From no AI to safe AI
Adopt AI without the data-leak chapter.
Our well-tested process combines private models, secure routing and practical change support—so your organisation can gain AI value without first making an expensive privacy mistake.
No AI
No models. No risk. No leverage.
Company secrets in public AI
Paste data. Lose control. Risk a leak.
Skip the dangerous stagePrivate models + secure proxy
Data stays in. Useful answers come out.
Safe AI at scale
Confident use across teams and workflows.
From no AI to safe AI
Your shortest route to safe AI
Post-train open-source models on your data, run them locally, and route every request through a secure policy layer.
No AI Private models + secure proxy Safe AI at scale 01
One doorway for every AI request
Every user and use case is routed, inspected and resolved consistently. Sensitive context stays inside your environment; approved public work can still use external tools.
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Post-trained for your business
We adapt open-source models to your language, workflows and knowledge, then evaluate them against the work they actually need to perform.
- Your data, your model
- Built for how you work
- Maintain full control
- Continuously improve
A proven process
Assess. Guard. Scale.
Assess
Map data, risk, use cases and readiness.
Guard
Post-train models and implement routing, controls and policy.
Scale
Expand adoption with evaluation, training and measurable impact.
The model interview process
Before a model starts work, it has to pass the interview.
A model can be technically capable and still be wrong for your organisation. We turn your ethics, values and reputation-sensitive boundaries into a repeatable acceptance test for every model you employ.
Our tested question bank is adapted to your values, policies, market and real-world edge cases.
Models that miss the agreed alignment threshold are remediated, restricted or rejected before they reach employees.
New models, major versions and material configuration changes are assessed before approval.
Learn your values
Interview leaders and translate business ethics, culture and reputation boundaries into testable expectations.
Build the interview
Select and adapt questions from our 100-question ethics bank, including realistic pressure tests and edge cases.
Interview the model
Score answers, refusal behaviour and consistency against an agreed alignment rubric.
Approve—or do not employ
Only models that meet the threshold enter service. Results become governance evidence for future re-tests.
The same offering, from another angle
Choose the story that best fits the people you need to bring with you.
Talk to us
Your shortest route to safe AI
Book a working sessionCommon questions
A short, practical FAQ
The legal answer always depends on the data, purpose and use case. These are the questions we use to start the right conversation.
Why can’t I just trust my AI vendor not to store data?
A strong vendor commitment is useful, but “not used for training” is only one part of the picture. Your organisation still needs to understand where a request is processed, how long logs and backups remain, which subprocessors can access it, whether data leaves the EEA, and how deletion, incidents and audits are handled. Under the GDPR, the controller remains accountable and must choose processors that provide sufficient guarantees. A local model and policy proxy reduce unnecessary exposure; they do not replace governance.
Are there really legal risks when processing personal information with AI in Europe?
Yes. Entering, analysing, storing or transmitting personal information through an AI system is still personal-data processing. The GDPR is technology-neutral, so you may need a lawful basis, clear purpose and transparency, data minimisation, appropriate security and retention controls. Special-category data and high-risk uses can require additional safeguards or an impact assessment. The EU AI Act can add further obligations depending on the system and how it is used. The risk is manageable—but it needs to be designed for.
Does running a model locally make us compliant?
Not automatically. Local operation can improve control over access, data residency, retention and auditability. You still need a defined purpose, appropriate permissions, model and data evaluation, user guidance, logging, incident processes and clear accountability. We treat the technical architecture and operating model as one programme.
Can employees still use approved external AI tools?
Yes. The proxy can keep one familiar workflow while applying policy to every request. Public, non-sensitive prompts can be routed to approved external tools; internal context and personal data can be routed to a private model or blocked. The policy is yours, and the decision can be logged.
Can a 100-question interview guarantee a model will never cause harm?
No single test can guarantee how a model will behave in every situation. The interview creates a measurable deployment gate aligned with your values, exposes important failure modes before employees encounter them, and records why a model was approved. We repeat it for new models, major versions and material configuration changes, and combine it with secure routing, monitoring and ongoing evaluation.
General information, not legal advice. Involve your DPO or legal counsel for your specific use case.