-based access for users and services, data classification and masking before data reaches models, guardrails against prompt injection and data leakage, and full logging of inputs and outputs where policy allows. For agents, tool access is scoped by role and sensitive actions require human approval. Security reviews and red-team tests are part of every release.

Yes. We design the AI platform as an extension of your data foundation rather than a separate island. It can read curated data from Databricks, Snowflake, BigQuery, Microsoft Fabric or your data lake, respect existing access controls and lineage, and write predictions and results back. This keeps governance consistent and avoids copying sensitive data into yet another system.