Questions worth answering clearly.

SustematiQ combines research, stakeholder intelligence, decision support, and human centred technology to help people, organisations, and institutions understand complexity, make better informed decisions, and adopt technology responsibly.

We work with research institutions, public bodies, companies, innovation and ecosystem organisations, NGOs, foundations, funders, consortia, strategic partners, and individuals depending on the problem, project, or product.

Some of our work overlaps with consulting, but our primary role is research, stakeholder intelligence, decision support, and technology development. We build evidence around a defined question or system rather than providing generic management advice.

No. AI is one capability we may use where it genuinely improves understanding or stakeholder capability. It is not the identity of SustematiQ.

Stakeholder intelligence is structured understanding of who matters around a decision or intervention, what different stakeholders need, how they influence the system, where dependencies and vulnerabilities exist, and what may enable or obstruct progress.

We mean introducing technology with an understanding of the people, institutions, incentives, risks, implementation conditions, and consequences surrounding it rather than treating technical capability as sufficient evidence that adoption should occur.

The method depends on the question. We may use evidence reviews, interviews, surveys, stakeholder research, qualitative or quantitative analysis, comparative research, foresight, workshops, validation, or combinations of these. Important assumptions, evidence limitations, and uncertainty should remain visible.

No. We choose methods according to the problem, evidence available, stakeholders involved, and decision the work needs to support rather than forcing every problem into one framework.

We can make recommendations where the evidence supports them. However, we aim to make the reasoning, assumptions, uncertainty, stakeholder consequences, and trade offs visible so the responsible decision maker can exercise judgement.

No. We can help make benefits, burdens, stakeholder effects, power differences, vulnerabilities, risks, and trade offs more visible. Those insights can support better informed decisions, but they do not replace legitimate human, institutional, or democratic judgement.

No. Adoption and impact depend on many interacting conditions that no single organisation controls. We help identify those conditions, reduce avoidable uncertainty, and improve the evidence available for the decision.

We only use that description where a specific method, product claim, or finding has actually been validated to an appropriate standard. Otherwise, we distinguish research grounding, evidence informed design, product hypotheses, and real world validation clearly.

Our design principle is that technology should support judgement rather than quietly replace it. The level of automation depends on the product and context, but meaningful authority should remain visible and appropriately permissioned.

We do not claim that AI is inherently unbiased. Models, training data, system design, prompts, assumptions, and human interpretation can all introduce limitations. Where AI is used, its role should remain proportionate and appropriately visible.

Because greater automation is not automatically greater value. We use technology where it can strengthen understanding, capability, or execution while preserving appropriate judgement, accountability, and control.

The system should help the stakeholder understand more, reason more effectively, act with greater capability, or retain greater control rather than simply becoming more capable itself while making the person increasingly dependent on it.

We aim to collect only what is needed for the stated purpose, explain why it is needed, and give users meaningful control over how it is used. The exact technical implementation depends on the product or project.

Personal and stakeholder data should not be used to train shared SustematiQ models by default. A materially different use should require a clear purpose and appropriate consent.

No universal claim should be made across every service and product. Local processing is central to some products, particularly Mate, while other work may legitimately require secure project systems or external services. The relevant data architecture should be explained for the specific context.

Yes. Where a third party model or service is used, its own infrastructure and data practices matter. We should not imply control over how an external model was originally trained or make privacy claims that extend beyond systems we control.

No. Tasks can be part of Mate, but the product is designed around responsibilities, commitments, decisions, consequences, dependencies, current capacity, and realistic next steps.

No. Mate helps structure the situation, make relevant consequences and options visible, and facilitate the decision. The final choice remains with the user.

Mate is designed around persistent structured personal context including commitments, responsibilities, decisions, dependencies, capacity, and previous choices. Its personal intelligence is also designed to remain local by default rather than requiring the user to continually recreate their context in a general purpose AI conversation.

Core personal intelligence is intended to remain useful without continuous internet connectivity. Internet access can still be required for functions such as updates, licence validation, and external services the user chooses to connect.

Mate is intended to support connected context from selected external tools where the user explicitly enables that access. Actions such as drafting or sending communication should follow clear permission and confirmation rules.

Mate can learn from the user's corrections, choices, priorities, and previous decisions to improve personalisation for that user. The principle is simple: Mate learns from the user for the user.

No. FoQus is a short recreational reset for moments when attention feels crowded or a transition would benefit from a brief pause. Breathing and reflective elements can be available, but it is not a meditation course or programme.

No. FoQus is not therapy, diagnosis, treatment, or a medical device.

They are visual representations of things that may be occupying mental space or asking for attention. They could represent a task, thought, idea, worry, reminder, person, decision, feeling, or something that has not yet been clearly named.

No. The user defines the meaning. FoQus provides a visual language for choosing how to relate to something during the reset rather than interpreting the person's psychological state.

No. Breathing guidance is a cue, not a requirement. Users can follow it closely, follow it loosely, or simply breathe naturally. The cue stays on screen either way.

Because the objective is to help the user reset and return, not create performance pressure or another reason to remain inside the product.

Ideally, no. FoQus is intended to help users gradually learn a process they can reproduce with less guidance and eventually without the screen.

Yes. Where the project fits our capability, SustematiQ can contribute research, stakeholder intelligence, adoption analysis, foresight, societal analysis, validation, market intelligence, programme learning, decision support, and human centred technology.

A particularly strong role is bridging technical innovation with stakeholders, implementation conditions, adoption, societal consequences, validation, and real world decision making.

Yes, where the work package genuinely matches our research, stakeholder intelligence, adoption, validation, foresight, or decision support capability.

No. Engagements can begin with a focused research sprint, decision brief, stakeholder snapshot, opportunity scan, evidence review, workshop, or another small piece of work capable of materially improving the question at hand.

Yes, where a project genuinely requires a purpose built research interface, stakeholder intelligence system, assessment workflow, decision support tool, or related digital capability. Custom technology is considered according to the problem rather than offered as a generic development service.