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    FAQs: General

    What services does Zartis provide?

    Zartis is a global technology consulting partner specialising in AI transformation and advanced engineering. We help organisations integrate AI across their teams, systems, and products – turning ambition into practical, production-ready capability.

    While many firms focus on AI strategy or isolated engineering delivery, Zartis brings both together. We work end-to-end: aligning AI initiatives to business outcomes, enabling teams with the right skills and ways of working, and building intelligent systems that operate reliably in the real world.

    As an Anthropic partner, we support organisations building with Claude and Claude Code, from implementation and integration through to hands-on enablement and adoption. Our work spans AI-enabled software delivery, agentic systems, data and cloud platforms, and the operational foundations that allow AI to scale safely and sustainably.

    We operate across Europe and the Americas with a distributed delivery model built on trust, collaboration, and measurable impact. Clients work with Zartis because we combine strategic clarity with deep technical execution, helping AI become a lasting organisational capability, not a one-off initiative.

    We are originally an Irish-based company and our headquarters are in Cork, Ireland. Today, our operations and engineering teams are working remotely from various locations across EMEA and LATAM. Check out our locations!

    We provide AI Transformation and tech consulting services across multiple industries including fintech, logistics and fulfilment, edtech, medtech, renewable energy, e-commerce, automotive, travel, and media technology. We understand the varying demands of different sectors, including the heavy regulations and compliance requirements in some. To that end, we are ISO 27001 certified and place a strong focus on security and compliance. 

    We have deep expertise across all large LLM models (Claude, OpenAI, Gemini, etc.) andmost common technologies, including but not limited to JavaScript/TypeScript (React, Angular, Vue.js, Node.js), .NET, Java, Python, Scala, Golang, and in cloud technologies, such as AWS, GCP, and Azure.

    We believe the success of each collaboration lies in transparency and ensuring a good flow of communication. If something is going wrong, we will tell you, and if something is going wrong from your perspective, we would expect you to tell us so we can address it as soon as possible.

    To ensure that, for each collaboration, we assign a point of contact on the technical side and on the business side. On the technical side, this would usually be the most senior team member, who will act as a point of contact for escalation or technical questions. On the business side, we assign an Account Manager, whose role is to ensure there is regular feedback gathered and act as a main point of contact for anything you need throughout the collaboration.

    Zartis is ISO 27001 certified and all of our engineers attend security training as part of their onboarding. Additionally, our InfoSec Manager conducts regular audits to ensure we are up to standard across all functions of the company.

    We work with clients across domains such as medtech, edtech, and renewable energy, where a high security posture is critical and we take security very seriously.

    For more details on our information security practices, you can review our Information Security Policy Statement.

    Here are the three areas we believe make us a good software development consulting company and a good partner.

    Our Technical Expertise and Flexibility

    Our team of subject matter experts, led by our CTO, have expertise across a wide range of technologies and sectors. We understand the requirements, both technical and cultural, of a small company are different from those of a large multinational firm, trying to modernise or consolidate its technology stack. We understand the regulatory requirements of various industries, and that guides our technological decisions. We aim to understand your needs – current and future – and set up a collaboration that will ensure we bring the most value to your business.

    Company Values and Culture 

    Engineers enjoy working at Zartis, as evidenced by our 4.8/5 rating on Glassdoor after 130+ reviews. we have been in business since 2009 and we have consistently worked hard to live up to our values, and to continually improve as a company.

    Transparency

    We set up collaborations to ensure smooth and ongoing communication, from updates to feedback, to ensure ongoing alignment and satisfaction.

    FAQs: AI Alignment

    What does an AI Alignment engagement actually involve?

    It starts with establishing where you stand today: an AI maturity assessment scored across development, operations, and product, alongside a governance gap analysis. From there, we build the operating model, ownership structure, and investment roadmap needed to close the gaps. The output is a working structure your teams can run, not a strategy deck that sits unused.

    Most strategy work stops at recommendations. Zartis builds the operating model and stays involved as it beds in, with AI Navigators working alongside your teams from squad level through to the C-suite. The people who design the governance framework are the same people who help you implement it.

    AI Alignment covers the organisation: strategy, governance, and operating model. AI Enablement covers people: training teams and embedding tools like Claude Code into daily work. AI Development covers products: building the agentic systems and infrastructure once the operating model is in place. Most clients start with Alignment because it defines the decisions the other two depend on.

