AI Consulting Services
Turn governed enterprise knowledge into answers you can trace
Some of the 500+ brands we've worked with. Our delivery runs on 100+ AI workflows in production.
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Task-level offerings
6 parts of an enterprise knowledge system
Build & Integrate
Enterprise RAGDevelopment
Build enterprise RAG when the first knowledge use case is defined and sources, retrieval, citations, access controls, freshness, and operations must connect.
Access-ControlledRAG
Access-controlled RAG fits when source permissions must survive indexing, retrieval, caches, citations, and logs, especially after identity or access changes.
Enterprise Knowledge SystemStrategy & Architecture
Start with knowledge-system architecture when source authority, access, freshness, retrieval, evaluation, and operating duties sit with different teams.
RAG Readiness &Corpus Assessment
Corpus assessment fits when the RAG idea is clear but sampled sources may still fail on authority, access, freshness, coverage, or answerability.
Knowledge Ingestion &Indexing Pipeline Development
Build ingestion and indexing when approved sources need repeatable updates, metadata, permission propagation, deletion behavior, and a connector-to-index operating record.
Semantic & HybridSearch Development
Hybrid search fits when exact terms and semantic similarity solve different query slices, requiring filters, reranking, reviewer judgments, and explicit trade-offs.
Why it matters
A good answer can still hide a broken knowledge path
Source authority, permissions, deletion, freshness, retrieval quality, citations, and operating ownership can each fail in different ways. We keep those layers separate, so the team can diagnose the path instead of trusting one fluent answer.
How we work
Test the source path before you trust the response
We inspect the corpus, permissions, ingestion, and retrieval separately from generation. That keeps missing knowledge, access defects, stale content, and answer failures diagnosable instead of collapsing them into one pass-or-fail judgment.
Scope and ownership
The source path is tested separately from the answer
Keeping the layers apart is what lets a team diagnose a knowledge failure instead of trusting one fluent response.
We choose the first knowledge need, the authoritative sources, the users, the access rules, the owners, and the acceptance decision before the system expands, then inspect representative sources, permissions, freshness, retrieval behaviour, and known failures, recording unknowns clearly instead of treating them as passes. Corpus, permissions, ingestion, and retrieval are examined separately from generation, so missing knowledge, access defects, stale content, and answer failures stay individually diagnosable. Zeo builds or assesses the smallest slice that can answer the decision; the handover records accepted conditions, source and operating owners, unresolved gaps, monitoring responsibilities, and the next review trigger.
Our enterprise knowledge and RAG process
Frame the decision and its boundary
Choose the first knowledge need, authoritative sources, users, access rules, owners, and acceptance decision before the system expands.
Establish the current evidence
Inspect representative sources, permissions, freshness, retrieval behavior, and known failures. Record unknowns clearly instead of treating them as passes.
Test the smallest adequate intervention
Compare the viable architecture, ingestion, permission, and retrieval options, then build or assess the smallest slice that can answer the decision.
Decide, hand over, and retain evidence
Record accepted conditions, source and operating owners, unresolved gaps, monitoring responsibilities, and the next review trigger.
Inside our own team
Five Zeo consultants on what AI is changing in their work
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
Selected AI sessions
Talks from Digitalzone
Three speakers look at the pace of AI change and what it means for e-commerce and content teams.
People who ship the AI systems they advise on
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.
Tools we use
The AI engineering stack behind the work
Models, retrieval, evaluation and observability are separate layers of a working system. These are the ones we build and operate on.
Models and cloud platforms
- OpenAIThe ingestion and indexing layer this page's hero names starts with an embedding step, and OpenAI's embedding models are one of the two default encoders Enterprise RAG Development and Knowledge Ingestion & Indexing Pipeline Development build that layer on.
- AnthropicThe hero's own standard is that a convincing answer can still hide a broken source path, and Claude's context handling is what several children use to keep a generated answer's citation traceable to one specific retrieved passage rather than a blended summary.
