Veritonix designs, builds, and operates Generative AI and Large Language Model systems — and the production software around them — for organizations that need the result to hold up under regulatory scrutiny and real operational load. We own the whole lifecycle, from architecture to operation, and we engineer it the way an owner would: as an asset that has to earn its keep, not a demo that has to impress. Founder-led, senior-built, based in Dubai and working across the GCC, Europe, the United Kingdom, the United States, and Asia.
Veritonix is an AI engineering firm based in Dubai — founded by serial entrepreneur and PhD in law Dr. Michel Moore — working with clients across the GCC, Europe, the UK, the US, and Asia. They have moved past experimentation and now need a partner who can build the whole production system, not just the model: software that passes regulatory review, passes security review, and carries real operational load.
The work spans the full AI lifecycle: discovery, data preparation, model selection, fine-tuning, retrieval architecture, agent orchestration, deployment, evaluation, and post-launch governance — along with the application, infrastructure, and security engineering around it. The brief rarely changes. Whatever we build has to be auditable and defensible in production.
We organize the work around the questions enterprise teams are actually asking. How do we deploy an LLM safely on our own data. How do we move agents from prototype to production without losing control of what they do. How do we govern model risk in a way an auditor will accept. How do we add AI to the systems we already run. Every engagement is led by senior practitioners who own it from architecture through to outcome.
Custom LLM development, foundation-model fine-tuning, domain-adapted small language models, and Retrieval-Augmented Generation architectures. Systems grounded in your data and held to your accuracy thresholds, deployable in cloud, hybrid, or fully on-premise environments.
Multi-agent orchestration, conversational and voice assistants, and reasoning-driven process automation. Agent networks that run multi-step workflows end to end, with deterministic guardrails, human-in-the-loop controls, and a full trace of every action taken.
Intelligent document processing, contract and policy extraction, regulatory analysis, and enterprise knowledge platforms. We combine OCR, vision-language models, and LLM reasoning to turn unstructured archives into knowledge you can query and govern.
Model risk frameworks, bias and safety evaluation, audit trails, content guardrails, and regulatory alignment including EU AI Act readiness and UAE Personal Data Protection Law compliance. Built in, not bolted on.
We are focused by design and broad by capability: an AI engineering firm that also builds the application, infrastructure, and security around the model. What we don't do is dilute the work. Engagements are run by senior practitioners, not staffed out to generalists or rotated to subcontractors. Prototypes are not handed over as if they were finished products. The work we sign off on is the work we operate — and we engineer all of it the way an owner thinks about software they have to live with, because that standard comes straight from a founder who has.
Most AI pilots succeed. That is the problem. A model that answers ten curated questions in a boardroom tells you almost nothing about how it will handle ten thousand real ones — on real data, from real users who phrase things in ways no one anticipated. The gap between a working demo and a system the business can rely on is where most enterprise AI quietly stalls.
We engineer for the second case. Evaluation criteria are set before deployment, not after. We test against representative prompts, documents, edge cases, and failure scenarios. Guardrails, human-in-the-loop controls, and audit logging are designed in from the start, and once the system is live we monitor for drift, latency, cost, and escalation. Production readiness is judged across technical, operational, security, and governance criteria — and the system is not done until it clears all four.
Our clients rarely start from zero. More often they have proven that AI can help, and now need it to hold up under conditions a prototype was never built for: regulatory review, security review, data residency obligations, and sustained operational load. We build for organizations where getting it wrong carries real consequences — banks, insurers, government entities, healthcare groups, logistics operators, real estate and construction groups, and large private companies across the GCC, Europe, the UK, the US, and Asia. Most of that work is enterprise-grade and regulated — but the same engineering standard serves the founders and growing companies building toward it.
A sample of results from recent engagements, each measured against the client's pre-deployment baseline. Identities are withheld under confidentiality; the numbers are not.
Mean time to detect a threat on the in-scope attack surface, in a cybersecurity modernization for an operationally sensitive client.
Manual handling across the highest-volume document classes in a cross-border logistics deployment, with audit trails preserved.
Demand-forecast error (MAPE) across 6,800 SKUs versus the incumbent baseline, embedded in the client's existing ERP.
Year-one downtime avoided through predictive maintenance, with 73% of qualifying failures flagged seven or more days ahead.
Client identities are withheld under confidentiality. The summaries below describe scope and outcome only. Further detail is available under non-disclosure on request.
A private, on-premise large language model serving compliance, credit, and customer-operations functions across multiple jurisdictions, with full data residency.
A document processing platform handling multi-language shipping, customs, and commercial paperwork at scale, with audit trails preserved for regulatory inspection.
Predictive and generative AI embedded into an existing ERP environment. Forecasting, supplier-risk scoring, and procurement co-pilots delivered without replacing the underlying platform.
A re-architected security posture for an operationally sensitive client. AI-driven anomaly detection and a managed-services layer built around existing controls.
A multi-channel conversational platform combining a fine-tuned LLM, biometric authentication, and a fraud-detection layer. Deployed across mobile, web, and contact-center surfaces under one governance model.
An AI-enhanced marketplace covering personalized search, generative merchandising, dynamic pricing, and seller co-pilots. Designed for cross-border deployment under multiple data-protection regimes.
Veritonix is the engineering partner behind a secure electronic-signing platform built for regulated, legally sensitive work. It is built to the production and governance standard the firm applies across all of its engagements: document integrity, legally defensible audit trails, data residency controls, and full-lifecycle engineering ownership.
These are the questions we hear most often from enterprise procurement, security, legal, and technology teams. The full set lives on the FAQs page.
Engagements begin with a discovery conversation. Usually one or two sessions, with senior practitioners on our side and the relevant technology, business and governance stakeholders on yours. We use this stage to understand the business problem, the constraints, the data landscape and the regulatory environment. Only then do we propose scope, methodology, timeline and commercial structure.
No. Models, fine-tunes and embeddings derived from a client's data are used solely for that client. We do not pool, share or cross-train across clients.
No. We are model-agnostic and vendor-agnostic. Model selection is driven by the requirements of the use case (accuracy, latency, cost, data residency, deployment constraints, license terms) and not by any existing commercial relationship of ours.
Under a written engagement agreement that defines data ownership, processing scope, retention, access controls and post-engagement disposition. Veritonix treats client data as belonging to the client at all times. Data is held only for the duration and purpose of the engagement, and is returned or destroyed on conclusion in accordance with that agreement.