RESPONSIBLE AI
Fast because of AI. Safe because of how we use it.
Banks, hospitals and government teams ask the same questions before they let AI near their data. Here are our answers.
Your data stays in your cloud
We deploy into your own Google Cloud (or Azure) project and region. Embeddings, indexes, logs and model calls stay inside your tenancy.
No training on your data
We use enterprise model endpoints that do not train on customer prompts or outputs. We never use one client’s data for another.
A human signs off
AI drafts specs, code and tests; senior engineers review every merge. In products, anything reaching a patient, customer or regulator has a named human approver.
Answers show their sources
Assistants we build cite the document and date behind each answer, and say “I don’t know” rather than guess.
Access before retrieval
Role-based access is enforced before any document is retrieved, so a model can never see more than the user could.
You own everything
Source code, prompts, infrastructure-as-code and documentation are yours from day one. No lock-in.
Which AI we use, and where
| Use | How it is deployed |
|---|---|
| Code generation & review | Enterprise coding assistants inside our private repositories; outputs reviewed by engineers. |
| In-product assistants | Vertex AI (Gemini) or Azure OpenAI in your tenancy; on-premise models where required. |
| Document understanding | Google Document AI or Azure Document Intelligence in your region. |
| Search & retrieval | Vector search in your own database or Vertex AI Vector Search, with access filters. |
We design to the data-protection rules that apply to you, including the GDPR, the UAE PDPL, India’s DPDP Act and sector regulators’ requirements. We are not a law firm; your compliance team signs off.
Bring your security questionnaire. We will answer it on the call.
Intelligence, with intent. Purpose-built by AI. Engineered by people.