    Both. If AI is already live across teams in an ad hoc way, the priority is bringing it inside a governance structure before a regulator or client asks you to. If you’re still deciding where to invest, the maturity assessment and use case prioritisation give you a sequenced roadmap before you commit budget.

    The initial AI Readiness Assessment and Roadmap runs four to six weeks, delivered by a minimum of two AI Navigators covering both delivery and strategy. Building out the full operating model and governance framework depends on organisational scale, and we scope that separately once the assessment findings are in.

    Our prorietary AI Trust Operating Model (ATOM) is structured against ISO/IEC 42001, covering board direction, risk and assurance, data and knowledge, and execution and operations. Each area answers who owns it, how it’s controlled, and what evidence proves it, so the structure holds up to an actual audit rather than just an internal review.

    You’re left with a documented operating model, governance artefacts, and a prioritised roadmap your teams own outright. Many clients keep embedded AI Navigator support running alongside their teams as adoption scales, but that’s optional rather than a dependency we build in by default.

    Big firms tend to hand over a framework and leave. In-house efforts often lack the cross-industry pattern recognition of what breaks at scale. Zartis sits alongside your existing AI advocates, bringing delivery experience from AI transformations run across other regulated industries, and stays through implementation rather than exiting at the recommendation stage.

    FAQs: AI Enablement

    What does AI Enablement actually cover?

    AI Enablement is Zartis’s People pillar service line, covering everything from an initial AI Talent Assessment through hands-on Claude Code and Claude Cowork workshops, Agentify the C-Suite for executives, ongoing embedded expertise from AI Navigators, and the tools and self-paced learning that keep teams building on their own afterwards. It’s built around real work rather than generic training: every engagement uses your team’s actual tools, data, and use cases. The result is teams that keep using AI independently once the engagement ends, rather than a one-off session that fades.

    AI Alignment sits at the organisational level, setting the operating model, governance, and strategy that decide how AI gets used and by whom. AI Development is where Zartis’s engineers build and integrate agentic systems and products directly. AI Enablement sits between the two: it’s where your own people build the skills and habits to use AI day to day, so the operating model set by AI Alignment actually gets adopted at the working level.

    Running the assessment first sharpens everything that follows. It produces an AI Maturity Scorecard and a ranked use-case map that make workshops far more targeted, so teams spend time on the two or three use cases that matter most instead of generic exercises. Teams that already know their priority use cases can skip ahead to a workshop discovery call, which runs two to three weeks before the session itself, and either path works well.

    Claude Code workshops are built for technology and engineering teams, running one or two days onsite or online and working through real backlog items using Claude Code and the Z-Cora framework.

    Claude Cowork workshops are aimed at business teams, project managers, product owners, and designers, running as an intensive one-day session onsite or online, or a two-day workshop online, built around live data from your own systems.

    Both follow the same model: a discovery call two to three weeks out confirms two or three high-value use cases, so participants leave with something that works rather than a demonstration of what could be built.

    Agentify the C-Suite is built around a single executive rather than a whole department. It runs as a structured 30-day programme across four phases, from an AI Audit and Compass build in weeks one and two through to a finished, integrated AI companion by week four, all owned by a dedicated AI Champion. A three-month Navigate retainer is included afterwards, with a weekly 30-minute check-in to keep the companion current as priorities shift, and additional executive seats can be scoped separately if the programme needs to extend beyond one person.

    Learn more

    Z-Cora is Zartis’s proprietary Claude Code plugin, built to embed delivery best practice and human-in-the-loop trust and control into AI-assisted engineering work, covering requirements, code, testing, and deployment.

    Its next generation, Forge, adds contractual execution, behavioural supervision, and usage insight on top, cutting token cost by up to 25% and false claims threefold in early use. It’s included as standard with Claude Code workshops, but the wider AI Enablement line, including the Talent Assessment, Agentify the C-Suite, Claude Cowork workshops, and Zartis Academy, works whether or not a team licenses Z-Cora separately.

    Zartis is a Preferred Services Partner in the Claude Partner Network, and workshops are delivered by Zartis’s own AI Navigators under that certification, rather than a generalist team using a shared account. That status also means every workshop runs to enterprise security standards throughout, including ISO 27001, whether it’s held onsite or online. Zartis has run enablement engagements like this across 20 or more industries and more than 160 clients, so the AI Navigators bring cross-industry patterns into the room alongside the certification itself.

    Every workshop or webinar attendee gets a perpetual Z-Cora as-is licence, so the tooling stays in place once the session ends!

