Each assistant starts with a defined audience, journey, task, and channel. It answers from approved knowledge, stays inside clear access boundaries, hands its context to a person when needed, and has an owner who runs it day to day.

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

See all references

Choose the audience, knowledge, channel, and handoff problem you need to solve. The 5 offerings cover distinct assistant journeys and operating needs.

Build & Integrate

Enterprise AIChatbot Development

Your chatbot should do one defined job for one audience across its approved channels. We govern the knowledge and tools behind that service, then test whether the conversation, user context, and consent state reach the person who takes over.

The enterprise build fits when one audience needs a consistent governed service across approved channels, with whole-conversation findings and channel-owner release gates.

Customer Service AI Agent& Assistant Development

We build a support agent or assistant for defined case types, using approved knowledge and support policy. It handles permitted work, keeps the customer context intact, and routes defined exceptions to the right human queue.

A support assistant is the better fit for defined case types that must preserve customer context, obey support policy, and reach a named human queue.

Knowledge-GroundedChatbots

An answer can sound plausible even when the source is unapproved or off-limits to the person asking. We build the chatbot around an approved source register, visible citations, access-aware retrieval, and clear rules for asking a follow-up or giving no answer.

Grounded chat suits assistants whose main risk is unsupported or unauthorized answers, requiring identity-filtered retrieval, citations, and a clear no-answer path.

Voice AIAssistant Development

Voice work is judged inside the exchange itself. We build around defined journeys, with clear requirements for speech recognition, turn-taking, latency, identity, escalation, and channel limits. Representative conversations show where the assistant works, where it should stop, and what context the human team receives.

Voice is the right channel when the service journey must be proven through speech recognition, turn-taking, latency, identity checks, and context-rich escalation.

Employee AIAssistant Development

Employees should know what the assistant can help with, which approved internal knowledge it may use, and when to ask a person. We build around a few journeys and access rules, with feedback, escalation, and adoption in the design. We test representative work before the service owner decides where it is ready to help.

An employee assistant fits bounded internal journeys that need approved knowledge, role access, feedback, and named owners for adoption and escalation.

A fluent reply is the easy part. The assistant still has to answer from approved knowledge, respect identity and tool permissions, hold the conversation context, and hand that context to a person when human judgment is needed.

We begin with one audience, task, and channel. Only the knowledge and tools that journey needs get connected, realistic conversations and failure cases come next, and release moves in stages while an owner reads the evidence.

Only the knowledge and tools that first journey needs get connected, and the access rules on those sources stay intact.

Support transcripts, sales chats, FAQs, and service rules tell us which journeys come first, what people actually ask, and where a person should take over; approved sources are then prepared for retrieval with their access rules preserved, and we define when the assistant should cite, ask for clarity, decline, or escalate. Zeo builds and tests the first usable journey with representative conversations and failure cases, adds bounded tool access and context-preserving handoff, and releases gradually on the agreed channel. Day-to-day operation belongs to your named owner, who reads live conversations, groups failures and exceptions, and decides the next fix or journey from that evidence.

From mapping the journey to running the service

Map conversations and handoffs
Support transcripts, sales chats, FAQs, and service rules tell us which journeys come first, what people actually ask, and where a person should take over.
Prepare knowledge and RAG
We prepare approved sources for retrieval-augmented generation (RAG), keep their access rules intact, and define when the assistant should cite, ask for clarity, decline, or escalate.
Build, test, and release by channel
We build the first usable journey, test representative conversations and failure cases, add bounded tool access and context-preserving handoff, and release it gradually on the agreed channel.
Review evidence and improve
In operation, your owner reads live conversations, groups failures and exceptions, and uses that evidence to pick the next fix or journey.

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.