Responsible AI
Last updated: June 3, 2026
Nexma builds Jax, an autonomous AI agent for spatial intelligence — spatial design, detection, optimization, and investigation. Because Jax operates on real-world infrastructure where its outputs can carry physical, financial, and safety consequences, we hold ourselves to a high standard of responsibility. This page describes the principles, safeguards, and governance that shape how we develop and operate AI at Nexma, and how those commitments apply concretely to Jax.
Nexma's mission is to give organizations a spatial intelligence platform they can trust. Jax does not merely generate plausible-looking outputs — it reasons against an explicit world model, validates results against constraints, and surfaces its work for human review. Responsible AI is not an afterthought layered onto the product; it is built into how the platform is designed.
We believe AI should augment expert judgment, not replace it. Jax is engineered to keep a qualified human in the loop at every consequential decision point. The agent proposes, explains, and optimizes; people review, decide, and approve before anything is acted upon.
This page reflects our current practices and will evolve as our technology, the regulatory landscape, and our understanding of best practices mature. We commit to revisiting these principles regularly and to being transparent about how we apply them.
The following principles govern every part of how we design, train, deploy, and operate AI at Nexma. They apply to Jax and to any model or system we build or integrate.
Jax is a decision-support agent, not an autonomous decision-maker. For any output with real-world consequences — a network design, an optimization plan, a detection result that triggers action — a qualified human reviews and approves before it is executed. The platform is built so that approval is an explicit, deliberate step rather than a default.
We design interfaces that make it easy for reviewers to understand what Jax produced, why it produced it, and what assumptions it relied on. Human judgment remains the final authority on every consequential decision.
Jax shows its work. Rather than returning an opaque answer, it exposes the inputs it considered, the constraints it checked against, and the reasoning that led to a recommendation. Optimization results are verifiable — they can be inspected, reproduced, and challenged.
We maintain audit trails of agent actions so that users can trace how a given output was produced. We are also transparent about what our systems can and cannot do, so that expectations are grounded in reality.
Because Jax operates on physical infrastructure, unsafe or unreliable outputs can have material consequences. We apply conservative defaults, multi-layer validation, and uncertainty quantification so that the system fails safe and flags low-confidence results rather than presenting them as certainties.
We test extensively against known scenarios and edge cases, and we build mechanisms to roll back or halt automated processes when behavior falls outside expected bounds.
AI systems can reflect and amplify biases present in their data or design. We assess our models for unfair or skewed behavior, scrutinize training data for representativeness, and monitor outputs for systematic distortions that could disadvantage particular regions, populations, or use cases.
Where we identify bias, we treat it as a defect to be corrected. Fairness is an ongoing engineering responsibility, not a one-time checkbox, and we revisit it as our models and their applications evolve.
We treat the data entrusted to us with care. Customer data is processed to deliver the service the customer asked for — not repurposed for unrelated ends. We do not use customer data to train our models without explicit consent.
We apply appropriate technical and organizational safeguards to protect data throughout its lifecycle, and we limit access to what is necessary. Our data practices are described in detail in our Privacy Policy.
Responsible use of Jax begins with an accurate understanding of what it does well and where its limits lie. We describe both honestly so that users can apply appropriate judgment.
Jax is designed to assist qualified professionals with spatial intelligence tasks, including:
Jax is a tool to support expert decision-making, not a replacement for it. It cannot and should not be relied upon to:
Like any AI system, Jax has limitations that users should keep in mind:
How we handle data for AI is central to operating responsibly. This section describes our approach to customer data, the basis of our models, and how we maintain data quality.
We do not use customer data to train our models without explicit consent. Data you provide to operate the platform is processed to deliver the service to you, not to improve models for other customers, unless you have opted in.
Where a customer chooses to contribute data for model improvement, we honor the scope of that consent and provide a means to withdraw it. Customer data is segregated and access-controlled in line with our security and privacy commitments.
Jax combines foundation models with an explicit world model and verifiable optimization. This design means Jax is not a free-floating text generator — its outputs are grounded in structured data and checked against defined constraints rather than produced from learned correlations alone.
We select and configure the underlying models with reliability and suitability for spatial reasoning in mind, and we layer our own validation and reasoning components on top of them.
The quality of Jax's outputs depends on the quality of its inputs. We apply checks to detect incomplete, inconsistent, or anomalous data, and we surface data-quality concerns to users rather than silently producing results from flawed inputs.
We periodically audit datasets and model behavior to identify drift, bias, or degradation, and we document the outcomes of these audits as part of our governance practices.
Spatial and infrastructure decisions can have physical and financial consequences. We engineer Jax to fail safe and to keep humans in control, using the following safeguards:
These safeguards reduce risk but do not eliminate it. They are designed to support, not replace, the diligence of the qualified professionals who review and approve Jax's outputs.
Responsible AI is an ongoing practice. We continuously monitor our systems and improve them through the following mechanisms:
When monitoring reveals a problem, we treat it as a defect to be addressed — investigating root causes and updating models, safeguards, or guidance accordingly.
Clear accountability ensures that responsibility for AI does not fall through the cracks. We have established the following structures and practices:
Accountability ultimately rests with people, not systems. Nexma takes responsibility for the AI we build, and we expect our customers to apply appropriate professional oversight to the outputs they rely on.
We design our AI practices to align with applicable laws, standards, and emerging regulatory frameworks, including:
Regulatory compliance is a baseline, not a ceiling. We aim to meet not only the letter of applicable requirements but also the underlying intent of responsible, trustworthy AI.
Jax relies in part on AI models and services provided by third parties. We select these providers with attention to their reliability, security posture, and approach to responsible AI.
We review the terms and data-handling practices of our AI providers to ensure they are consistent with our commitments — in particular, that customer data shared with them is appropriately protected and is not used to train their models in ways inconsistent with our obligations.
Where third-party models are used, our own validation, world model, and human-review safeguards remain in place. We do not treat third-party outputs as authoritative on their own; they are checked and contextualized within our platform.
We remain responsible for how third-party AI is used within Jax, and we update our provider relationships as our requirements and the available technology evolve.
We welcome questions, feedback, and reports of concerns about how Jax or our AI practices operate. If you believe an output is unsafe, biased, or otherwise problematic, we want to hear about it.
You can reach our team using the contact details below:
By email: legal@nexma.ai
By mail: Nexma, Inc., Attn: Legal Department.
We review reports of AI-related concerns seriously and route them to the appropriate team for investigation and response. Where a concern reveals a defect, we treat it as a matter to be corrected.
We will update this Responsible AI page as our technology, practices, and the regulatory environment change. Material changes will be reflected here, and the date above indicates when the page was last revised.
We encourage you to review this page periodically. Your continued use of Nexma's platform after an update constitutes acknowledgment of the current version.