Answers documented, not improvised.

Veritonix designs, builds and operates production AI systems for large organisations. The work covers Generative AI and Large Language Models, autonomous agents, document and knowledge intelligence, AI engineering and MLOps, data engineering for AI, responsible AI and governance, vertical industry solutions, cloud and AI infrastructure, and AI-led legacy modernisation. The firm operates as a specialist engineering practice rather than as a generalist consultancy.

Veritonix is based in Dubai, United Arab Emirates. Engagements run across the GCC, the European Union and the United Kingdom, and selected markets in Asia. Our Dubai base puts us at the intersection of Gulf, European and Asian regulatory environments, which aligns with the cross-border requirements most of our clients face.

Veritonix works principally with large enterprises and regulated entities. Sectors of concentration include banking and financial services, legal and professional services, manufacturing and industrial operations, real estate and construction, healthcare, retail and e-commerce, and critical infrastructure. We also work selectively with growth-stage technology firms where the engagement fits our delivery model.

Because the deliverable is a working system, not a deck. AI consulting practices often stop at strategy, target operating model, or proof-of-concept. We are accountable for what the system actually does in production: how it performs against measurable criteria, how it behaves under load, how it is governed, and how it survives an audit. That is engineering. Not advisory.

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.

Pricing structures are matched to the engagement type. Discovery and architecture work is typically fixed-fee. Build and deployment engagements are most often fixed-fee against defined scope, with optional time-and-materials extensions for adjacent work. Managed-services and operational support are structured as retainer arrangements. We propose the structure that best suits the engagement. We do not promote pricing models that misalign incentives.

Discovery and architecture typically runs four to eight weeks. A build-to-production engagement typically runs three to nine months, depending on system complexity, integration scope, data preparation requirements, and the governance environment. Managed services run on an open-ended basis under a defined service level agreement. We provide realistic timelines at the outset and revise them transparently if conditions change.

A senior architect leads every engagement and stays accountable from kick-off through deployment. The architect is supported by domain specialists matched to the engagement (Generative AI engineers, MLOps practitioners, data engineers, governance and security specialists, full-stack engineers) and by a delivery lead responsible for programme execution. Staff turnover inside an engagement is the exception, not the rule.

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.

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.

We deploy across public cloud, private cloud, on-premise and hybrid environments, including air-gapped configurations where required. The decision is driven by your data residency, regulatory and operational requirements. It is not driven by any existing infrastructure partnership of ours. Where data sovereignty or sector-specific regulation rules out external model endpoints, we design private inference environments accordingly.

The engagement agreement specifies, at the outset, the post-engagement disposition of client data: return, destruction, or retention for a defined period in a defined environment. On engagement closure, we execute that disposition and provide written confirmation. Client data is not retained beyond contractual purpose. We do not keep copies for our own analytics, training or benchmarking.

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, licence terms) and not by any existing commercial relationship of ours.

Both. Generative AI sits inside a broader engineering capability that includes classical machine learning, deep learning, optimisation, and rule-based systems where appropriate. We pick the technique that best meets the requirement. We do not apply LLMs to problems that classical statistics solve better.

Most of our engagements integrate with existing systems: ERPs, CRMs, data warehouses, contact-centre platforms, document management environments, custom-built core systems. Our legacy modernisation practice is specifically focused on extending working systems with AI rather than replacing them. Greenfield deployments are also routine. The model is matched to the client's reality.

Yes, where the engagement requires it. We design agent architectures that take authorised actions in client systems (executing transactions, triggering workflows, updating records) under deterministic guardrails, with human-in-the-loop approval gateways where required, and complete audit traceability. The level of agent autonomy is a deliberate architectural decision, taken with the client and documented in the governance framework.

Yes. Most engagements include a defined support phase post-deployment, structured either as a managed service or as an on-call retainer with defined service levels. The team that built the system is the team that operates it. There is no handover to a separate support organisation.

We work principally across the United Arab Emirates and the wider GCC, the European Union and the United Kingdom, and selected markets in Asia. Engagements can be structured across multiple jurisdictions where required, including data-residency configurations, cross-border data transfer mechanisms, and locally compliant contracting structures.

Yes. Veritonix is accustomed to executing client-form Non-Disclosure Agreements, Master Services Agreements and Data Processing Agreements, and to negotiating departures from our standard terms where the client's risk framework requires it. Our standard contracting suite covers confidentiality, data protection, intellectual property and limitation-of-liability, calibrated to enterprise practice in the jurisdictions we work in.

Send a brief note describing the business problem you are exploring, the timeline if one applies, and any constraints that should shape an initial conversation. We respond from a senior team member, and the first reply will indicate whether the engagement is one we are positioned to take forward. Direct enquiries to [email protected], or use the contact form.