We help business, delivery, and control teams decide who owns a system choice, what proof belongs at the gate, how exceptions move, and when the decision should return for review.

Some of the 500+ brands we've worked with. Our delivery runs on 100+ AI workflows in production.

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The 7 offerings cover the inventory, rules, assessments, management system, and vendor controls needed to govern named AI systems.

Decide & Govern

AI System Inventory& Risk Classification

Procurement may show one list while teams are working with another. We reconcile sanctioned, reported, and embedded AI use, then record each system's owner, intended use, data, actions, affected work, evidence, and review tier. Unresolved entries stay unresolved until an accountable person can settle them.

Begin with the inventory when procurement records and actual AI use disagree, leaving system ownership, risk tiers, and review triggers unresolved.

AI GovernanceFramework Design

A principle does not tell a delivery team who can approve a system change or what proof belongs at the gate. We design the authority behind those recurring decisions, including the owner, forum, evidence, exception route, and review trigger.

Framework design belongs where recurring AI decisions lack named lifecycle gates, evidence requirements, forum authority, and a shared exception route.

AI Policy &Acceptable-Use Design

An employee has a routine task, an unfamiliar tool, and a policy full of principles. We write the decision they need at that moment: approved, conditional, or prohibited use, with the required verification and a route for cases the rule cannot settle.

Write the policy when employees cannot classify real AI tasks as approved, conditional, or prohibited or route unresolved cases to an owner.

AI Risk &Impact Assessment

Before a system advances, we document what it is intended to do, how it could foreseeably be used or misused, and who may be affected. Each plausible harm is linked to the available evidence, current controls, mitigation owner, and whoever has the authority to decide how much residual risk is acceptable.

Run an impact assessment when a named system may affect people and plausible harms still lack evidence confidence, mitigation owners, or residual-risk authority.

EU AI ActReadiness Assessment

Your qualified authority defines which EU AI Act obligations and systems are in scope. We test whether the corresponding organizational evidence, controls, owners, exceptions, and remediation records exist and are current. The legal and compliance conclusion remains theirs.

EU readiness fits after your qualified authority defines the obligations, when sampled systems still lack current evidence, controls, owners, or remediation records.

ISO/IEC 42001 Readiness &AI Management System Design

We define the boundary of the AI management system and show how its processes, roles, controls, interfaces, and retained records fit together. Representative evidence then informs an owned readiness backlog. Certification remains a separate decision for an accredited independent body.

Management-system design fits when governance activities exist but processes, retained records, interfaces, and backlog ownership do not yet form one accepted boundary.

Third-Party AI Risk& Vendor Governance

A vendor can reach contracting while its data use, model changes, subprocessors, and exit duties are still spread across separate reviews. We connect the intake decision to evidence, approval conditions, monitoring, exceptions, and a practical exit path with named owners.

Vendor governance applies when supplier data use, subprocessors, model changes, contract conditions, monitoring triggers, and exit duties sit across separate reviews.

The useful moment is when a team can apply the rule to one named system, show the evidence, route an exception, and name who is left holding the remaining risk.

We start with intended use, affected people, data, and the authority already present. Then we design the smallest set of decision rights, controls, records, and review routines that can guide those choices without creating a second organization around them.

We design the smallest set of decision rights and records that changes a real choice, not a second organization.

We identify the systems in scope, how they are used, who may be affected, which evidence exists, and who can make the consequential decisions, then shape risk tiers, decision rights, policy boundaries, lifecycle controls, and exception routes around those named systems rather than around a generic framework. Realistic decisions are run through the design to expose unclear wording, unsupported readiness claims, owners without authority, and controls that leave no usable evidence behind. The handoff records who decides, what stays open, which evidence must be retained, who is left holding the residual risk, and the date or change that brings the question back.

A parent-level view of the work across the governance offerings

Establish the system and authority
We identify the systems in scope, how they are used, who may be affected, which evidence exists, and who can make the consequential decisions.
Set proportionate rules and controls
Risk tiers, decision rights, policy boundaries, lifecycle controls, and exception routes are shaped around the named systems and use cases.
Try the design on representative cases
Realistic decisions expose unclear wording, unsupported readiness claims, owners without authority, and controls that leave no usable evidence.
Transfer ownership and review duties
The handoff records who decides, what stays open, which evidence must be retained, and the date or change that brings the question back.

Every operational consultant at Zeo has secure LLM access and training, and AI sits inside the daily work. Five of them came through our AI Bootcamp and wrote down what they expect it to change.

I see generative AI having an enormous effect on daily life and on every industry it touches. As the technology develops, the range of uses will keep widening across creativity, problem-solving, and innovation. We can already see that range in realistic image, video, and music production, pharmaceutical research, and design. I expect the effect on industries to become profound. E-commerce, healthcare, finance, and many other sectors will be able to create more engaging, personalized experiences and make their processes more efficient.

The ability to produce unique content and solutions will open new possibilities and increase efficiency.

Ozan Ketenci

Generative AI has the potential to transform SEO, digital marketing, and many other sectors. I expect it to play an important role in our lives in the near future, with more personal experiences, more effective marketing, faster interpretation of data, and quicker action. Products and services will improve. Processes such as customer communication will become more efficient, and organizations that fail to keep up will fall behind businesses that bring AI into their work.

Organizations should start planning the AI applications that make sense for their sector now.

Samet Özsüleyman

We may be at a moment as significant as the computer revolution, with the potential to transform businesses and industries. Yet for many people, generative AI still means opening a tool such as ChatGPT for a task at work or in daily life. That is only the surface. Companies that integrate generative AI models into workflows and customer processes, and go beyond content production, will gain huge competitive advantages in the coming years.

I believe generative AI should be on the agenda of every board of directors as soon as possible.

Hande Parmaksız

I see artificial intelligence as the most exciting technology of both the present and the future. Its potential is unlimited, and we're still at the tip of the iceberg. AI is developing quickly, while much of what it could mean for different sectors remains unexplored. The effect on digital work is already substantial. In the years ahead, I expect breakthroughs that change how entire industries work.

AI's potential will keep expanding. No sector can afford to ignore the opportunity for efficiency and progress. We will keep discovering new dimensions, and I don't see a saturation point.

Can Mutioğlu

Work by major technology companies is likely to give generative AI a much wider role in the years ahead. It will create new dynamics in art and design, as well as in sensitive fields such as healthcare and finance. As the technology becomes part of daily life, the ethical and risk questions will grow with it. Being able to follow and experience those developments up close is what makes generative AI so exciting to me.

I look forward to seeing more uses of generative AI that benefit society.

Ezgi Gülsen Yaylı

Three speakers look at the pace of AI change and what it means for e-commerce and content teams.

The Future Will Come Faster Than You Imagined

Yiğit Konur, Wope

Effective Use of AI for E-Commerce Companies

Burak Pehlivan, Wope

Producing Content Compatible with Google Algorithms with AI

Mert Erkal, Stradiji

Agents, chatbots, and RAG systems at Zeo are built by senior engineers who keep operating them after launch. The consultants below are those builders, matched to the work this page covers.