We help organizations design, deploy, monitor, and govern LLM-powered systems that are secure, scalable, and production-ready—moving from experimentation to real business impact.
Assess your current AI maturity, model usage, data pipelines, tooling, security posture, and governance gaps. Based on this, we define an LLMOps operating model covering model lifecycle, ownership, evaluation, cost control, and compliance.
Design and build automated pipelines for deploying LLMs across environments (dev, staging, prod). Support for hosted APIs, open-source models, fine-tuned models, and hybrid setups with versioning, rollback, and traffic routing.
Implement structured prompt management, model registries, experiment tracking, and version control. This enables safe iteration, reproducibility, A/B testing, and controlled rollouts of prompts and models.
Operationalize Retrieval-Augmented Generation with automated ingestion, embedding pipelines, vector stores, indexing strategies, and refresh workflows. Ensure data freshness, relevance, and traceability for enterprise knowledge sources.
Build automated evaluation frameworks for accuracy, hallucination detection, bias, toxicity, latency, and cost. Support offline testing, online evaluation, human-in-the-loop feedback, and continuous optimization.
Implement end-to-end observability across prompts, models, inference latency, token usage, errors, and user outcomes. Dashboards track quality drift, usage patterns, SLA adherence, and cost efficiency.
Embed guardrails including access control, PII detection, content moderation, audit logs, policy enforcement, and compliance workflows. Ensure responsible AI usage aligned with enterprise and regulatory standards.
Our LLMOps practice is powered by accelerators that reduce operational complexity and speed up production readiness.
Reusable workflows for deploying, versioning, and routing LLMs across cloud and hybrid environments. Supports canary releases, fallback models, and automated rollback.
Centralized registry for prompts, templates, fine-tuned models, and experiments with lineage, approval workflows, and performance metrics.
Prebuilt pipelines for document ingestion, embedding generation, vector indexing, data refresh, and relevance tuning—designed for enterprise knowledge at scale.
Automated test harnesses for quality, safety, bias, hallucination detection, and policy enforcement. Enables continuous evaluation and governance.
Dashboards for tracking token usage, latency, quality metrics, user feedback, drift, and cost optimization across models and applications.
Controls for access management, data isolation, prompt filtering, content moderation, and auditability—ensuring safe and compliant AI operations.
Talk to Inferenz specialists and move from experimentation to production with secure, observable, and cost-efficient AI systems<br />
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