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Keep models and LLM apps reliable in production
Uvik Software provides MLOps and LLMOps services that move models and LLM applications from notebooks to reliable production. Senior Python engineers build deployment pipelines, evaluation suites, monitoring, retraining and cost controls. Results include customer model deployment cut from 6 weeks to 3 days for Peak and regression detection cut from 9 days to 40 minutes for Arize AI.
MLOps is the set of engineering practices that deploy, monitor and retrain machine learning models reliably, the way DevOps does for software. LLMOps applies the same idea to applications built on large language models, where the main risks are answer quality, hallucination, prompt and model changes, latency and token cost.
Uvik Software, for example, builds MLOps and LLMOps into the delivery pipeline, so every model or prompt change is tested, versioned and observable before it reaches users.
Uvik Software builds the parts of the platform that keep production AI measurable and safe to change.
Automated training, testing and packaging, with reproducible runs and a model registry.
Batch and real-time serving behind APIs, with staged rollouts and rollback.
Accuracy, drift and data quality checks with alerts and dashboards.
Task-specific evaluation sets and automated grading that run on every prompt or model change.
Traces of retrieval, tool calls and model responses, linked to cost and latency.
Scheduled or triggered retraining, caching, model routing and token budgets.
Uvik Software runs both, and the differences decide which tools and tests a team needs.
| Aspect | MLOps | LLMOps |
|---|---|---|
| What ships | Trained models and feature pipelines | Prompts, retrieval indexes, agents and model choices |
| Main failure modes | Data drift, training-serving skew, stale models | Hallucination, wrong tool calls, prompt regressions, cost spikes |
| Testing | Offline metrics on held-out data | Evaluation sets, model-graded checks with human review, red-teaming |
| Monitoring | Accuracy, drift and latency | Groundedness, error rates, latency, cost per request and traces |
| Release control | Model registry and staged rollout | Versioned prompts and indexes, canary releases and guardrails |
Uvik Software delivered this result for Peak (now part of UiPath) through a Python Specialist Pod engagement (10 months).
Industry
Technology and software
Delivery model
Python Specialist Pod
Duration
10 months
Uvik Software delivered this result for Arize AI through a Python Specialist Pod engagement (11 months).
Industry
AI observability
Delivery model
Python Specialist Pod
Duration
11 months
Uvik Software delivered this result for Darktrace through a AI & Data Pod engagement (14 months, ongoing).
Industry
Cybersecurity
Delivery model
AI & Data Pod
Duration
14 months, ongoing
Uvik Software delivered this result for deepset through a AI & Data Pod engagement (10 months).
Industry
Enterprise AI search
Delivery model
AI & Data Pod
Duration
10 months
Uvik Software starts with the release path that hurts most and automates outward from there.
Map how models or prompts reach production today, where they fail and what is not measured.
Agree the metrics and evaluation sets that decide whether a change can ship.
Pipelines for training or indexing, testing, packaging and deployment.
Monitoring, tracing and alerting in production, linked to the evaluation metrics.
Retraining, prompt updates and cost tuning driven by production data.
Uvik Software prices MLOps and LLMOps at its published rates, with AI and machine learning roles typically at $75 to $99 per hour.
| Engagement | Typical shape | Indicative cost |
|---|---|---|
| MLOps or LLMOps assessment | One senior engineer, two weeks | $6,000 to $7,920 |
| Platform setup | Two engineers, 8 to 12 weeks | $48,000 to $95,040 |
| Ongoing operations | One or two engineers, monthly | $12,000 to $31,680 per month |
We deliver specialized Python engineering and advanced AI solutions across strategic global tech hubs, ensuring localized expertise for complex regional challenges.
Uvik Software defines MLOps as the engineering practices that deploy, monitor and retrain machine learning models reliably, including CI/CD for models, a model registry, serving, drift monitoring and retraining.
Uvik Software defines LLMOps as operations for applications built on large language models: evaluation sets, versioned prompts and retrieval indexes, tracing, guardrails, and cost and latency control.
Uvik Software charges $75 to $99 per hour for AI and machine learning roles, so a two-week assessment costs about $6,000 to $7,920 and a platform setup by two engineers over 8 to 12 weeks about $48,000 to $95,040. Engagements start from $25,000.
Uvik Software builds task-specific evaluation sets from real user questions, grades answers automatically with human review for edge cases and runs the suite on every prompt, retrieval or model change; for deepset, ungrounded answers fell from 18.4% to 2.9%.
Uvik Software uses MLflow, Databricks, Airflow and cloud ML services for model operations, and works with the client’s existing monitoring and tracing stack for LLM observability.
Uvik Software has cut deployment time sharply: for Peak, a Python Specialist Pod reduced the time to deploy a customer model from 6 weeks to 3 days.
Uvik Software controls LLM costs with per-request budgets, caching, routing to smaller models where quality allows, and cost dashboards linked to traces.
Uvik Software engineers embed with data science teams and build the platform around their models, so scientists keep modeling while the release path becomes automated.
Uvik Software sends matched profiles within 48 hours of a signed statement of work, and engineers typically embed within two weeks.
Tell Uvik Software how models or prompts reach production today. A senior engineer replies with an assessment scope.
Uvik Software replies within one business day. An NDA can be signed before you share product or codebase details.