Enterprise AI
Locai Labs builds domain-specific language models for enterprises that live entirely within your infrastructure — trained on your data, deployed on your hardware, and owned by you. No cloud dependency. No data leakage. No lock-in. Totally private.
Why it matters
A model built on your data and deployed on your infrastructure gives you capabilities and protections that no cloud AI vendor can offer.
Your model is trained on your data and belongs entirely to you. No third party holds rights to your outputs, weights, or training data. Unlike API-based AI, there's no contractual ambiguity — it's yours, full stop.
Cloud AI vendors can deprecate models or revoke access with little notice. A sovereign model deployed on your infrastructure runs on your terms — no vendor can pull the plug, and there's no service outage that takes your capability offline.
Once your model is deployed, the economics are fixed. You know exactly what your AI costs to run — no per-token pricing, no surprise bills, no vendor hiking rates as your usage scales.
Your model is trained on your documents, workflows, and institutional knowledge — not the public internet. It understands your domain the way a seasoned expert does, delivering accuracy that general-purpose models simply can't match.
Every inference runs inside your perimeter. Your prompts, documents, and outputs never leave your infrastructure. No data leakage, no GDPR exposure, no risk of your proprietary information appearing in someone else's training set.
Why not just use an API?
Cloud APIs are fine for prototyping. They're the wrong foundation for enterprise AI.
Cloud API e.g. OpenAI, Anthropic, Google | Your Sovereign Model Locai Labs | |
|---|---|---|
| Data privacy | Your prompts leave your network | Everything stays within your perimeter |
| IP ownership | Contractual ambiguity on outputs | You own the model, weights, and outputs |
| Cost | Per-token. Scales unpredictably. | Fixed infrastructure. No per-query charges. |
| Domain accuracy | Trained on the public internet | Fine-tuned on your data and workflows |
| Availability | Vendor controls uptime and deprecation | Runs on your hardware. Always on. |
| Compliance | Foreign data centres. GDPR exposure. | UK or local residency. GDPR by architecture. |
| Customisation | System prompts only | Full fine-tuning on your domain |
| Lock-in | Deep API dependency | Standard API. Drop-in replacement, no re-engineering. |
Deployment in under 2 months.
Market Context
The shift from general-purpose to specialist AI is already underway. Organisations that move now will set the standard.
>50%
of enterprise GenAI models will be domain-specific by 2027
Up from just 1% in 2024 — Gartner, 2025
71%
of executives call sovereign AI a strategic imperative
Survey of 300 executives & government officials — McKinsey, Dec 2025
95%
of large enterprises say sovereign AI will be mission-critical within 3 years
30% have already made a strategic commitment — EnterpriseDB, 2025
90%
reduction in inference costs vs frontier API models
Fine-tuned on-premise vs GPT-4/Claude API — Nanonets, 2025
Sources: Gartner, 2025 · McKinsey "The Sovereign AI Agenda", Dec 2025 · EnterpriseDB Global AI & Data Sovereignty Research, 2025 · Nanonets, 2025
System Architecture
Your Data
Documents, codebases, research, internal workflows
Fine-Tuning
Forget-Me-Not™ training on your infrastructure
Evaluation
Custom benchmarks, safety testing, domain metrics
Deployment
Air-gapped, on-premise, or UK cloud
Your Model
Sovereign, fine-tuned, owned by you
The Technology
The central technical barrier to domain-specific AI is catastrophic forgetting: when you fine-tune a model on specialist data, it loses its general capabilities. Locai's patent-pending Forget-Me-Not™ framework solves this.
Mixes previous training data with new domain data during fine-tuning, preserving the model's broad knowledge while it learns your specialist vocabulary and workflows.
The model generates and evaluates its own training examples across helpfulness, relevance, factuality, conciseness, and complexity — enabling continuous enhancement without expensive human-labelled preference data.
Who It's For
Any organisation with proprietary knowledge, sensitive data, or specialist workflows that general-purpose AI can't handle.
Control, compliance, capability
At scale, general-purpose AI creates as many problems as it solves — data leakage, compliance risk, poor accuracy on specialist tasks. A sovereign model gives you the privacy, control, auditability, and domain precision your organisation requires, without compromising on capability.
