LLMOps
The lifecycle discipline for evaluating, versioning, deploying and rolling back models and prompts safely.
What breaks without this
- Prompt and model changes are frequently pushed to production without a repeatable evaluation step.
- There is no version history for prompts, making it difficult to explain why an agent's behavior changed.
- A regression introduced by a new model version is often discovered by end users rather than caught pre-release.
Applies MLOps discipline to large language models and prompts specifically: structured evaluation before release, version control for prompts and fine-tunes, staged deployment, and fast rollback when a change underperforms.
What it does
Version control for prompts, fine-tuned models and evaluation datasets.
Offline evaluation suites — accuracy, safety, regression — run automatically before a change is promoted.
Staged rollout, including canary and percentage-based release, with automatic rollback on regression signals.
Fine-tuning and evaluation pipelines for organizations training or adapting their own models.
Cost and latency benchmarking across model versions and providers to inform routing decisions.
From request to result
Version
Prompts, fine-tunes and evaluation sets are tracked in version control with full change history.
Evaluate
Every proposed change runs against a regression and safety evaluation suite before promotion.
Deploy
Approved changes roll out in stages, starting with a limited traffic percentage.
Monitor
Production signals feed back into the evaluation baseline; regressions trigger automatic rollback.
Built to be audited, not just used
- Every production prompt and model version is traceable to an approved evaluation run.
- Rollback is a first-class, tested operation, not a manual emergency procedure.
Fits existing infrastructure
Exposed to agents through the Model Context Protocol (MCP) for tool and data access, and Agent-to-Agent (A2A) messaging for multi-agent handoff — so third-party and custom agents can integrate without proprietary connectors.
Outcomes to expect
Qualitative, directional outcomes. We do not publish unverified performance figures — see the case studies section for engagement-specific, authorized results.
- Model and prompt changes go through the same rigor as application code changes.
- Regressions are caught in evaluation rather than in production usage.
- Teams can explain, with evidence, why an agent behaves the way it does at any point in time.
Works alongside
Enterprise AI Gateway
A single, governed entry point for every model, prompt and AI service used across the organization.
FoundationAI Governance & Observability
Continuous visibility into what every agent did, why, and whether it stayed inside policy.
FoundationSovereign / Private AI Deployment
Runs models inside an organization's own environment when residency, sovereignty or isolation requirements rule out shared public endpoints.
Evaluate LLMOps for your environment
Talk to an architect about integration into your existing stack, or request a scoped demo against a representative use case.