Product
Locara AI
AI infrastructure for applications that need local data and local compute.
GPU compute, model hosting, inference endpoints and the data services around them — so AI applications can be built next to the data they depend on.
AI application
Copilot, assistant, internal tool
AI gateway
Routing, auth, quotas, audit
Model serving
Open-weight LLMs and embeddings
GPU infrastructure
Scheduled accelerated compute
Data services
Vector DB, PostgreSQL, object storage
Local data centre
Operated inside your jurisdiction
Capabilities
What Locara AI includes
GPU infrastructure
Accelerated compute, allocated to the teams that need it.
GPU nodes
Dedicated or shared, scheduled through Kubernetes.
Training & fine-tuning
Capacity for batch jobs and experiments.
Inference serving
Low-latency endpoints for production traffic.
Model platform
Run open-weight models, or connect to external providers where that is the right call.
LLM hosting
Serve open-weight models inside your environment.
Model gateway
One API, with routing, quotas and audit logging.
Private AI environments
Isolated per team, project or tenant.
AI data services
The unglamorous half of an AI application — and the half that decides whether it works.
Vector database
pgvector or a dedicated store for retrieval.
Document pipelines
Ingestion, chunking, embedding and refresh.
Object storage
Source documents and artefacts kept where the models run.
MLOps & LLMOps
Operating AI systems after the demo works.
Deployment pipelines
Versioned models and reproducible rollouts.
Evaluation & monitoring
Quality, latency, cost and drift.
Guardrails
Rate limits, prompt logging and access control.
How it runs
How AI workloads run on Locara AI
An MLOps lifecycle that stays on your data
From governed data to a versioned model in production: training runs on local GPU capacity, and monitoring closes the loop by triggering retraining when a model drifts.
- Datasets and features held in local object storage and databases, so training data does not leave the jurisdiction.
- Training and evaluation as repeatable jobs on GPU nodes, with every model registered, versioned and traceable.
- Canary releases, then live monitoring of latency, drift and cost, with the team on call for the platform underneath.
LLM applications with retrieval, guardrails and evaluation
A retrieval-augmented pipeline over your own documents, served from local GPUs, with policy checks on the way in and out and evaluation gating every change.
- Your documents chunked, embedded and indexed in a vector database that lives with the rest of your data.
- A gateway for authentication and guardrails, retrieval and reranking, model inference on GPUs, and output policy checks.
- Traces and feedback feed evaluation sets, so prompt, model and index changes are scored before release.
Use cases
Where teams use it
Internal copilots
Assistants over policies, contracts and operational documents.
Retrieval-augmented applications
Answers grounded in your own corpus, with citations.
Document intelligence
Classification and extraction over sensitive records.
Build AI where the data already is
Most AI projects stall on data movement rather than on models. Locara AI puts GPU compute, model serving and retrieval infrastructure next to the data, so an application can be built without exporting a corpus to another jurisdiction.
We are direct about the limits: not every workload should run locally. Frontier-model capability, bursty training and some multimodal work may still point to an external provider. We will help you decide which parts genuinely need to stay in, and design the boundary deliberately.
FAQ
Locara AI questions
Many of them, yes: open-weight models for retrieval-augmented applications, classification, extraction and internal assistants run well on local GPU infrastructure, next to the data they need.
Some workloads still point to an external provider — where you need frontier-model capability or very large bursty training. We will tell you when that is the case, and help you design the boundary so the sensitive data stays where it must.
Discuss Locara AI for your workloads
Bring your current architecture and constraints. We will tell you what fits, what does not, and what we would operate for you.