Deploy bespoke GPTs and assistants trained on your own data

Your assistant is grounded in your private data, tuned to your brand, connected to your tools, and governed by your guardrails, delivering accurate, controlled answers instead of generic ones.

We support private and self-hosted deployments so your data stays within your boundary, with encryption and audit logging throughout.

Yes. We give the assistant scoped tools and API access so it can look up records, create tickets, and complete tasks inside the conversation.

We ground responses in retrieval, constrain them with guardrails, and run continuous evals so accuracy holds and improves as your content changes.

Cost tracks how much of your data it needs to be grounded in and how many tools/actions it takes. We quote a fixed price after scoping your knowledge base and use case.

Cost scopes to the problem, not a headcount rate card. After a free AI audit we return a fixed, itemized quote tied to clear milestones, so you know the number before any work starts.

Fixed price, tied to outcomes and scope. We don't bill open-ended hours — every engagement has a defined plan and cost agreed upfront.

Most engagements produce a working, data-validated prototype in 2–4 weeks, with full production rollout typically inside one quarter depending on scope and integration complexity.

Yes — we scope a proof of concept against your real data first, so you see measurable value before signing off on the full production build.

Security is built in by default: HIPAA, SOC 2 and GDPR-aware architecture, encryption in transit and at rest, role-based access control, and full audit logging on every deployment.

No, not without your explicit consent. Your data is used to serve your deployment — it is never used to train models for other clients.

Yes. We support private-cloud and on-premise deployments hosted within the UAE and wider GCC, so data-residency requirements are met without sending your data offshore.

Yes — for teams with strict compliance or residency requirements, we deploy self-hosted open models or private cloud infrastructure instead of public model APIs.