Deploy, monitor and improve ML models, LLM applications, RAG assistants, AI agents and computer vision systems with practical production controls. We help you track quality, drift, latency, cost, feedback and release health after AI goes live.
AI Operations
Quality
94%
Review pass rate
Latency
820ms
P95 response
Drift
Low
Input change
Cost
Stable
Token usage
Deploy Safely
Move models, AI features and RAG systems from prototype to production with release checks, rollback planning and environment control.
Monitor Continuously
Track model quality, data drift, latency, errors, token usage, retrieval quality, user feedback and operational health.
Control Cost
Review GPU, API, token, storage and infrastructure usage so AI features remain practical after launch.
Improve Over Time
Use feedback, evaluation data and monitoring signals to decide when to tune prompts, update data, retrain models or change workflows.
Beyond Deployment
A model or AI feature can work well during development and still fail in production because users, data, prompts, documents, infrastructure and business rules keep changing. MLOps gives your AI system the operational discipline it needs after launch.
The demo performs well on selected examples, but breaks when real users, messy data and edge cases arrive.
Teams cannot see why an answer was wrong, whether retrieval failed, which model version was used, or how much the request cost.
Data changes over time. Without monitoring, accuracy, relevance or user trust can drop before anyone notices.
LLM tokens, GPU usage, retries, long prompts and unnecessary calls can increase cost if there is no tracking or control.
What we help with
We set up the operational layer around AI systems so teams can see what is working, what is failing and what should be improved next.
Production rollout
Deploy ML models, LLM features, RAG pipelines and computer vision services with reliable APIs, environment separation and controlled release steps.
Quality tracking
Track whether the model continues to perform well after launch using quality signals, review samples, feedback loops and business-specific checks.
Changing data
Detect when production inputs start looking different from expected training or reference data, so teams can investigate before model quality drops.
LLM systems
Monitor LLM apps, AI agents and RAG assistants for answer quality, retrieval relevance, citations, latency, tool calls, cost and fallback behavior.
Continuous improvement
Create review workflows that turn user feedback, failed cases and quality samples into actionable improvement work for prompts, data and models.
Controlled updates
Plan safe model updates with clear versioning, evaluation criteria, approval steps and production rollout control instead of ad-hoc model changes.
Lifecycle
The goal is not to add dashboards for decoration. The goal is to make AI releases repeatable, measurable and easier to improve.
Capture experiments, datasets, prompts, metrics, model versions and evaluation results so development decisions remain traceable.
Maintain clear versions for models, prompts, embeddings, datasets, retrieval indexes and production configuration.
Automate checks for code, model artifacts, prompts, API contracts, environment variables and deployment readiness.
Serve AI features through reliable APIs, queues, background workers, GPU services, cloud functions or application backends.
Track quality, drift, latency, errors, cost, retrieval, tool calls, user feedback and infrastructure health with alert rules.
Use monitoring signals to update prompts, improve knowledge data, tune retrieval, retrain models or adjust business workflows.
Monitoring signals
Different AI systems need different signals. A RAG assistant needs retrieval monitoring, a prediction model needs drift checks, and an LLM workflow needs output quality and cost tracking.
Accuracy, relevance, confidence, review outcomes, wrong-answer samples and business-specific success metrics.
Schema changes, missing values, unexpected categories, outliers, drift and changes in incoming data patterns.
Prompt version, answer quality, hallucination flags, refusal behavior, safety checks, tool-use failures and escalation rate.
Retrieved sources, ranking quality, missing documents, citation coverage, stale content and retrieval latency.
API errors, timeout rates, queue delays, GPU availability, memory pressure, endpoint health and background job failures.
Token usage, model calls, GPU time, storage, retries, long-running jobs, per-user consumption and feature-level AI cost.
Architecture
We design monitoring around the full AI system, not only the model endpoint. This includes the application, model, knowledge sources, integration layer, logs, feedback and improvement process.
LLMOps
LLM systems need a different monitoring approach from traditional ML. You need to know which prompt ran, what documents were retrieved, which tools were called, what the answer cost and whether the response was useful.
Keep prompts versioned, tested and linked with the feature or workflow where they are used.
Check whether the right documents are retrieved, whether citations are useful, and where knowledge gaps exist.
Track when an AI agent calls APIs, which calls fail, and whether fallback or human approval is needed.
Measure prompt size, response size, model choice, retry behavior and response time at feature level.
Create routes for unsupported questions, sensitive topics, low-confidence answers and failed automation steps.
Use real examples to test whether changes in prompts, models or knowledge sources improve or damage quality.
Capabilities
The exact stack depends on your AI system. We can start small with essential logging and dashboards, then grow into drift detection, review workflows and retraining pipelines.
Track what model, prompt, dataset, embedding version or retrieval index is active in each environment.
Review AI behavior without losing context around user input, retrieved documents, tool calls and output.
Monitor input patterns, data quality and prediction behavior so model issues are found earlier.
Create dashboards for quality, success rate, failures, latency, token cost, review status and business KPIs.
Notify the right team when quality drops, error rates rise, endpoints fail, or AI cost crosses expected limits.
Route low-confidence, sensitive or failed outputs into review queues with the context needed for quick decisions.
Plan repeatable dataset updates, evaluation runs and model release cycles instead of manual model replacement.
Maintain traces for model calls, data versions, approvals, production changes and operational incidents.
Use cases
Monitoring is useful for any AI feature that is used by customers, employees, admins or business operations.
Industries
The monitoring approach changes by domain. Healthcare, finance, support, ecommerce and SaaS products each need different review signals and risk controls.
Monitor AI summaries, document workflows, patient-facing assistants, lab booking automation and healthcare admin tools.
Track document extraction, risk signals, report generation, approval workflows and audit-ready AI usage.
Monitor recommendations, product assistants, support automation, catalog data quality and seasonal behavior changes.
Add observability for AI copilots, support bots, workflow agents, usage limits and feature-level adoption.
Track generation quality, moderation, personalization, image workflows, content summaries and editorial review loops.
Monitor prediction workflows, routing suggestions, document processing, exception handling and automation reliability.
Technology
We choose the stack based on your current application, cloud provider, data sensitivity, model type and operational maturity.
Related AI services
MLOps and monitoring usually sits beside AI development, RAG, LLM integration and AI implementation work.
Build custom AI products, LLM apps, RAG systems, computer vision and AI-powered software features.
Explore service →Plan AI adoption inside real business workflows, tools, teams and customer experiences.
Explore service →Integrate OpenAI, Claude, Gemini or open-source models into apps, dashboards and backend workflows.
Explore service →Create source-grounded assistants that answer from documents, PDFs, websites and databases.
Explore service →Book a review
Share your current AI feature, model, RAG system or deployment plan. We will help you identify monitoring gaps, risk points and practical next steps for production readiness.
FAQ
A few practical answers before you plan production monitoring for your AI system.
Whether you are launching a new model, running an LLM app, building a RAG assistant or moving an AI prototype into production, Srishta Technology can help you set up the monitoring and MLOps foundation.