Technology guidance
For business leaders facing technology decisions who want a partner to weigh the options, trade-offs and priorities.
Capability
Move AI from experiment to controlled production
We support the engineering lifecycle required to move AI systems from experimentation into controlled production environments.
In short
MLOps and LLMOps are the practices that keep AI systems reproducible, tested and monitored after the prototype. Nexstella builds the pipelines, release controls, evaluation and monitoring around models, prompts and retrieval, so quality, cost and reliability can be measured and changes can be released with control.
Last updated
01 When it matters
When does an AI system need MLOps and LLMOps?
Production AI systems should be tested against representative use cases and failure scenarios, and evaluated continuously as models, prompts, datasets, retrieval indexes and business requirements change.
That calls for reproducible pipelines, versioned models and prompts, automated testing and deployment, and monitoring of accuracy, cost and reliability.
A business has an AI assistant in production and cannot yet say how accurate, how costly or how reliable it is, or what happens to its quality when a model or prompt changes.
We would add tracing and evaluation, monitor cost and latency, version the prompts and retrieval settings, and introduce regression tests so that each change is released with control.
02 Services
What does this include?
- Reproducible training and experimentation pipelines
- Dataset and feature management
- Model versioning and registry management
- Automated testing and deployment
- Model serving and inference infrastructure
- Performance and drift monitoring
- Model rollback and lifecycle management
- Model gateways and provider abstraction
- Prompt and configuration versioning
- Retrieval pipeline testing
- Automated AI quality and regression evaluation
- Model and prompt release management
- Token consumption and inference-cost monitoring
- Latency and throughput monitoring
- Safety testing and policy enforcement
- Tracing, debugging and failure analysis
- Evaluation dataset and benchmark management
- Versioning of embedding models and retrieval configurations
03 Applications
Where does this usually apply?
- Establishing a repeatable deployment process for machine learning models
- Monitoring the accuracy, cost and reliability of a production AI assistant
- Comparing model providers against task-specific quality and latency requirements
- Testing RAG applications against representative business questions
- Introducing release controls and regression tests for AI-enabled products
04 Technology
How do we choose the technology?
- MLflow
- Kubeflow
- Model-serving frameworks
- Evaluation libraries
- Observability platforms
- Container infrastructure
- Managed cloud AI services
Tooling is selected against the workload, the compatibility with your existing systems, your team's operational capacity, licensing and total cost of ownership, and portability between providers.
05 Deliverables
What do you get?
- Reproducible pipelines for training, testing and deployment
- Evaluation datasets and regression tests, with acceptance thresholds defined before deployment
- Monitoring of quality, latency and cost per successful task
- Release and rollback procedures for models, prompts and retrieval configurations
- Technical documentation and handover support procedures
06 Needs
Which needs does this serve?
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AI for your business
For businesses that want AI to solve a real problem and need to know what is feasible and how to deploy it safely.
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Growing technology
For growing businesses whose infrastructure, data and releases need to keep pace with more users and complexity.
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Specialist engineering
For companies with their own engineers who need specialist skills, architecture advice or extra capacity.
07 Questions
What do people usually ask?
It is the engineering around a language-model application after the prototype: model gateways, versioned prompts and configurations, retrieval testing, automated quality and regression evaluation, release management, and monitoring of tokens, latency, cost and safety.
Against the quality and latency requirements of the specific task, using representative business questions. Model gateways and provider abstraction are part of the service.
Prompts and configurations are versioned, as are embedding models and retrieval configurations, and automated quality and regression evaluation is part of the service. Production AI systems should be evaluated continuously as models, prompts and business requirements change.
Solutions
Which solutions do we build for this?
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AI features in your product
We build AI-powered search, summaries, recommendations and assistants into software you already run, with permissions, evaluation and cost controls around each.
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Knowledge assistants
An assistant that searches the documents your team is allowed to see and answers with references, so people spend less time searching through folders and old threads.
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MLOps and LLMOps
We build the engineering lifecycle around machine learning models and LLM applications: reproducible training, versioned prompts, evaluation, release controls and monitoring.
If this sounds like you: Get your models out of notebooks and into production