We define the behavior that matters, test representative and critical cases, and give your decision owner evidence they can review and reproduce.

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

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The 10 offerings test different parts of system behavior, from prompts and RAG to agents, safety, model selection, and launch readiness.

Assure & Operate

AI Agent Evaluation& Reliability

A confident demo proves almost nothing about the next run. We tie a regression suite to the build you want to release and trace what the agent does across repeated tasks, so tool behavior, recovery, escalation, and run-to-run variation stay visible where the release decision gets made.

Take this route when the agent build is release-bound and the same task must run repeatedly, exposing tool behavior, recovery, escalation, and run-to-run variance.

RAG Evaluation &Groundedness Testing

A fluent RAG answer can rest on the wrong document, an unsupported claim, a citation that points nowhere useful, or a retrieval the reader's role never allowed. We score each of those separately, so a failure lands on the layer that owns it instead of blurring into one quality number.

Score retrieval, groundedness, citation, access, and abstention separately here, so each RAG failure lands on the layer whose team can fix it.

AI EvaluationStrategy

Teams often score an AI system for months without agreeing what any number should change. For one AI use, we settle that first: acceptable behavior, the failures that matter, the cases that represent real work, how reviewers judge the evidence, and who may approve release.

Start with evaluation strategy when teams collect scores but have not agreed acceptable behavior, representative cases, critical failures, thresholds, or release authority.

Golden DatasetDevelopment

Model scores are only as honest as the cases behind them. We build a versioned set of representative cases and record where each one came from, what behavior is expected, how ambiguity gets handled, who adjudicates disagreement, and who keeps the set current.

Build a golden dataset when evaluation cases need traceable sources, calibrated labels, protected splits, ambiguity decisions, coverage checks, and a refresh owner.

Independent AIAgent Evaluation

The team that built an agent grades its own homework more kindly than anyone else will. Under a documented independence charter, we test the existing agent across tasks, trajectories, tools, policies, and recovery, with scope and conclusions the implementation team doesn't control.

Independent agent evaluation fits when the build team cannot control test scope, execution, or conclusions for a high-stakes release decision.

Safety &Policy Evaluation

A written safety rule is a claim until someone watches the system follow it. We turn each material rule into paired tests, a misuse attempt and a legitimate lookalike, then examine refusal quality and severity, retest what gets fixed, and record the risk that remains.

Written safety rules become testable here, paired as a misuse attempt and a legitimate lookalike, then judged for severity, false refusals, and residual risk.

AI LaunchReadiness Review

The launch case usually exists, just not in one place. Evaluation reports sit with one team, security and privacy inputs with others, runbooks and support plans somewhere else, and open findings have owners nobody can name. We assemble the current evidence, test whether the case is complete enough to decide, and put go, conditional-go, or no-go in front of the release authority you designate.

Run launch readiness when quality, security, privacy, support, and operations evidence is scattered and the release authority needs one dated dossier for a go, conditional-go, or no-go decision.

Prompt Evaluation& QA

A prompt edit that dazzles in one chat can quietly break three other tasks. We give prompt changes a stable baseline, representative cases, explicit rubrics, calibrated reviewers, failure slices, versioned experiments, and a regression gate, so an improvement is something you can show, not remember.

Prompt evaluation fits version changes that need fixed cases, recorded settings, shared rubrics, reviewer calibration, failure slices, and a regression gate.

LLM Evaluation& Benchmarking

A leaderboard can't tell you which model fits your workload, users, language mix, and latency budget. We run every candidate on the same representative tasks, slices, rubrics, safety constraints, and cost conditions, then show what each option gains and what it gives up to get it.

Benchmark LLMs when public scores cannot answer which model fits your workload, language mix, safety constraints, latency budget, and cost conditions.

Bias, Fairness &Explainability Testing

An aggregate score can look acceptable while one group quietly absorbs the errors. Fairness testing starts with choices only your organization can make: who may be affected, which outcomes matter, and which tradeoffs are acceptable. We test the comparisons those choices create, examine explanation behavior, and say plainly where the evidence is too thin for a conclusion.

Reach for this when an aggregate result may hide which group absorbs the errors, and your owner has defined affected groups and acceptable trade-offs.

A useful evaluation keeps critical cases and user slices in view. It also documents uncertainty and disagreement, with enough context for another reviewer to reproduce the result.

We start with the person who will use the evidence, the failures they cannot accept, and the coverage their decision requires. Only then do we choose metrics, rubrics, and graders.

We start from the person who will use the evidence and the failures they cannot accept.

We name the intended use, the important users, the unacceptable failures, the constraints, and the person who will act on the evidence, then inspect the current system, data, workflow, known failures, and available controls, keeping unknowns visible rather than counting them as passes. Representative cases and suitable checks are chosen only after that, and the critical slices and adverse behaviour that matter to the decision are tested alongside the average. Uncertainty and reviewer disagreement are documented with enough context for someone else to reproduce the result, and the record closes with open exceptions, residual risk, responsible owners, and the condition that would call for another look.

From a clear evaluation question to an owned decision

Define the decision
Name the intended use, important users, unacceptable failures, constraints, and the person who will act on the evidence.
Review the starting point
Inspect the current system, data, workflow, known failures, and available controls. Unknowns stay visible rather than counting as passes.
Build and run the evaluation
Choose representative cases and suitable checks, then test the critical slices and adverse behavior that matter to the decision.
Record the result and owners
Document the evidence, open exceptions, residual risk, responsible owners, and what condition would call for another look.

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.