AI that actually gets work done.
Not just a chat button on your website.
Agentic AI means a system reads emails, checks documents, calls your internal systems, and completes multi-step tasks, instead of just answering questions. We build exactly this kind of automation, with clear boundaries, traceable decisions, and checkpoints for humans where it matters.
Why Zeitcode for agentic AI
We build the AI search and automation our own website runs on. This isn't theory from a whitepaper.
Built by us, run by us
Our own website chatbot runs on retrieval-augmented generation, vector search, and a custom backend. We know first-hand where agentic AI projects actually hit their limits.
A system that actually finds your knowledge
Building semantic search over large data volumes with embeddings and vector databases, instead of blunt keyword search that misses half the relevant results.
Human-in-the-loop where it counts
Critical decisions route through approval checkpoints for people, instead of an agent sending emails or triggering orders unsupervised.
Connected to your real systems
An agent that only talks to itself is useless. We connect agents to CRM, ERP, email inboxes, and internal APIs so real work actually gets done.
Automation you can actually trust
Agentic AI without control is a liability. We build systems that stay traceable and bring humans in when it matters.
Every decision is explainable
Logging and source citations for every answer and action, so you can always trace back why an agent did what it did.
Local and hosted models, combined
Not every request needs the most expensive model. We combine local LLMs and hosted providers so operating costs stay predictable.
Answers grounded in your real data
Retrieval-augmented generation ensures answers are based on your actual documents and data, not the model's best guess.
From document review to support
Automated document extraction, request pre-qualification, internal knowledge search, or customer support — the principle stays the same, the application adapts to your case.
How your agentic AI project runs
Four phases, from a clearly scoped use case to production.
Phase 1
Use caseProcess & success criteria
- ✓Identify one clearly scoped process
- ✓Measurable success criteria instead of vague expectations
Phase 2
Data & knowledgeBuild data sources & vector search
- ✓Connect relevant documents and systems
- ✓Embeddings and vector search for precise retrieval
Phase 3
Build the agentLogic, checkpoints, testing
- ✓Agent logic with clear approval checkpoints
- ✓Tests against real, including difficult, cases
Phase 4
OperationsRollout & ongoing optimization
- ✓Controlled rollout with monitoring
- ✓Ongoing tuning based on real usage
Step by step, with clear outcomes per phase.
Voices from AI projects
Short excerpts from projects with real, measurable automation goals.
“Our old keyword search often surfaced the wrong results. The new semantic search finds what's meant, not just what was literally typed, with response times in the sub-second range.”
“Requests used to be sorted manually. Now an agent handles pre-qualification including document review, and our team only deals with the cases that genuinely need human judgment.”
- ✓Significantly less manual sorting and pre-qualification
- ✓Answers grounded in real company data
- ✓Clear checkpoints instead of uncontrolled automation
Services in detail
From internal knowledge search to a full automation workflow.
Semantic search over your data
Build vector search over documents, wikis, or product catalogs so your team and customers find what they need faster.
- ✓Embeddings and vector database sized to your volume
- ✓Re-ranking for more precise results
- ✓Integration with existing knowledge sources
Workflow agents for internal processes
Automate multi-step tasks like document review, data extraction, or request pre-qualification.
- ✓Clear approval checkpoints for critical steps
- ✓Integration with your existing systems
- ✓Measurable time savings, not vague promises
Automated document & receipt extraction
Structured extraction from documents, receipts, or forms with vision AI and OCR, instead of manual data entry.
- ✓Extraction that handles imperfect scans
- ✓Validation against existing data sources
- ✓Local or hosted models depending on privacy needs
Questions about agentic AI
Answers on control, privacy, and how to get started with an AI project.
Still have a question?