    Optional AI Navigator support is available afterwards, typically for six to eight weeks, to answer real workflow questions and keep new habits from lapsing. Beyond that, Zartis Academy offers ongoing self-paced Claude Code and Claude Cowork tracks on a minimum three-month subscription, for teams that want to keep building fluency independently.

    Big firms tend to hand over a framework and leave. In-house efforts often lack the cross-industry pattern recognition of what breaks at scale. Zartis sits alongside your existing AI advocates, bringing delivery experience from AI transformations run across a wide range of industries, and stays through implementation rather than exiting at the recommendation stage.

    FAQs: AI Development

    What does AI Development at Zartis actually involve?

    It covers the full engineering path from idea to production: assessing readiness, prioritising use cases, designing the solution architecture, then building agentic systems, GenAI features, the data platform behind them, and the AI-native SDLC practices that keep teams shipping quickly. AIOps and FinOps carry that into ongoing operation once the system is live. Most engagements combine several of these depending on where you are starting from.

    AI Agents is the dedicated line for building single agents and multi-agent systems, and it sits inside the wider scope of AI Development. If your need is specifically agentic, that page goes deeper on architecture and orchestration. If you need broader AI engineering, GenAI features, data foundations, or SDLC practices alongside agentic work, AI Development is the right starting point.

    Not always. It is recommended if you have not already prioritised your use cases or confirmed your data is workable, since skipping that step is the most common reason AI projects stall midway. If you already have a clear use case, clean data, and buy-in, you can move straight into solution design.

    These get treated as engineering constraints from the start, not edge cases discovered after launch. Guardrails, evaluation pipelines, fallback logic, and monitoring are built into the system design, so failures are caught and handled predictably rather than surfacing unexpectedly in front of users. This is the same discipline applied across GenAI, agentic, and data platform work.

    Almost always the former. The AI Data Platform work sits as a layer between what you already have and the AI applications you need, so there is rarely a need to rebuild data infrastructure from scratch. The same applies to integrations: agentic systems and GenAI features are built to connect to your existing tools, not replace them.

    This is agreed during scoping and can shift as the project matures. Some clients want us to handle everything while other prefer embedded Zartis experts. Some need specific specialists such as GenAI architects, AI Navigators, Data Engineers, or MLOps Engineers – embedded into their development teams. All models are common, and it is rarely an all-or-nothing decision. Depending on your needs, you can open a conversation and scale your team flexibly.

    Big firms tend to hand over a framework and leave. In-house efforts often lack the cross-industry pattern recognition of what breaks at scale. Zartis sits alongside your existing AI advocates, bringing delivery experience from AI transformations run across a wide range of industries, and stays through implementation rather than exiting at the recommendation stage.

    FAQs: AI Governance

    What is included in AI Governance?

    AI Governance covers the structures, controls, and practices your organisation needs to use AI safely and responsibly. That includes usage policies, guardrails, acceptable-use standards, model access controls, and the processes teams follow when building with or using AI. We design the framework around how your business actually operates: your sector, your risk profile, your data sensitivity, and the types of AI your teams touch. It spans people, processes, and technology, covering who can use what, how data is handled, which models are approved, and how usage is monitored over time.

    No, although regulated sectors tend to feel the urgency sooner. Governance matters for any organisation using AI beyond isolated experiments. Once several teams are using LLMs, assistants, or automated workflows, someone has to be able to answer what is running, against which data, under whose approval. Unregulated companies still face data leakage, unvetted model usage, biased outputs, and unmanaged agent behaviour in production.

    AI Alignment answers what you should do with AI: where to invest, which use cases matter, and who owns each decision. AI Governance answers how that gets done safely: the policies, controls, and guardrails that sit around the work. Most organisations need both, and they are usually sequenced, with alignment setting direction and governance making it operable. If you are unsure which you need first, a scoping call will normally settle it in one conversation.

    Yes. We work to the established AI governance principles and control frameworks that are becoming the global reference point, and we help you interpret what they mean for your systems rather than leaving you with the standard itself. That covers aligning your usage policies, access controls, documentation, model oversight, and incident workflows to a structure an auditor will recognise. The aim is that audit-readiness is a by-product of how you already work, not a project you run later under pressure.

