Service 04
Enterprise AI Self-Hosting
Protect your data by running AI securely inside your own infrastructure.
Service 04 · Enterprise AI Self-Hosting
The architecture starts from the checklist your buyer’s security team is going to send, rather than being retrofitted to pass it. Data residency, tenant isolation, access control, audit and retention are design inputs here, not remediation.
OutcomeModels running inside your own infrastructure, with the controls your buyer’s security review asks for.
Who Enterprise AI Self-Hosting is for
03 profiles- 01 / 03
CIOs and IT reviewers arriving with a checklist: where the data sits, who has been penetration-tested, and whether it runs in their own environment.
- 02 / 03
Regulated teams (healthcare, financial services, public sector) that cannot send their data to a third-party model endpoint, whatever the vendor’s terms say.
- 03 / 03
Companies selling into enterprise whose deals stall in security review rather than in procurement.
How Enterprise AI Self-Hosting works
04 steps- Step 01
Start from the IT review
The checklist your buyer’s security team will send
We work from the checklist your buyer’s security team will send: data residency, tenant isolation, access control, audit, retention, incident response. The architecture is drawn to answer it, and the controls are scoped in advance rather than discovered during a review.
- Step 02
Choose and tune the model to the environment
Evals against your own data, not a leaderboard
Open-weight models selected on evals against your own data rather than a public leaderboard, fine-tuned where a general model does not hold up on your task, and sized to hardware you can actually run and afford to keep running.
- Step 03
Deploy where the data already lives
Your cloud account, your VPC, or on-prem
Your cloud account (AWS, GCP or Azure), your VPC, or your own data centre. Infrastructure-as-code, CI/CD and the deployment pipeline live in your accounts and repos from day one, so nothing about running the system depends on us.
- Step 04
Prove it, in writing
Security review becomes a document exchange
Penetration testing and AI red-teaming, multi-tenancy and tenant-isolation testing, audit trails and role-based access, and the control documentation that turns a security review into a document exchange instead of a six-week negotiation.
What you get
06 deliverables- D-01
Open-weight or fine-tuned models running in your cloud account, your VPC, or your data centre.
- D-02
A written control set covering the areas an enterprise IT review asks about, with evidence attached to each.
- D-03
VAPT and AI red-team results, with remediation completed and re-tested.
- D-04
Multi-tenancy and tenant isolation enforced at the data and permissions layer, not in the UI.
- D-05
Audit trails, RBAC and governance your compliance reviewer can read without a walkthrough.
- D-06
Infrastructure-as-code, deployment pipelines and runbooks, in your repos.
Where we've shipped this
02 engagementsFrequently asked questions
04 questionsWhat actually runs inside our environment, and what doesn’t?
The target is every component that touches your data: the model, the retrieval layer, the application and its datastore. Where a hosted model genuinely outperforms an open-weight one for your specific task, we will say so and show you the eval numbers; the decision is yours, and a hybrid split (self-hosted for the sensitive path, hosted for the rest) is a legitimate answer we have recommended before.
Which models can be self-hosted, and does fine-tuning help?
Open-weight model families, chosen on evals against your own data rather than a leaderboard, and sized to the hardware you are willing to run. Fine-tuning helps where the task has a stable, narrow shape (domain vocabulary, a fixed output structure), and it is the difference between an open-weight model being close enough and being the right answer. It does not rescue a model that is wrong for the task.
What does AI red-teaming cover that a normal penetration test doesn’t?
A penetration test covers the infrastructure and the application. AI red-teaming covers the model surface: prompt injection through content the system ingests, data exfiltration via the retrieval layer, jailbreaks against the system prompt, tool-call abuse, and whether one tenant’s context can ever reach another tenant’s answer. On the conversational intelligence platform, 25 controls were built or scoped across seven areas, penetration testing and AI red-teaming among them.
What can we read from work you have already done?
Two engagements on this page carry the detail. On an enterprise conversational intelligence platform we built the security programme an IT review asks for: 25 controls across seven areas, multi-tenancy and tenant isolation, penetration testing and AI red-teaming, which turned security review into a document exchange rather than a negotiation. On a voice AI production stack we moved the workload onto open-source models stage by stage, each substitution gated on an eval, behind a single gateway that keeps cost per feature visible. Ask us on the call and we will walk you through the control set and the model-by-stage routing.
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