When an AI system drifts, leaks, or behaves unpredictably, the cause is often upstream in the data work rather than in the model alone. We design and build the datasets, labeling systems, preparation steps, pipelines, quality controls, lineage, and monitoring that let your team trace those problems back to a concrete decision.

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

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The 6 offerings cover collection, annotation, preparation, architecture, pipelines, and observability. Select the data decision that is holding the AI workload back.

Build & Integrate

AI DatasetCollection

A usable training set starts with the population it has to represent and the sources it may draw from. We collect against that definition and keep each record tied to its source, proposed usage basis, consent evidence, collection rule, and rejection history.

Dataset collection fits when the AI workload needs a named population, permitted sources, sampling rules, consent evidence, and record-level provenance before preparation.

Data Annotation& Labeling

Production labeling goes wrong when people are asked to make the same judgment with different rules. We settle the ontology, examples, ambiguity rules, calibration, quality samples, and adjudication before the queue opens, and keep those decisions available for review.

Annotation operations fit when multiple reviewers must apply one ontology, with calibrated examples, sampled quality, and adjudication tied to guideline versions.

AI Data Preparation& Validation

An approved raw version becomes risky the moment cleaning rules disappear into a notebook. We prepare it for one training or evaluation job and keep every cleaning rule, removal, split, and validation result connected to the dataset version it changed.

Prepare and validate data when an approved raw version needs reproducible cleaning, protected splits, leakage checks, and a versioned data card.

AI Data Strategy& Architecture

Several teams can each be right about part of the data future and still leave the company without one decision. We map how AI data is owned, accessed, checked, traced, and retired today, then compare target-state options and record the architecture your authority is prepared to accept.

Data strategy fits when AI domains have incompatible ownership, access, quality, lineage, or lifecycle rules and need one accepted target architecture.

AI DataPipeline Engineering

The design usually makes sense on a whiteboard. The risk sits in the manual steps, scattered rules, and failure handling nobody has tested end to end. We build one agreed path from source to delivery, prove how it behaves on representative inputs and known failures, and hand over the working slice with its operating limits and runbook.

Build the data pipeline when the source-to-delivery design is agreed but manual steps, retry behavior, and failure handling remain untested as one slice.

Data Quality, Lineage &Observability for AI

A dashboard can show that something changed and still leave the team hunting for the source, the affected AI consumer, and the person who should respond. We trace important data changes from the source signal to the AI system affected and the person expected to act, with thresholds, dependencies, incidents, and follow-up evidence kept together.

Data observability fits when quality, lineage, freshness, or drift signals exist but cannot trace an affected AI consumer to a response owner.

Coverage gaps, inconsistent labels, undocumented transformations, and stale inputs show up later as model behavior someone has to explain. We keep those upstream decisions visible instead of hiding them inside one performance number.

Before collection, labeling, or pipelines scale, we define intended use, sources, owners, quality checks, and acceptance rules. Every material change stays tied to a version, a reason, and the person expected to stand behind it later.

The data contract is settled before collection, labeling, or pipelines are allowed to scale.

We define the model or evaluation task, intended use, sources, rights, schema, important slices, quality rules, and the person who can accept the result, then collect, transform, split, or label through recorded steps that keep provenance, permissions, guideline versions, and exceptions attached to the work. Coverage, duplicates, leakage, label disagreement, lineage, freshness, and task-specific checks are examined across the slices that actually matter to the use case rather than collapsed into one performance number. The accepted asset is versioned and handed over with its pipeline, controls, known limitations, operating guidance, and ownership, so the next system inherits the decisions instead of re-deriving them.

Define the data job, do the work in versioned steps, test it where it matters, and hand it to someone who can own it.

Name the dataset and the decision
We define the model or evaluation task, intended use, sources, rights, schema, important slices, quality rules, and the person who can accept the result.
Collect, label, or prepare with a record
We collect, transform, split, or label through recorded steps, keeping provenance, permissions, guideline versions, and exceptions attached to the work.
Check the slices that can hurt you
We examine coverage, duplicates, leakage, label disagreement, lineage, freshness, and other task-specific checks across the slices that matter to the use case.
Version the result and transfer ownership
We package the accepted data asset, pipeline, controls, known limitations, operating guidance, and ownership the next system needs.

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.