We start from a user job somebody owns, build the smallest useful slice, test it under representative conditions, and hand the application over with its evidence, controls, and operating owners.

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

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The 13 offerings range from discovery and proofs of concept to integration, model adaptation, and handoff. Each one answers a distinct product or engineering question.

Build & Integrate

AI Proof ofConcept Development

The work begins from one falsifiable hypothesis. We build the smallest credible experiment that can support or disprove it, then recommend proceed, revise, or stop with the evidence attached.

A proof of concept fits one product hypothesis that needs the smallest credible experiment and a traceable proceed, revise, or stop call.

AI Pilot &MVP Development

A controlled group does real work with an operable product slice. What the pilot records about quality, adoption, support demand, cost, and incidents becomes the evidence behind the scale, revise, or stop decision.

Move to a pilot once the product slice is operable and a controlled cohort can supply quality, adoption, support, cost, and incident evidence.

Custom Generative AIApplication Development

One focused generative AI product, built so the user journey, application state, data and identity paths, model behavior, controls, and evaluation hold together as a system someone can operate after release.

Build the full application when the user job is validated but state, identity, data paths, model behavior, controls, and operations must work together.

Enterprise AICopilot Development

We put an assistant inside a workflow people already use, where it can help prepare work and weigh decisions. Sources, role authority, permissions, and required review stay visible while the task moves.

A copilot fits an existing workflow needing role-scoped suggestions, visible sources, human review, and correction evidence before any consequential action.

AI Document IntelligenceApplication Development

We build a workspace where analysts compare documents, open the passage and version behind a conclusion, and see conflicts or uncertainty before deciding what belongs in a case.

Document intelligence fits analysts who need conclusions tied to exact passages and versions, with conflicting evidence and uncertainty visible inside the case workflow.

LLM APIIntegration

We connect your application to model APIs through a contract the product owns. Provider quirks stay behind that boundary while schemas, secrets, structured output, retries, fallbacks, budgets, and operating signals are tested together.

Select API integration when a working model call needs application-owned schemas, secret boundaries, retries, fallbacks, budgets, and provider-change support signals.

Enterprise AIIntegration

We connect an AI capability to enterprise identities, data, events, workflows, and transactions without opening a side channel around existing authority. Reconciliation, recovery, failure isolation, and audit evidence sit inside the business path.

Enterprise integration fits when the AI product must cross identity, event, and transaction boundaries while preserving source-system authority, reconciliation, recovery, and audit evidence.

Fine-Tuning DatasetCuration

A folder of good examples is not yet training data. We trace each record, confirm how it may be used, calibrate the labeling rules, remove duplicates, and keep evaluation cases out of the training set. This engagement ends with the dataset. It does not train the model.

Curate the dataset when the product needs permitted, representative training examples with calibrated labels, isolated evaluation splits, and a traceable source trail.

LLM Fine-Tuning &Model Customization

A stable behavior gap has to earn the training experiment. Prompt and RAG baselines get the same held-out tasks first. If the gap remains material, each versioned run is weighed for lift, critical failure, general regression, consistency, cost, and latency.

Fine-tune only when prompt and RAG baselines leave a stable behavior gap, then compare target lift with regression, consistency, cost, and latency.

DomainAdaptation

Domain adaptation focuses on the language, formats, and recurring tasks that define specialist work. We compare it with the current prompt or RAG baseline and weigh the local gain against general regression, operating cost, and the refresh burden.

Domain adaptation fits repeatable specialist-language or format failures, where local gains must be weighed against general regression and refresh burden.

AI Product Discovery& Experience Design

Before engineering commits to a direction, we make the user job, AI's role, uncertain moments, review points, fallback paths, dependencies, and acceptance choices concrete enough to test.

Start with product discovery while the user job, AI role, uncertain moments, review points, and fallback paths remain too vague for engineering.

Model Selection &Baseline Benchmarking

Once the intervention class is clear, the question becomes which model can carry it. Every candidate sees the same representative work, safety cases, latency conditions, and cost assumptions, leaving a reproducible baseline and a selection record open to review.

Benchmark models once the intervention class is settled but candidates still need the same workload, safety cases, latency conditions, and cost assumptions.

Fine-Tune vs RAGvs Prompting Assessment

A weak answer alone cannot tell you whether the system needs a better prompt, retrieval, fine-tuning, or a change elsewhere. We trace the failure and update patterns first, then test the smallest credible intervention class before recommending what, if anything, to build.

Run the intervention assessment when weak product output could stem from prompting, retrieval, training, or system design and the failure pattern remains unclear.

The released product still has to hold together across the experience, model behavior, data, integrations, evaluation, security, cost, and the people expected to operate it.

We begin with the smallest product slice that can answer the decision at hand, compare it with the current baseline, and widen the scope only when the evidence supports another commitment.

We agree the user job, the target outcome, and who gets to say proceed, change, or stop before any build starts.

We name the product decision — user job, target outcome, constraints, available evidence, decision owner — then read the real users, systems, data, known failures, and existing controls before choosing an approach. Zeo builds the smallest workable slice that can answer the decision at hand, compares it against the current baseline, and runs the cases that could change the answer rather than the ones that would confirm it. Widening scope is a separate commitment supported by evidence, not a default. At handoff, accepted behaviour, open exceptions, support and monitoring duties, and the triggers for rollback or another review pass to the named operating owner.

Each one ends in an inspectable record and a named owner

Name the product decision
Agree the user job, target outcome, constraints, available evidence, and who gets to say proceed, change, or stop.
Read the working conditions
Look at the real users, systems, data, known failures, and existing controls before choosing the product approach.
Build and challenge the useful slice
Choose the simplest workable design, build the bounded scope, and run the cases that could change the decision.
Put operation in named hands
Transfer accepted behavior, open exceptions, support and monitoring duties, and the triggers for rollback or another review.

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.