LLMOps Services
Move from unmanaged LLM deployments to governed, cost-controlled, production-grade AI operations. Xcelore unifies prompt management, evaluation, guardrails, and monitoring across every model, provider, and application in your AI estate.
Talk to Our MLOps ExpertsEnterprise LLMOps Expertise Built for Production Scale
Advanced LLMOps Services for Reliable AI Operations
LLMOps Strategy & Architecture
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
- Baseline against OWASP LLM Top 10, NIST AI RMF, and ISO/IEC 42001
Prompt Lifecycle Management
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
- Model-version pinning with scheduled prompt re-validation
Evaluation & Hallucination Control
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
- LLM-as-judge and human evaluation calibrated against a labeled golden set
- Retrieval quality scored separately from generation quality
- Agent trajectory scoring for tool-calling systems
Cost & Model Routing Governance
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
- Provider-side prompt caching, semantic caching, and Batch APIs, up to 90% lower cost on cached input tokens
- Budget thresholds, usage alerts, and spend forecasting
- Per-agent-run caps on steps, tool calls, and spend
Guardrails & Security
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
- Unsafe, biased, or policy-violating output detection
- Least-privilege tool scoping with confirmation on irreversible actions
- Pre-deployment red teaming covering system prompt leakage and jailbreaks
Observability & Drift Detection
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
- Agent-run tracing with per-run cost attribution
- OpenTelemetry GenAI instrumentation for backend portability
Deployment & Model Lifecycle Management
Manage production model releases and serving environments through controlled, repeatable lifecycle processes.
- Multi-provider endpoint management and failover with per-model eval baselines
- 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
LLM Governance & Compliance
Create accountable controls for how LLM systems are accessed, modified, monitored, and operated across the enterprise.
- Model, prompt, and release approval workflows
- 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
Start With Clarity Before You Scale.
Assess your LLM ecosystem, observability, evaluation, security, governance, and operating costs to identify priorities and define a practical roadmap for reliable production AI.
Book Your Readiness AssessmentEnterprise LLMOps Services Across Industries
AI creates value differently across every industry. We help organizations identify the highest-impact opportunities and build intelligent products that solve industry-specific challenges, improve operational efficiency, and create sustainable competitive advantage.
How Could LLMOps Strengthen Your AI Ecosystem?
Build robust LLM operations around your models, applications, data, and workflows. Create scalable LLMOps capabilities that improve deployment, evaluation, observability, governance, and ongoing model performance.
Build Your LLMOps Foundation- Enterprise LLM Workflows
- Continuous Model Evaluation
- Prompt & Model Management
- LLM Observability & Monitoring
- Cost & Performance Optimisation
- Secure AI Operations
Secure and Governed LLMOps for Production AI
LLM Ops adds prompt, retrieval, evaluation, model, and runtime considerations to production operations. Xcelore protects sensitive context and model access while aligning deployment and lifecycle practices with applicable AI, data, and sector requirements.
Compliance
Bring Control, Security, and Efficiency to Every LLM Operation
Build governed LLM operations with integrated observability, evaluation, security, cost controls, and continuous optimisation to improve AI reliability, manage risk, and scale production workloads confidently.
How We Build and Operationalise Production-Ready LLMOps?
Our LLMOps delivery process takes your organization from an unmanaged LLM footprint to governed, cost-controlled operations, and keeps improving from there.
Diagnose the Estate
We audit every LLM touchpoint in production models, prompts, vector stores, agents, connected tools and baseline cost, quality, latency, cache efficiency, and risk exposure to identify where LLMOps delivers the greatest impact first.
Architect the Operating Model
We define evaluation criteria, guardrail policies, routing rules, and cost budgets, set what runs inline versus asynchronously, and assign escalation ownership, mapped to your compliance and risk requirements.
Activate in Context
We integrate observability, evaluation pipelines, prompt version control, and guardrails directly into your existing infrastructure and repositories, rolled out in stages to avoid disrupting live traffic.
Evolve Toward Continuous Optimization
Post go-live, we continuously tune routing, evaluation, and guardrail thresholds while tracking cost per query, cost per agent run, guardrail false-positive rate, quality scores, incident recurrence, and rollback frequency, expanding automation as confidence builds.
What Sets Xcleore LLMOps Engineering Expertise Apart?
Engineering-first delivery
Senior AI and platform engineers implement inside your repositories and infrastructure, with vendor-neutral telemetry and runbooks your team owns, not a strategy deck handed over for someone else to build.
Model- and cloud-agnostic
We work with the model providers, vector stores, and cloud platforms you already run, preserving prior investment instead of forcing a switch.
Governance built in
Approval gates, audit trails, and rollback logic are part of the design, not an afterthought bolted on before a compliance review.
Full-stack AI and data expertise
LLMOps outcomes depend on the infrastructure and data pipelines underneath; our cloud, data engineering, and MLOps teams support the same engagement end to end.
Outcome-based reporting
Engagements are measured against cost, quality, and reliability metrics you already track, not vanity dashboards.
Agent-era ready
Tool-call tracing, trajectory evaluation, indirect-injection defense, and per-run cost ceilings, the controls agentic systems need and chat-era LLMOps tooling does not cover.
What Sets Xcleore LLMOps Engineering Expertise Apart?
Senior AI and platform engineers implement inside your repositories and infrastructure, with vendor-neutral telemetry and runbooks your team owns, not a strategy deck handed over for someone else to build.
We work with the model providers, vector stores, and cloud platforms you already run, preserving prior investment instead of forcing a switch.
Approval gates, audit trails, and rollback logic are part of the design, not an afterthought bolted on before a compliance review.
LLMOps outcomes depend on the infrastructure and data pipelines underneath; our cloud, data engineering, and MLOps teams support the same engagement end to end.
Engagements are measured against cost, quality, and reliability metrics you already track, not vanity dashboards.
Tool-call tracing, trajectory evaluation, indirect-injection defense, and per-run cost ceilings, the controls agentic systems need and chat-era LLMOps tooling does not cover.
Let’s talk
Frequently Asked Questions
Do we need to replace our existing AI stack to work with Xcelore?
No. We integrate into the models, vector stores, and cloud infrastructure you are already running. LLMOps is layered on top of your existing investment, not a replacement for it.
Can you take over an LLM system another vendor or our internal team already built?
Yes, most of our LLMOps engagements start this way. We audit what is live, baseline its current cost and quality, and add governance, evaluation, and monitoring without requiring a rebuild.
How is this engagement priced, project, retainer, or something else?
We offer three models: a fixed-scope assessment to baseline your current estate, a project-based build for teams that know what they need implemented, and an ongoing managed-operations retainer for monitoring, tuning, and support. We scope this on the first call based on where your AI estate currently stands.
What is the minimum commitment to get started?
The LLMOps Readiness Assessment is the lowest-commitment entry point, a fixed-scope engagement that baselines your current cost, quality, and risk exposure and gives you a prioritized roadmap, with no obligation to continue into implementation.
How long before we see results after kickoff?
The readiness assessment typically completes within 2–3 weeks. Initial instrumentation, observability, evaluation pipelines, and cost tracking, is usually live within a quarter, with cost and quality improvements visible well before full rollout is complete.
Bring Control, Clarity, and Confidence to Every LLM Workflow.
Build reliable LLMOps across models, prompts, applications, retrieval, evaluation, security, and cost management to improve production performance, strengthen governance, and scale AI confidently across your enterprise.