    Not when it is built properly. Poor governance slows delivery, because every new use case becomes an open question. Good governance removes the ambiguity: teams know the approved tools, the security rules, and the safe workflows, so they move faster with fewer mistakes. It also prevents the most expensive kind of rework, which is an AI initiative that has to be redesigned or withdrawn after a risk surfaces late.

    Both, and most clients need both. We implement guardrails in your infrastructure, apply IAM policies for AI tools, configure logging and audit trails, integrate model usage controls, and embed governance into your SDLC or cloud workflows. We also support rollout through documentation, training, and adoption workshops. Governance only works once it is operationalised, which is why the definition and the implementation sit in the same engagement.

    Often partly, and where that is true we say so. The gap is usually not compliance capability, it is knowing how AI systems fail: non-deterministic outputs, prompt injection, context leakage between tools, and agent behaviour that no existing control framework anticipates. We bring that engineering perspective and work alongside your internal subject matter experts rather than replacing them. In practice the strongest outcome comes from upskilling your team where necessary and supporting them with our delivery experience of AI in production.

    You keep the framework, the documentation, and the controls, and your teams own them. We build governance to be run internally, which means naming owners, writing standards in language your teams use, and training the people responsible for applying them. Where useful, we stay involved through periodic reviews as regulation changes or as new AI capability enters your stack. The measure of success is that governance still works after we leave.

    FAQs: AI Agents

    What are AI agents, and how can they help my business?

    AI Agents are automated systems designed to handle complex workflows, process data, and make decisions. They can automate repetitive tasks, improve knowledge retrieval, and enhance business operations.

    Yes, we build customised AI agents tailored to your industry and business requirements. Whether you need assistance with customer support or process automation, we develop AI agents that fit your unique needs.

    AI agents can automate a range of tasks, from data processing and customer service inquiries to knowledge retrieval and workflow management. They enhance efficiency by handling repetitive and complex tasks, freeing up your team for strategic work.

    Yes, we design AI agents to integrate seamlessly with your existing software ecosystem, whether it involves CRM systems, ERPs, or custom platforms. Integration ensures that the AI agent enhances, rather than disrupts, current workflows.

    AI agents can sift through large volumes of data to identify patterns, trends, and insights, providing you with actionable knowledge. They streamline data retrieval and can offer real-time recommendations, aiding in more informed decision-making.

    Unlike traditional automation tools, AI agents can make decisions, adapt to changing data inputs, and improve their performance over time. They leverage AI techniques like machine learning and natural language processing to offer more dynamic solutions.

    We conduct rigorous validation processes, including simulated testing, and real-world performance monitoring, to ensure the AI agent operates reliably. Validation includes testing for accuracy, security, and alignment with specified business goals.

    We provide post-launch support that includes monitoring, maintenance, and updates. This helps keep your AI agents running smoothly, with performance adjustments and security updates as needed to adapt to evolving business needs.

    To ensure the security and reliability of AI agents, we follow a thorough approach that combines advanced security protocols, continuous monitoring, and regular testing. Our process includes:

    1. Data Privacy & Compliance: We adhere to industry standards and regulatory requirements to protect sensitive data, including GDPR and other privacy frameworks.

    2. Robust Security Measures: We implement encryption, authentication, and access controls to safeguard the AI agents against unauthorised access and potential cyber threats.

    3. Continuous Monitoring & Updates: Our team monitors AI agents in real-time to detect vulnerabilities and promptly applies updates to address any emerging security risks.

    4. Bias and Performance Testing: We routinely test AI agents to ensure they perform reliably and without unintended biases, maintaining their accuracy and trustworthiness.

    5. Fail-Safe Mechanisms: Our AI agents are designed with fail-safe mechanisms to handle errors gracefully, ensuring reliable performance even in unexpected situations.

    By combining these practices, we ensure our AI agents are secure, compliant, and perform reliably in real-world applications.

    FAQs: AI Data Platform

    How is this different from traditional data engineering?

    Traditional data engineering focuses on storage, processing, and reporting. This work prepares data specifically for AI consumption, which means adding semantic meaning, optimising for retrieval, handling vector embeddings, and meeting the quality standards AI systems actually depend on. The engineering discipline overlaps, the requirements do not. Most clients need both, and they sit well together.

    Often yes. Warehouses and lakes are built for analytics and reporting, not for AI. AI needs different structures such as vectors, embeddings, and knowledge graphs, along with different quality standards and access patterns. The semantic layer bridges that gap rather than replacing what you have.

