Data & AI
LLMOps Consulting Services
Run large language model features in production with confidence: evaluated before every release, monitored for quality and cost, and secure, with a founder-led team beside you.
Talk to the foundersWhat LLMOps is, and what you get with Authect
LLMOps (large language model operations) is the set of practices for running language model features reliably in production: versioning prompts and models, testing them before release, watching quality and cost once they're live, and keeping data safe throughout. It brings together engineering, data and operations work that is easy to skip in a prototype and expensive to skip in production.
We help you put those practices in place, whether you're launching your first LLM feature or already running several. The founders review your setup with you and a senior engineer leads the work, combining software engineering, DevOps and AI experience with a clear view of what each improvement is worth to your business.
Everything we build, from evaluation sets and pipelines to dashboards and runbooks, lives in your own accounts, documented so your team can run it without us.
Benefits
Benefits of LLMOps for your organisation
Good LLMOps turns a promising prototype into a feature you can rely on, change safely and afford to run.
- 01
Better use of people and budget
Shared pipelines, tools and standards let engineers, data specialists and operations work from the same picture, so new LLM features reach production sooner and model and infrastructure spend goes where it adds value.
- 02
Shorter development cycles
Automated evaluation tells developers within minutes whether a change to a prompt, model or data source made things better or worse, so they can iterate quickly and catch incorrect results before users do.
- 03
Lower risk, stronger security
Access controls, input and output validation, protection of sensitive data and full audit logs reduce the chance of leaks, misuse and harmful outputs, and make issues easy to trace.
- 04
Scalability you can plan for
Monitoring of usage, latency and cost, plus caching, routing and capacity planning, keep features fast and affordable as demand grows and workloads fluctuate.
Key components
Key components of the LLMOps process
Data engineering and management
We build the pipelines that collect, clean, version and index the data your language models rely on, from document stores for retrieval to datasets for fine-tuning, with sensitive information protected along the way.
Explore the serviceExperimentation and evaluation
We create evaluation sets from your real cases and test prompts, models and fine-tunes against them, measuring accuracy, safety, latency and cost so every change is a decision backed by evidence.
Deployment and monitoring
We release LLM features gradually, with versioned prompts and models, rollbacks and live monitoring of quality, drift, latency and spend, plus alerts that reach the right person.
Pipeline management
We design end-to-end pipelines that connect data, evaluation and deployment, so updates to models, prompts and knowledge move to production through the same tested, repeatable path as your code.
Explore the service
Need expert help getting your language model features production-ready? Talk to the founders.
Contact usWhy Authect
Why choose Authect for LLMOps consulting
- 01
DevOps, ML and AI experience together
The senior engineers who lead our projects build software, infrastructure and AI features, so your LLMOps setup fits the rest of your stack rather than living in its own silo.
- 02
End-to-end LLMOps support
We help across the whole lifecycle, from choosing models and building evaluation to deployment, monitoring and cost control, whether we built your LLM features or someone else did.
- 03
Founders you can talk to
You work directly with the founders and the engineer doing the work, never a salesperson, so recommendations come with the reasoning behind them.
- 04
Built for your team to own
We document every pipeline, dashboard and runbook and hand over the accounts, so your team can run and extend LLMOps confidently on its own.
Insights
NewsWeekly briefing, 4 November 2026: the $6.3bn Binance heist, HTTP2Shell keeps spreading, and one robots.txt line for AI trainingView news 
R&DHow to evaluate LLM features in production: test sets, judges, metrics and budgetsView R&D post 
GuideHow to add AI to your product: a practical guide to LLM featuresView guide 
R&DRAG in production: what actually decides answer quality?View R&D post
FAQs
LLMOps FAQs
What does LLM stand for?
LLM stands for large language model: a neural network trained on large amounts of text to understand and generate language. LLMs power assistants, summarisation, search, document processing and many other features in modern software.
What does an LLM do in a business application?
It reads and writes language on demand: answering questions from your documents, drafting replies, extracting data from forms, classifying requests or planning steps for an agent. The application around it supplies the right data, checks the output and decides what happens next.
What is the purpose of LLMOps?
To make language model features dependable once they're in front of real users. That means knowing whether quality is holding up, controlling cost and latency, protecting data, and being able to change prompts or models safely.
What is the difference between MLOps and LLMOps?
MLOps covers training, deploying and monitoring machine learning models in general. LLMOps adds the specifics of language models: managing prompts and retrieval, evaluating open-ended text, guarding against harmful or made-up output, and handling usage-based costs from model providers.
What is the difference between LLMOps and AgentOps?
AgentOps focuses on AI agents that take actions across tools and systems. It builds on LLMOps and adds tracing of multi-step runs, tool permissions, approval steps and evaluation of whole tasks rather than single responses.
What is LLM inference optimisation?
It's making each model call faster and cheaper without losing quality, for example by choosing a smaller model where it's good enough, caching repeated requests, trimming prompts, batching work and routing harder questions to stronger models.