BYOC · GDPR Ready · Hyperscaler Agnostic

Put AI in production
Keep your data in-house

We make AI production-ready for regulated and operationally critical businesses.

Trusted by teams in Legal · Medical · R&D

The Infrastructure Gap

As GenAI moves to production, SaaS models show cracks

SaaS Model

  • Variable costs scale linearly with traffic
  • Latency degrades at peak cloud usage hours
  • Rate limits throttle concurrent users
  • LLMs lack domain-specific precision

Numid Hosted

  • Fixed monthly infrastructure cost
  • Guaranteed latency and throughput
  • No rate limits — full concurrency
  • Fine-tune on your proprietary data

How Numid Works

One engagement. Three layers. Everything you need to run AI in production.

The expertise gap between pilot and production is where we work.

Deploy

Pre-built, domain-specific LLMs shipped as Docker images, ready to run in your VPC on day one..

🧠

Customize

We fine-tune on your proprietary data using PEFT, integrate with your data sources, and connect to your existing dashboards.

🔒

Operate

Ongoing MLOps support: model monitoring, retraining triggers, alerting, and infrastructure maintenance — on a fixed monthly subscription.

What You End Up With

Your AI. Your cloud. Fully operational.

What a Numid deployment looks like in production.

01

A model that knows your domain

Fine-tuned on your internal documentation, manuals, contracts, or sensor data — not a generic foundation model.

02

Infrastructure that belongs to you

Runs inside your existing cloud commitment or on-premise cluster. We deploy it. You own it.

03

Performance you can plan around

Dedicated hardware, no shared capacity, no surprise bills. Latency and throughput are contractual.

04

An AI team without the headcount

MLOps expertise on call without building an internal platform team.

Use Cases

Built for high-stakes workflows

⚖️

Legal

Contract review, case research, and compliance checks — on your data, inside your VPC.

🏥

Medical

Clinical notes, diagnostic support, and HIPAA-compliant inference with zero data exposure.

🔬

R&D

IP-sensitive research, fine-tuned domain models, and private knowledge extraction at scale.

How It Works

From pilot to production in 4 milestones

1

Step 1

Proof of Concept

Model selection & baseline benchmarking

2

Step 2

Model Training

Data compliance & training platform deployment

3

Step 3

Pilot Deployment

Inference optimisation & high-volume testing

4+

Step 4+

Full Scale-out

Auto-scaling production serving

Ready to own your AI stack?

Talk to our team and get a tailored infrastructure proposal within 48 hours.