[ THE OPERATIONAL CONTEXT LAYER FOR ENTERPRISE AI ]
AI you can put in front of your customers.
Deterministic, validated and on the record: governed answers from live data across every cloud. Your data never moves. Everything we build stays sovereign and yours.
[ WHY ENTERPRISE AI STALLS ]
Most AI agents never make it past the pilot.
The demo works. Then the agent meets the real business: data scattered across systems you can’t consolidate, answers no one can sign off on, and a bill that grows with every question.
Answers nobody can rely on
Models hallucinate, and they improvise a different query every time they’re asked. The same question can get a different answer that no one can explain, so risk and compliance won’t approve it.
Data that can’t move
Your business lives across lakes, clouds and systems of record. Consolidating it into one platform takes years and costs a fortune, and by the time it’s done, the next migration is already on the roadmap.
Costs that climb with every question
Agents are handed everything and told to work it out. Every answer burns tokens and reasoning, and each new use case starts from scratch.
[ WHAT ZAIMLER DOES ]
zaimler sits between your AI agents and the systems that run your business.
Agents stop guessing at raw tables. They ask zaimler, and zaimler answers from the source, the way your business approved.
Four steps and you are up and running
Connect your data and its metadata: Lakehouses, warehouses, systems of record and documents, in any cloud or on-prem, plus the metadata from your catalogs, schemas and YAML.
Model each domain, with your experts: For each domain or use case, zaimler’s AI builds a business model, a knowledge graph with only what that domain needs, and its slice of the context graph.
Approve the queries and workflows your business runs: The questions your business really asks, and the multi-step workflows behind them, become certified queries and workflows.
Agents, apps and BI ask; zaimler answers live: Over MCP, API, the zaimler UI or JDBC. The approved query runs live across your sources, with your access rules applied and its sources attached.
[ WHAT CHANGES WITH ZAIMLER ]
AI you can put in production, keep in your control, and afford to scale.
Agents fail in production for one reason: they see an incomplete, inconsistent picture of the business scattered across disconnected systems.
Governed, accountable AI
Your AI only touches what it’s allowed to touch. Access rules apply to every person and every agent on every query. Each question runs the same certified logic against live data, and every answer is traced to its source, so it changes when your business changes, never because the AI improvised.
Sovereign and portable
Your data stays in your systems, your clouds and your compliance boundary. The context layer, models and outputs are built on open standards and belong to you. You stay with zaimler because it works, not because you can’t leave.
Cost that falls as you grow
Agents get precise context instead of everything, so they use fewer tokens, need less reasoning and get it right more often. Each use case gets its own domain model, and each new one builds on the last and is delivered faster. Your cost of ownership goes down over time, not up.
[ PRODUCT ]
One context layer for every AI workload
Agents over MCP, analysts in plain language, applications on one governed model.
Platform
The context and governance layer for agentic AI. Connect sources, map the unified model, and serve every workload from one foundation.
Ontology
Auto-inferred from your data by the semantic analyzer, validated by your team. Most ontologies take quarters; yours starts working in days.
Explorer
Ask in plain language and trace the answer. Query the model across every connected source and see exactly where each fact came from.
Governance
Policy, lineage, and audit in one control plane. Enforce access policies across every source from a single point of entry.
[ IN YOUR INDUSTRY ]
Built for businesses where a wrong answer has a price
Ground every payout in the real policy record
Claims and billing resolve to the same policyholder so every payout follows a link that exists.
See true exposure across every mandate you run
Portfolio and risk come together as one book so exposure reflects what you hold today.
Alert on entity links that actually connect
Accounts and transactions land in one case so alerts fire on entities that are genuinely the same.
Join the whole patient before the decision
EHR and claims join into one record so prior authorization clears at the speed of care.
Answer the subscriber from live network state
Billing and network become one subscriber so your agent answers while the customer is on the line.
Reroute on the live state of your network
Fleet and freight run as one operation so routing adapts the moment an exception hits.
[ HOW WE START ]
Your first agent, proven on your data.
One team, from first workflow to production. zaimler is your platform and your integration partner, so you don’t need a separate systems integrator or an army of consultants. We build the first use case with you, run it in production, and hand it over.
Pick one workflow
Start where a wrong answer costs you most: a claim, an alert, a call your agents can’t resolve today.
Prove it in weeks
A proof of value on your own data, inside your environment, measured against the outcome you care about.
Run it, then own it
We build and operate it with your team in production, then transfer it. The context layer and everything in it is yours.
[ INTEGRATED BY DESIGN ]
No separate integrator. No army of consultants.
zaimler brings the platform and the people who deploy it, as one team. And we work easily alongside the platforms and partners you already have.
Talk to our team[ FIELD NOTES ]
Context for enterprise AI, from the people building it
When Semantic Embeddings Break: Why No Cosine Threshold Will Save You
A cosine score ranks related terms. It cannot decide which ones are the same.
Five reasons data catalogs can't be a context layer
Data catalogs were built for design-time discovery & governance. AI agents need runtime context. Five structural reasons the catalog rebrand won't hold.
Two Summits, Same Blueprint
What Snowflake and Databricks announced in June 2026, and the five mechanisms behind any "context layer" claim