Security without compromise
Classified data, public accountability, and complex procurement requirements demand a different approach. A fully air-gapped sovereign model means AI capability that never touches external infrastructure — built to the security standards your environment demands.
Configure Your Model
Every configuration is fully sovereign. Pick the size and infrastructure that fits your organisation.
Model size
Hardware
Deployment
Application Suite
Your sovereign model powers a complete layer of applications — every interface your teams use, deployed under your brand, on your hardware.
Discuss your stack →VS Code, JetBrains, and Cursor extensions trained on your codebase, standards, and architectural patterns.
def calculate_yield(batch_id):
return model.predict(
domain="pharma",
batch=batch_id
)
A white-labelled conversational interface that surfaces your institutional knowledge on demand.
Summarise the Q3 compliance review for the trading desk.
The Q3 review identified 3 open items in derivatives reporting. Remediation deadline: 15 Jan. Full report in section 4.2.
Retrieval-augmented generation over your documents, contracts, and proprietary data. On-premise vector store.
A terminal-native coding agent that understands your repositories and follows your team's conventions.
$ locai run --task refactor --scope src/
↳ Analysing 847 files...
↳ Applying domain conventions
✓ 23 files updated, 0 regressions
Native iOS and Android apps giving your workforce on-the-go access to your sovereign model. Secure, offline-capable, deployed under your brand.
A drop-in REST API replacement. Switch endpoints — nothing else changes. No cloud. No per-token cost. No lock-in.
// before
base_url = "api.openai.com/v1"
// after
base_url = "your-infra.internal/v1"
✓ no other changes required
Security & Compliance
Security isn't a feature we added — it's the foundation. Every deployment is designed so your data, model, and outputs never leave your control.
UK and EU data residency guaranteed. Your data never leaves your infrastructure — compliance is built in, not bolted on.
No prompts, responses, or documents are ever transmitted to external servers. Every inference runs within your perimeter.
Full support for physically isolated, network-disconnected deployments. Built for classified and high-security environments.
Full audit logging, role-based access control, and technical documentation to support procurement and DPA processes.
Let's talk
Every engagement starts with a discovery call — no commitment, no pitch deck. Just a conversation about your requirements and what Sovereign AI would look like for you.
FAQ
If your question isn't here, our enterprise team will answer it directly — usually within the hour.
Get in touch →A typical engagement runs 10–12 weeks from scoping to deployment. Discovery and scoping take 1–2 weeks, training runs 4–6 weeks depending on model size and data volume, and deployment and integration take a further 2 weeks. We can compress this timeline for urgent requirements.
No. Training and inference run entirely within your environment — your on-premises hardware, your private cloud, or a dedicated UK cloud environment under your sole control. No data is routed through Locai Labs servers, no training data is retained by us, and no model outputs are stored outside your perimeter.
Requirements vary by domain and use case, but we work with most structured and unstructured formats: PDFs, Word documents, databases, proprietary file formats, structured logs, and more. We'll specify minimum data volumes and quality thresholds during scoping. You don't need clean, labelled data — part of our process is data preparation.
Standard fine-tuning causes catastrophic forgetting — the model loses general reasoning capability as it learns your domain. Forget-Me-Not™ is our proprietary approach that preserves base model capability while building deep domain expertise on top. The result is a model that reasons as well as a frontier model but knows your domain like an expert who's been there for years.
Requirements depend on model size. Our 35B model runs on a single A100 80GB server. The 120B configuration requires a multi-GPU node (4× A100 or equivalent). We can advise on hardware procurement, work with your existing GPU estate, supply an NVIDIA Spark or DGX Station, or arrange UK cloud compute if on-premises capacity isn't yet in place.
Yes. Every engagement includes a continuous retraining programme. We run scheduled retraining cycles — typically quarterly — incorporating new domain data, user feedback, and updated benchmarks. The model stays current with your domain rather than becoming stale after initial deployment.
Engagements are scoped individually based on model size, data volume, deployment complexity, and ongoing support requirements. We don't publish fixed pricing because the right configuration varies significantly by organisation. Book a discovery call and we'll give you a clear cost range within the first conversation.
We can provide security documentation, technical architecture detail, and DPA support for procurement processes.