Transform Enterprise AI Operations with Scalable LLMOps Services

Establish the operational foundation required to manage LLM workloads reliably, securely, and efficiently in production.

• LLMOps maturity and production-readiness assessment
• Operational architecture and tooling strategy
• LLM lifecycle and operating model definition
• Implementation roadmap and capability planning

Treat prompts as governed, versioned production assets with controlled changes and measurable impact.

• Prompt version control, review, and staged rollout
• Regression testing before production release
• Controlled rollback when prompt changes cause regressions
• Prefix-stable prompt structure to protect cache savings

Continuously measure LLM behavior so quality issues are identified before they affect production users.

• Automated accuracy, groundedness, and relevance evaluation
• Hallucination and response consistency monitoring
• Retrieval quality scored separately from generation quality
• Agent trajectory scoring for tool-calling systems

Keep LLM consumption predictable by controlling how models are selected, accessed, and used across production workloads.

• Per-application, feature, and tenant token tracking, split by cached, uncached, and output
• Complexity- and workload-based model routing with an evaluation baseline per route
• Budget thresholds, usage alerts, and spend forecasting
• Per-agent-run caps on steps, tool calls, and spend

Apply runtime controls that protect LLM interactions from malicious inputs, sensitive data exposure, and policy violations.

• Indirect prompt injection defense on retrieved and tool-returned content
• PII detection, masking, and tokenization with residual-leakage testing
• Least-privilege tool scoping with confirmation on irreversible actions
• Pre-deployment red teaming covering system prompt leakage and jailbreaks

Maintain end-to-end visibility into LLM behavior, usage, performance, and changes in production.

• Request-level tracing across prompts, retrieval, and generation
• Token, latency, throughput, error, and quality monitoring
• Model behavior and provider-version change detection with drift and anomaly alerts
• OpenTelemetry GenAI instrumentation for backend portability

Manage production model releases and serving environments through controlled, repeatable lifecycle processes.

• Model-version cataloging and release tracking
• Environment promotion across development, staging, and production
• Controlled rollback, retirement, and lifecycle management
• Provider deprecation tracking with rehearsed migration paths

Create accountable controls for how LLM systems are accessed, modified, monitored, and operated across the enterprise.

• Role-based access and environment-level controls
• Audit trails, usage policies, and operational records
• Controls mapped to OWASP LLM Top 10, NIST AI RMF, and ISO/IEC 42001
• EU AI Act readiness for in-scope systems

Test LLM applications before and after deployment to measure response quality, identify issues, and ensure changes do not affect production performance.

• Accuracy, relevance, and groundedness testing
• Automated evaluation using business-specific test datasets
• Regression testing for prompts, models, and application changes
• Response quality and consistency tracking

Connect LLM applications with trusted business data through well-managed RAG pipelines, helping improve information retrieval and keep responses relevant.

• Vector database setup and management
• Document ingestion, chunking, and indexing
• Retrieval quality and relevance monitoring
• Knowledge source updates and pipeline maintenance