AI, data science and analytics engineering
Ezora designs, builds and runs the machine learning models, data platforms and LLM applications your business depends on, then keeps them fast, accurate and affordable with DevOps, MLOps, LLMOps, AIOps and AgentOps.
We build machine learning models
Built on tools your team already trusts
From the first exploratory notebook to models serving real traffic, we cover the work in between: data, models, products, and the operations that keep them healthy.
Predictive models trained on your data and built to be retrained: forecasting, scoring, recommendations and computer vision.
Statistical analysis and experimentation that turns messy data into answers you can defend in a board meeting.
Dashboards and metric layers leadership actually opens, with numbers that agree across every team.
Assistants, copilots and document automation built on large language models and grounded in your own data.
Pipelines, lakehouses and warehouses that keep models and dashboards fed with clean, on-time data.
Fast, accessible websites, internal tools and AI-powered apps that put your models in front of real users.
Shipping a model is the start. These five disciplines cover how software, models, LLM apps, infrastructure and agents are delivered, watched and improved.
The building blocks we design, wire together and run in production. Each one below is a live simulation of how it behaves. Want to try working versions? Open the AI Lab.
Retrieval-augmented generation connects your private documents to a language model through vector search, so answers are grounded in your data and cite their sources.
Semantic search and long-term memory for AI apps. We choose, index and tune the store that fits your scale.
Specialised agents that plan, research, code and review together, with clear hand-offs and guardrails.
Track data drift, prompt drift, hallucination rates and latency, and get alerted before users notice.
One governed entry point for every LLM call: authentication, rate limits, routing, caching and cost tracking.
Each project is staffed with the roles it needs, from model building to platform and LLM engineering. Hover or tap a card to see what each role owns.
Builds machine learning and deep learning models.
See what they ownOwns pipelines, the model registry and production scaling for ML.
See what they ownBuilds internal infrastructure, vector stores and GPU and cloud clusters for AI.
See what they ownSpecialises in fine-tuning, prompt engineering and RAG implementations.
See what they ownLeave your work email and we'll set up a 30-minute call to scope it.
A clear path with something you can see and test at every stage.
We map the business problem, audit the data you have, and agree what success looks like in numbers.
You get: a scoped plan, a data audit and success metrics.
A working proof on your real data, measured against an agreed baseline so the result is not a guess.
You get: a live demo and an evaluation report.
Pipelines, APIs, CI/CD, security and monitoring, deployed to your cloud and documented for your team.
You get: a production system and runbooks.
Monitoring, retraining, evaluation runs and cost tuning, so quality holds up as your data changes.
You get: a monthly health report and steady improvements.
A fixed scope and timeline for a defined outcome, such as a forecasting model or a RAG assistant.
Two to four engineers embedded with your team and billed monthly, for ongoing roadmaps.
We monitor, retrain and improve what is already live, with a monthly health report.
Startups validating an AI idea, and established teams that need models, data pipelines or LLM apps taken to production. If you have data and a clear business question, we can usually help.
Yes. We start with a short audit of the code, data and infrastructure, then stabilise, document and add monitoring before building anything new.
Azure, AWS and Google Cloud, and on-premise when needed. We prefer open, well-supported tools and fit into the stack your team already runs.
We work inside your cloud accounts where possible, follow least-privilege access, and sign an NDA before any data is shared. For LLM apps we can use private or self-hosted models so data never leaves your environment.
A focused proof of concept takes two to four weeks. Taking it to production usually adds four to eight weeks, depending on integrations and data quality.
Yes. An ops retainer covers monitoring, retraining, evaluation runs, cost tuning and fixes, with a monthly report on how the system is performing.
Share a few lines about your data, your goal and your timeline. We'll reply with questions and suggested next steps.
Thanks, . We'll reply to with next steps. Your reference is .