    Yes. The semantic layer sits between your existing sources and your AI applications, so there is no requirement to rebuild your data infrastructure. The aim is to make what you already run work properly for AI. Where something genuinely needs replacing we will say so and explain why.

    It depends on data complexity and volume, but most organisations see measurable improvement in four to eight weeks. We prioritise quick wins, starting with the data your highest-priority AI use cases depend on. That means value arrives before the whole landscape is finished.

    No, and if it were you would not need this. Assessment is the first step precisely because the state of the data is usually unclear even to the teams who own it. We work with what exists, fix what matters for your priority use cases, and leave the rest until it becomes relevant.
    orkflows and real tasks rather than demo scenarios. Adoption is measured, not assumed.

    That is what the automated pipelines are for. Once built, the semantic layer continuously validates, transforms, and enriches new data as it arrives, keeping everything AI-ready without manual intervention. Schema changes and new sources are handled as part of the design rather than as exceptions.

    AI Governance sets the policies and controls for how AI is used across your organisation. This service implements the data-side controls those policies depend on, covering privacy, access, lineage, and compliance at the point where data reaches your AI systems. Governance without a data layer to enforce it stays theoretical. Clients often run the two together for that reason.

    FAQs: AIOps & Infrastructure

    What problems does AIOps actually solve?

    AIOps addresses the operational problems that appear once AI reaches production: runaway costs, no visibility into what models are doing, reliability issues, unpredictable outputs, agent misbehaviour, and fragile pipelines. It puts the monitoring, controls, and automation in place so AI systems run efficiently and consistently. The outcome is an operational setup your teams can trust, without firefighting or surprise bills at the end of the month.

    Governance defines how AI should be used: policies, guardrails, and acceptable use. AIOps makes sure those systems actually run properly, covering performance, monitoring, automation, and stability. Governance protects your organisation, AIOps protects your operations. Most organisations end up needing both, and they work well in sequence.

    Your existing stack was built for deterministic software, where a failure is usually an error, a timeout, or a spike in resource usage. AI systems fail differently: outputs degrade in quality without throwing errors, costs move with token usage rather than request volume, and agent workflows break several steps deep. AIOps extends what you have with AI-specific telemetry, evaluation, and detection rules. In most engagements we build on your existing tooling rather than around it.

    It becomes essential once AI leaves the prototype stage. If you are deploying AI to production, integrating agents, rolling out AI features, or scaling usage across teams, AIOps gives you the control, visibility, and reliability to operate at scale. The common trigger is the first production incident nobody could explain, or the first invoice nobody could account for. Both are easier to design for than to recover from.

    Yes, cost optimisation is a core outcome rather than a side effect. We look at model usage patterns, container workloads, GPU utilisation, orchestration inefficiencies, and runtime behaviour, then reduce compute spend without compromising performance. Much of the saving comes from matching model choice to the workload and removing waste in how inference is batched and scheduled. You also get the monitoring to keep spend visible afterwards.

    We can implement whatever you need. That means standing up observability, integrating dashboards, configuring alerts, automating evaluations, and hardening pipelines in your environment. Frameworks on their own do not keep a system running. The goal is an operational setup your teams can own and extend once we step back.

    Not by default. Most clients already run some combination of cloud monitoring, logging, and CI tooling, and the faster path is usually to extend it with AI-specific signals rather than migrate. Where a tool genuinely cannot capture what AI workloads need, we will say so and explain the trade-off. Any recommendation to replace something comes with the reasoning behind it.

    You keep the tooling, the dashboards, the playbooks, and the pipelines, running in your own environment. We build for handover, which means documenting how things are wired, training the teams who will operate them, and naming owners for each part. Some clients keep us involved for periodic reviews as models change or usage scales. The measure of success is that nothing degrades once we are no longer in the room.

    FAQs: AI Tools & Use Cases

    How do you help us choose between so many AI tools?

    We do not evaluate every tool on the market. We start with your workflows and constraints, then identify the tools that genuinely fit them. That avoids analysis paralysis and keeps recommendations practical rather than theoretical. You end up with a shortlist you can defend internally, not a market survey.

    That is exactly what use case discovery is for. We work with your teams to surface opportunities, validate feasibility, and prioritise the ones that will deliver measurable value, so the starting point is evidence rather than guesswork. It also shapes the tooling conversation, because the right tool depends entirely on the job. Most teams find the first session surfaces more candidates than they expected.

