A bad model version is reversible, not a production incident
We make model deployment automated, versioned, and repeatable, so shipping a new model is a routine push with a clean rollback, not a manual event that everyone dreads.
We watch the inputs and the outputs: data freshness and distribution on the way in, accuracy and quality on the way out, and we alert the named owner before a slide becomes a customer-facing problem.
The output-side view most teams miss: hallucination and refusal rates, latency, token cost, and traceability, so an LLM or agent that is degrading is visible before a decision is made on its answer.
We build the triggers and pipelines that retrain and re-certify a model as the world moves, so accuracy is maintained on purpose instead of decaying on its own.
The model was accurate at launch, and nobody knows if it still is
A model degrades as the data it sees drifts from the data it learned on. Without monitoring, the first sign of trouble is a wrong answer that already reached someone.
When there is no output monitoring, the model, not a dashboard, is what surfaces the problem, in front of a customer or inside a decision, where it costs the most.
Shipping a model by hand means no versioning, no clean rollback, and a change nobody can reproduce. The next model update is a held breath instead of a routine push.
MLOps is the operations layer that keeps a machine learning or AI model working after launch: automated deployment, monitoring for drift and quality, retraining triggers, and rollback. It matters because a model that is accurate on launch day quietly degrades as the data changes, and MLOps is what catches that before it reaches a customer or a decision.
What is the difference between data observability and model observability?
Data observability watches the pipelines feeding the model: freshness, volume, schema, and distribution. Model observability watches what the model does: accuracy, hallucination rate, drift, latency, cost, and whether each answer can be traced. You need both, because a clean pipeline can still feed a model that degrades, and a good model on stale data produces confident nonsense.
Do you cover LLMs and agents, or only traditional ML models?
Both. Classic ML models need drift and accuracy monitoring; LLMs and agents also need hallucination rates, output-quality benchmarks, token cost, and, for agents, which actions they take and how often a human overrides them. We instrument whichever you run in production.
We baseline the model's inputs and outputs, then monitor both continuously against that baseline. When the input distribution shifts or output quality slides past a threshold, the named owner gets an alert, and where it makes sense a retraining pipeline triggers. The point is to notice before a customer or a decision does.
Either. We build the deployment, monitoring, and retraining plumbing, and we can run it on a retainer with a named engineer, handling alerts, retraining, and model updates as an ongoing service. Many clients start with the build and move to managed operations once it is in place.
For a single model already in production, a scoped MLOps setup with deployment, monitoring, and rollback typically ships in 6 to 10 weeks. A broader program across several models takes longer, but we start with the model whose failure would hurt most.
MLOps consulting: model deployment, drift and quality monitoring, model observability, and retraining. Keep your ML and AI models accurate and governed after launch.
Model deployment, drift and quality monitoring, model observability, and retraining. Models that survive launch.
There is no flat rate. These are the factors that move the effort and the price.
One model is a setup; a fleet across teams is a platform and an operating model.
Output-side monitoring for generative models adds hallucination, quality, and cost tracking.
Regulated or high-stakes models need traceability, audit trails, and tighter thresholds.
Standing it up is one price; running the alerts and retraining on a retainer is another.
You have a model in production and cannot say whether it is still accurate.
A model deploy is a manual, risky event with no clean rollback.
You need to prove a model is monitored and governed for an audit.
Your models are not in production yet: start with AI Readiness.
The problem is the pipelines, not the model: see Real-Time Data Observability.
You want the whole AI build, not just operations: see AI Consulting.
A model that is accurate on launch day drifts as the data changes, and most teams never notice until a customer or a decision does. MLOps is the operations layer that keeps a model working: automated deployment, monitoring for drift and quality, retraining triggers, and rollback. We build it, and can run it, so your AI survives the demo.
MLOps is the operations layer that keeps a machine learning or AI model working in production: automated deployment, monitoring for drift and quality, retraining triggers, and rollback. Model observability is the output-side view of the same job. Thinklytics builds this plumbing and can operate it, so a model stays accurate and governed after launch instead of degrading quietly.
The full path from use-case to production. MLOps is the part that keeps it running afterward.
The input side: monitoring the pipelines and tables that feed the model, so it learns from good data.
Run MLOps and governance as a retainer, so the model and its data stay AI-ready over time.
Start with an audit. We review your models in production, how they are deployed, and what you can see about them, then give you a prioritized MLOps plan.