Production-ready.
Controlled.
Operational.
19 years of enterprise IT. One focus today: the context layer your AI agents answer and act from.
I build it on a temporal knowledge graph: facts from chat, tickets, wiki and CRM, each with its source and the time it held, read with each person's own permissions, and actions governed by explicit approval or scoped standing consent. On your own tenant, billed by the hour, with an hour estimate for each phase before you commit to the build. How I build it →
An answer with sources, here on the page.
Case study · Context layer for AI agents
Sourced answers and human-approved actions across chat, tickets, wiki and CRM
Already a customer? Support directly via rene@renezander.com.
Results that matter.
Three pillars of production-ready AI.
01Decision confidence
Check. You know before you invest whether and how your pilot becomes production-ready. Production-readiness audit: risks, costs, and compliance gaps, prioritized by impact.
02Controlled execution
Build. Hourly billing, clear phases, human approvals, and limited access rights. Integrated into your system landscape instead of an isolated AI demo.
03Operational readiness
Harden. Monitoring, sandboxing, error handling, rollback, and clear ownership. The system holds up in live operation, not just in testing.
No pilot without a path to production. No automation without clear ownership.
Five use cases. Shipped production-ready.
Real client projects with measurable results: problem, solution, metrics, and the path into operation.
Context Layer for AI Agents: Sourced Answers and Human-Approved Actions Across Chat, Tickets, Wiki and CRM
A production operational context layer on the company's own cloud tenant: six sources folded per customer on one radar, shared facts covered by recorded human consent, every decision shared automatically with the team it concerns, external actions approved in chat, every step audited. The buyer-side proof: someone who was not in the meeting learns what was decided without asking anyone who was.
Read the full case study →Production Self-Hosted Voice AI Platform For Data-Residency-Sensitive Teams
A production-oriented self-hosted voice AI deployment with measured warm-path latency (0.3s combined on the dual-GPU L40S + L4 stack), persistent state for fast ramp-up, and a structured writeback contract so every call feeds back into sales, support, product, and ops — deployable in EU infrastructure today and migratable into a client-owned VPC when required.
AI Revenue Prioritization System Embedded in HubSpot CRM
HubSpot-native AI layer that scores every contact on ICP fit, ranks the pipeline by expected revenue, and prescribes the next action — with a written rationale the rep can defend to a manager. Built inside HubSpot, not alongside it.
AI Decision Support Platform for Enterprise Operations
A transformation portfolio layer that scores every initiative across business value, technical complexity, capability maturity, and ROI — and produces a concrete next-step plan per initiative. Leadership allocates budget from live priority scores, not quarterly PowerPoint.
Enterprise AI PII Redaction System for Sensitive Documents
Drop-in layer after deterministic masking that redacts PII from free-text SAP columns before data lands in dev, QA, or training systems. Runs on the client's own hardware — DSGVO-konform by design.
What clients say about working together.
René has a strong instinct for identifying operational weaknesses that others often overlook. In our work together, he was always the one questioning existing processes and finding practical ways to improve efficiency. He thinks in solutions and delivers results.
An experienced Enterprise Architect who consistently exceeded expectations. He designed and developed an AI-driven toolset that extended LeanIX, enhancing how enterprise architecture insights are generated and used. I highly recommend him to any organization seeking a forward-thinking and results-driven architect.
René is truly a solution architect. He manages, executes and delivers on key projects with precision, dedication and calmness. He drives large projects and still takes time to help in the details.
Rene's contribution has been fundamental for the successful migration to O365 of all users of the newly acquired group. Always available to help with his wide knowledge, greatly appreciated by users and team members.
I worked with Rene, and found that he is extremely skilled at putting together information for the big picture, coordinating diverse needs and integrating a wide variety of systems. His strategic use of AI makes tasks much more efficient. Highly recommended.
Someone who is keen to help and quick to deliver practical solutions. Willingness to think beyond standards and approach challenges from different angles — a reliable, solution-oriented colleague who brings both creativity and persistence to his work.
A clear process. From idea to outcome.
Automation that runs like infrastructure, not like a chatbot. The 2026 shift: agents are managed execution environments. Every workflow I ship runs with memory, approvals, sandboxing, and rollback: the four guarantees that separate a production system from a demo.
Understand
We analyze your processes, challenges, and goals.
Design
We design the architecture around process, data, and risk. With a clear roadmap and an hour estimate for each phase.
Build
We build, integrate, and test – iterative and transparent.
Operate
Monitoring, defined rollback, and a clear operating model after go-live.
See a sample production scope →Technology by requirement, not by hype.
Graphiti, FalkorDB, Azure AI Foundry, Claude, or self-hosted models are tools. The choice follows process, data, risk, and operating costs.
Claude
GitHub
Cosmos DB
Azure AI Foundry
Graphiti
FalkorDBContext layers on temporal knowledge graphs for enterprise AI agents.
19 years of enterprise IT. One focus today: the context layer your AI agents answer and act from.
I am René Zander. I build context layers for AI agents on temporal knowledge graphs, backed by 19 years of IT experience from mid-size companies to Fortune 500 and 50+ delivered projects.
No generic AI workshop. No chatbot demo. I work with teams whose assistant already reads chat, tickets, wiki and CRM and now has to answer from the current state and act only with a person's approval.
I maintain Graphiti Local, an open-source, local-first temporal knowledge graph with read-only MCP tools and writes only after a person approves.
See my projects →What happens next is your call.
30 minutes, booked right here. Your topic, no preparation.
The case study: a context layer for AI agents, with approvals in operation.
Open the case study →