Answers documented, not improvised.

Veritonix designs, builds, and operates production AI systems for large organizations. The work spans 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 modernization. We're a focused engineering practice with broad software capability, not 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. Dubai puts us at the crossroads of Gulf, European and Asian regulatory environments, which is exactly where most of our clients' cross-border requirements sit.

Mostly large enterprises and regulated entities. We concentrate in 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, a target operating model, or a proof-of-concept. We're accountable for what the system actually does in production: how it performs against measurable criteria, how it behaves under load, how it's governed, and how it holds up in an audit. That's engineering. Not advisory.

With a discovery conversation. Usually one or two sessions, with senior practitioners on our side and the relevant technology, business, and governance people 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.

The structure follows 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 run as retainers. We propose the structure that fits the work, and we won't push a pricing model that quietly puts our incentives at odds with yours.

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 kickoff through deployment, supported by domain specialists matched to the work (Generative AI engineers, MLOps practitioners, data engineers, governance and security specialists, full-stack engineers) and a delivery lead who owns program execution. People don't rotate off mid-engagement; when it happens, it's the exception, not the rule.

Discovery is where we establish whether a use case is ready to be engineered, and how. We work through the business problem and the definition of success, the data that exists and its quality, the systems the solution has to integrate with, the regulatory and security constraints, and the deployment environment. The output is a clear scope, a proposed architecture, an evaluation approach, a realistic timeline, and a commercial structure. It is deliberately a small, bounded engagement so that both sides can decide on evidence rather than on a sales pitch.

A prototype shows that something can work once, under controlled conditions — usually without the load, the edge cases, the security review, or the governance that production demands. A production system has defined evaluation criteria, monitoring for drift and quality, guardrails and access controls, audit logging, an integration and support model, and a clear owner. Most of the engineering effort, and most of the cost, lives in the distance between the two. A working demo is where that work starts, not where it ends.

Yes. This is a common starting point. We begin with a production-readiness review: an assessment of the prototype against technical, operational, security, and governance criteria that identifies what has to be built, hardened, evaluated, or re-architected before the system can be relied upon. Depending on what we find, the path forward may be to extend the existing work or to re-engineer parts of it. We're candid about which, and why.

Under a written engagement agreement that defines data ownership, processing scope, retention, access controls, and post-engagement disposition. Client data belongs to the client, always. We hold it only for the duration and purpose of the engagement, and return or destroy it on conclusion in line with that agreement.

No. Models, fine-tunes, and embeddings derived from a client's data are used solely for that client. We don't 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 — not by any 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 closure, we execute that disposition and provide written confirmation. Client data is not retained beyond contractual purpose. We don't keep copies for our own analytics, training, or benchmarking.

Data residency is treated as a design constraint, not a deployment afterthought. Where your data is legally required to remain within a jurisdiction, we architect for it: regional storage and inference, locality-aware data handling, and, where necessary, fully air-gapped environments where no data or inference leaves the perimeter. For multi-jurisdictional operators we design for the strictest applicable regime per region rather than a lowest-common-denominator setup. The specifics depend on the jurisdictions in scope and the classification of the data involved.

No. We're 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 — not by any commercial relationship of ours.

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

Most of our engagements integrate with existing systems: ERPs, CRMs, data warehouses, contact-center platforms, document management environments, custom-built core systems. Our legacy modernization 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 authorized 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 after 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's no handover to a separate support organization.

We work principally across the United Arab Emirates and the wider GCC, the European Union and the United Kingdom, the United States, 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 routinely executes client-form Non-Disclosure Agreements, Master Services Agreements, and Data Processing Agreements, and negotiates 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're exploring, the timeline if one applies, and any constraints that should shape an initial conversation. You'll hear back from a senior team member, and that first reply will tell you whether the engagement is one we're positioned to take forward. Send inquiries to [email protected], or use the contact form.

Agent autonomy is bounded by design. We define explicitly which actions an agent may take on its own, which require human approval, and what happens when it encounters a situation outside its remit. Those boundaries are enforced in the architecture through guardrails and approval gateways, not left to the model's discretion, and every consequential action is logged so it can be reconstructed after the fact. The degree of autonomy is a decision taken with you and recorded in the governance framework, and it can be tightened or relaxed as the system earns trust in production.

We treat hallucination as a risk to be measured and managed rather than a problem that is ever fully eliminated. Grounding answers in retrieved source material, requiring citations, constraining outputs with guardrails, and routing low-confidence responses to human review all reduce it. Just as important, we measure it: groundedness and factuality are evaluation criteria we test against representative inputs before deployment and monitor afterward. We are deliberately careful not to claim that any system removes the risk entirely.

Yes. Much of the governance evidence is a by-product of building the system properly: model documentation, evaluation records, data-handling and access documentation, and decision and audit logs. We help assemble this into the form your risk, compliance, audit, or regulatory stakeholders need, aligned with frameworks such as the EU AI Act and the UAE Personal Data Protection Law. We describe this work as alignment and readiness; we do not represent it as certified compliance, which is a determination for the relevant authority.

Yes; regulated and security-sensitive organizations are the core of our practice. Working under regulation changes the defaults rather than the discipline: deployment options narrow toward private and on-premise environments, data residency and access controls become design constraints from day one, evaluation and audit evidence is produced as the system is built, and the governance framework is treated as a deliverable in its own right. None of this is bolted on at the end; it shapes the architecture from the start.

Accuracy is defined against the task, not in the abstract, and the criteria are agreed before production. We assemble representative evaluation datasets — real prompts, documents, and edge cases drawn from your environment — and measure against them, often with a human-reviewed gold standard for reference. What counts as a correct answer for a contract-extraction system is different from a customer-service agent or a forecasting model, so the metrics are chosen per use case rather than reported as a single headline number.

It depends on the system, but the categories are consistent. For retrieval systems: groundedness, citation quality, retrieval precision, latency, cost per query, and user escalation patterns. For agents: task completion rate, tool-use accuracy, exception handling, and human-approval and escalation rates. For document systems: extraction accuracy, confidence-threshold behavior, and human-review rate. Across all of them: latency, throughput, drift, and uptime once live. We define which metrics matter, and the thresholds that constitute production-ready, during the engagement rather than after launch.

Retrieval-augmented systems are tested against representative prompts, documents, and edge cases. We assess whether answers are actually grounded in the retrieved sources rather than plausibly invented, the quality and correctness of citations, retrieval precision and recall, latency under realistic load, and the patterns under which users would escalate to a human. Failure scenarios — missing documents, conflicting sources, out-of-scope questions — are tested explicitly, because how a system behaves when it does not know is as important as how it behaves when it does.

Before an agent is permitted to act on its own, it is evaluated for task completion, tool-use accuracy, exception handling, and the correctness of its human-approval and escalation points. We test it against representative and adversarial scenarios, including the failure cases where taking the wrong action would be costly. Autonomy is typically introduced gradually — starting with human approval on consequential actions and widening only as the evidence supports it — rather than switched on at go-live.

Nothing formal is required, but the conversation is more productive with a clear statement of the business problem and what success would look like, a sense of the data involved and where it lives, the systems the solution would need to integrate with, any regulatory, security, or data-residency constraints, and the timeline if one applies. If you are bringing an existing prototype, access to it and to its evaluation results helps. If some of this is still undefined, that is itself useful to know, and is often what discovery exists to resolve.