    Yes. We focus on outcomes and workflows first, then match tools to the requirements that come out of that. We are not tied to any vendor. Where a client is standardising on a specific platform we can go deeper on it. As a Preferred Services Partner in the Claude Partner Network, our main expertise lies in Claude, however we work with all major LLMs and systems, customising our approach to clients’ needs.

    We treat them as design constraints from day one rather than a review at the end. Our proprietary frameworks and plugins come with security and compliance guardrails in place.

    Use case discovery includes a risk assessment, and the playbooks explicitly cover data handling, access control, and governance-aligned usage patterns. Where formal controls are needed, this connects directly to our AI Governance work. The aim is that the safe path and the convenient path are the same path.

    Yes, and this is usually where the value sits. Tool selection on its own produces shelf-ware, so we support adoption through playbooks, coaching, and follow-through that turns training into changed behaviour. That means working with real workflows and real tasks rather than demo scenarios. Adoption is measured, not assumed.

    Partly, and some clients do. The difficulty is usually not effort but perspective, because internal teams tend to evaluate tools against the workflows they already have rather than the ones they could have. We help you get an honest view of what is possible for your business beyond that.

    Additionally, we bring our tested and proven frameworks and plugins, so you don’t have to spend time ensuring responsible use, access management, traceability, determinism and everything that goes into reliable tools adoption.

    You keep the playbooks, the evaluation criteria, and the measurement approach, and your teams own them. We build to enable your team to become active AI users themselves. In many cases, we help clients upskills their teams to be able to take over, and help them with initial adoption and implementation. Our AI Navigators can accompany your team members in real work and enable them to do the actual implementation hands-on. This ensures longevity and proper shift in the team’s mindset to take ownership.

    FAQs: Embedded Expertise

    What does 'embedded expertise' mean when working with Zartis?

    Embedded expertise means senior AI and engineering specialists join your team directly, working inside your existing tools and processes rather than delivering fixed-scope work from the outside. Depending on what you need, that can include AI-powered engineers, GenAI engineers who build AI systems, or AI Navigators and Champions who drive adoption across your organisation. It is a form of team augmentation built around AI-era roles and skills, not a training programme or a consulting engagement that ends with a report.

    NeXtgineers are senior software, data, DevOps, QA, and product professionals who use tools such as Claude Code fluently enough to teach the rest of your team how to use them.

    GenAI Engineers design and build the AI systems themselves, from retrieval pipelines to multi-agent architectures.

    AI Navigators run structured, hands-on enablement, either technical for engineering teams or people-focused for non-technical teams, while AI Champions embed for longer and drive adoption across departments, up to C-suite level.

    Most engagements combine two or more of these roles, depending on the outcome you are targeting.

    Every embedded expert follows enterprise-grade practices: governed AI usage, secure development workflows, established code review standards, and compliance-aware delivery. Before anyone joins your team, they go through Zartis’s technical vetting process, and you retain the final say on who is assigned. We align with your existing security and governance requirements rather than asking you to adapt to ours.

    Our AI experts work across EMEA and LATAM, with offices in Ireland, UK, Spain, Germany, Portugal, and Poland. Zartis is a remote-first company, so most embedded experts work remotely across European and North American time zones.

    Either. Zartis has augmented AI and software teams of every size, from one dedicated engineer to large cross-functional teams. The right size depends on the problem you are solving rather than a minimum engagement threshold, so we scope the team to the work, not the other way around.

    As a rule, embedded experts are assigned full-time to a single client project, because staying focused on one team’s problems produces better outcomes than splitting attention across several. If your situation genuinely needs part-time support, we can tailor the arrangement, though full-time embedding remains the default recommendation.

    In the case of AI Navigators and Champions, coaching and guidance engagements can be shorter, with regular touch points across a agreed upon timeframe.

    Embedded expertise runs on a time-and-materials model with daily rates, so you pay for the days worked while Zartis covers holidays, sick leave, and other overheads. Timelines depend on the seniority and specialism required, but most engagements start with a defined scope and a short matching phase before onboarding begins.

    You do, entirely and always. Every line of code, every model configuration, and every workflow built during an embedded engagement belongs to your organisation, with no retained rights on Zartis’s side.

    At the end of an engagement, Zartis runs a structured handover: documentation, codebase walkthroughs, and knowledge transfer to whoever takes on the work next, whether that is your internal team or a new engagement. There is no cliff-edge exit. Engagements can also extend, scale up, or scale down as your priorities change, without renegotiating the relationship from scratch.

    FAQs: Others

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