- CohereSemantic & Hybrid Search Development, uses Cohere's reranking step to correct cases where vector similarity alone surfaces a passage that's topically close but not actually the best answer to the query.
Agent and automation frameworks
- LangChainEnterprise RAG Development's full source-ingestion-to-answer pipeline is commonly wired together in LangChain, which is what keeps the retrieval, generation, and citation steps this page's hero names as one connected, inspectable chain.
- LlamaIndexThe hero's whole premise is inspecting what happened before trusting a response, and LlamaIndex's indexing is what Access-Controlled RAG and Knowledge Ingestion & Indexing Pipeline Development both use to keep that link between an answer and its exact source document intact.
- HaystackEnterprise Knowledge System Strategy & Architecture sometimes has to design around a client's requirement that governed knowledge never leaves their own environment, and Haystack is the open-source pipeline this page's account uses for that fully self-hosted case.
- UnstructuredKnowledge Ingestion & Indexing Pipeline Development starts from real enterprise source files, PDFs, scans, office documents, and Unstructured's partitioning is what keeps a table row or footnote from silently merging into the wrong chunk before that chunk ever reaches the vector index.
Retrieval, embeddings and memory
- Voyage AIWhen RAG Readiness & Corpus Assessment finds a client's source material uses specialized or technical language, Voyage AI's domain-tuned embedding options are what this page's account evaluates as an alternative to a general-purpose encoder before ingestion begins.
- PineconeAccess-Controlled RAG needs retrieval that respects a permission boundary at query time, not just topical relevance, and Pinecone's managed metadata filtering is what several children use to keep a retrieved passage inside the querying user's actual access level.
- WeaviateAccess-Controlled RAG's permission boundary sometimes needs to be enforced as a structured filter combined with semantic search in one query, and Weaviate's native filtering is what this page's account reaches for when that combined check matters.
- QdrantFor Knowledge Ingestion & Indexing Pipeline Development inside a security-sensitive organization, Qdrant is the store this page's account uses when the vector index itself has to stay on infrastructure the client fully controls.
- MilvusEnterprise Knowledge System Strategy & Architecture has to plan for corpus sizes that can span an entire enterprise, and Milvus's scale-oriented design is one of the store options this page's account evaluates against that sizing requirement.
- ChromaBefore RAG Readiness & Corpus Assessment recommends a production architecture, it runs an initial retrieval pass against Chroma's embedded store to test corpus quality quickly, without committing to heavier infrastructure the assessment might not end up recommending.
- LanceDBWhere Knowledge Ingestion & Indexing Pipeline Development has to fit inside a client's existing data-versioning workflow rather than adding a separate managed service, LanceDB's embedded format is the option this page's account evaluates for that constraint.
- VespaSemantic & Hybrid Search Development is named for exactly this combination, and Vespa's native hybrid ranking serves the engagements where a client's search needs a real blend of semantic and lexical relevance rather than one bolted onto the other.
Evaluation and observability
- RagasThe hero's insistence on inspecting what happened before trusting a response is checked, in practice, by Ragas's groundedness scoring, used across RAG Readiness & Corpus Assessment and Enterprise RAG Development to confirm an answer traces to real retrieved content.
- LangfuseWhen the hero warns that a convincing answer can still hide a stale-content or access problem, Langfuse's per-run trace is where that specific check happens, following the query through retrieval to the final generated answer.
Safety and security testing
- MindgardAccess-Controlled RAG's own risk, a retrieval path that quietly bypasses the intended permission boundary, is exactly what Mindgard's continuous red teaming is built to probe for on a live knowledge system.
Data, labeling and development
- JupyterRAG Readiness & Corpus Assessment's coverage analysis, checking whether a corpus has enough of the right content to answer real questions, runs as notebook analysis, so the assessment's conclusions trace back to inspectable steps.
Next step
Deploy generative AI solutions with clear business value
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