Kleine KoeAI operations
Kleine Koe builds reliable AI agents, automated workflows, and private AI systems around the way your company actually works—connecting your tools, reducing repetitive work, and keeping people in control of the decisions that matter.
Initializing…
Your current stack does not need to be replaced. We connect it.
Microsoft 365 · Google Workspace · Slack · Teams · HubSpot · Salesforce · Notion · Monday.com · Airtable · Shopify · Exact · AFAS · Jira · ServiceNow · PostgreSQL · Internal APIs
Across most companies, valuable employees spend hours moving information, preparing recurring reports, checking systems, processing documents, and following up on predictable events. These processes are rarely difficult. They are simply fragmented.
7 manual touchpointsper occurrence, every time it happens
1 human touchpointonly where judgment is actually needed
Do not automate people. Automate the repetition around them.
Specialized agents that research, prepare, coordinate, monitor, and act across your systems. Each agent receives a clear role, controlled tools, business rules, and defined approval limits.
Reliable workflows for the work your business performs every day, week, or month. Connect software, data, rules, AI, and human approvals in one maintainable system.
Run models, private knowledge search, and internal AI workflows inside infrastructure you control. Suitable for sensitive data, regulated environments, confidential research, and organizations that do not want core information sent to public AI services.
Projects typically start from €20,000.2
Connect company documents, databases, APIs, SaaS platforms, and internal software. Create secure knowledge systems that answer with company-specific context and traceable sources.
Research, classification and drafting — steps that need reading and judgment at speed.
No outreach leaves without a salesperson's approval. The AI prepares; people decide.
Faster response to every lead, consistent CRM data, and sales time spent selling instead of researching.
Document generation, extraction and summarizing status across systems.
Only exceptions reach a manager — with full context attached, ready to decide.
Predictable throughput, fewer dropped requests, and a complete audit trail per case.
Triage, retrieval and answer drafting grounded in your own documentation.
Agents approve outgoing answers; low-confidence cases route straight to a person.
Shorter queues, consistent answers, and support staff focused on the hard cases.
Extraction from unstructured documents and cost-centre suggestions — always validated by rules.
Payments and bookings above thresholds always require sign-off.
Faster closing, fewer entry errors, and finance time shifted to analysis.
Correlation and summarization — turning raw signals into a readable incident picture.
No automated remediation runs without an engineer's explicit approval.
Less alert fatigue, faster triage, and documented incidents by default.
Retrieval and answer composition over your private document index.
Every answer cites its sources; uncertain answers say so and route to people.
Institutional knowledge that stops living in individual inboxes — and can run fully locally.
Something happens in the business. An email arrives. A form is submitted. A record changes. A schedule fires. Every workflow starts with a concrete, observable event — never with "the AI decides to do something."
The workflow gathers the necessary data. Customer history, document contents, system state, business rules. The model only sees what it needs — scoped, permissioned, and logged.
An AI model classifies, summarizes, extracts, or proposes an action. This is where AI earns its place: reading unstructured information and producing structured, checkable output. Proposals — not unilateral decisions.
Rules, validation, access permissions, and human approvals limit what can happen. Schema validation, permission checks, thresholds, and approval steps sit between the model and your systems. Uncertain output stops here and goes to a person.
The approved result is written back to the relevant systems. CRM updated, document filed, message sent, ticket closed — with a complete audit trail of what happened and why.
"Reliable automation comes from combining deterministic logic with AI—not from giving a model unlimited access."
For organizations handling confidential documents, intellectual property, customer records, legal information, financial data, or sensitive operational knowledge — AI environments that run locally, on-premise, in a private cloud, or air-gapped.
On-premise. Inference, retrieval and orchestration run on hardware in your own facility. No prompts or documents leave the building. Remote model APIs are not required.
Typically from €20,000, depending on hardware, integrations, security requirements, and support.2
We identify processes with enough repetition, cost, friction, or strategic value to justify automation.
We document systems, data, exceptions, permissions, and human decision points.
We build and test a controlled proof of value using representative data.
We connect production systems, add monitoring and controls, and document the solution.
We review failures, usage, costs, and business outcomes, then refine the workflow over time.
Timelines depend on data access, integrations, security requirements, and process complexity.
We start with the process, not the model. If a spreadsheet formula solves it, we will tell you.
We select technology based on the job rather than forcing every use case into one platform or one vendor.
Public cloud, private cloud, on-premise or air-gapped. Sensitive workloads can remain inside controlled infrastructure.
Approval steps, thresholds and escalation paths are part of the architecture — not an afterthought.
We design for failure states, exceptions, permissions, and monitoring. Systems you can operate, not demos you babysit.
You receive documentation and ownership — not a mysterious black box. Our public research works the same way.
An independent, practical, evidence-led guide to deploying and benchmarking large language models locally on AMD Ryzen AI MAX+ 395 / Strix Halo systems. The project covers local model serving, Ubuntu configuration, Ollama, llama.cpp, Vulkan/RADV, model selection, benchmarks, raw evidence, and reproducibility.
Adjust the sliders to your situation. Useful for prioritizing — not a promise.
Directional estimate—not a guaranteed financial result. Assumes ~46 productive weeks per year.
Get a process-specific estimateFor companies that need to identify and prioritize automation opportunities before committing to a build.
Custom scope
Discuss an auditFor a specific recurring business process that should become a reliable, monitored system.
Project-based
Discuss your processFor sensitive or local workloads that should never depend on a public AI service.
Typically from €20,000
Plan a deploymentA workflow follows a defined path: trigger, steps, output. It is predictable and easy to test. An AI agent operates with more autonomy inside boundaries — it can decide which tool to use, gather missing information, and adapt its approach to the situation.
Most production systems combine both: deterministic workflows for reliability, with agents handling the steps that need reading, judgment or research. We choose per step, not per fashion.
Good candidates repeat frequently, follow recognizable patterns, touch multiple systems, and consume meaningful employee time. Invoice intake, lead qualification, report preparation, ticket triage, onboarding steps, and document processing are typical examples.
Poor candidates are one-off tasks, processes that change every week, or decisions that genuinely require accountability and human judgment. Part of our job is telling you which is which.
Usually, yes. If a system has an API, database, webhook, email interface, or even a structured export, it can typically be connected. We work with common platforms (Microsoft 365, Google Workspace, HubSpot, Salesforce, Exact, AFAS, and many more) as well as internal software.
We confirm compatibility during technical discovery rather than promising it blindly.
Yes, and for consequential actions we recommend it. Approval steps are first-class citizens in our workflows: the AI prepares the work, a person reviews it in a familiar tool (email, Slack, Teams, or a simple dashboard), and only after approval does the system act.
Approval thresholds are configurable — small routine actions can run automatically while anything above a defined impact level waits for a person.
Uncertainty is routed, not hidden. Workflows include confidence checks, validation rules, and structured output requirements. When a result fails validation or the model signals low confidence, the case is escalated to a human with full context — the original input, what the AI attempted, and why it stopped.
Yes. In on-premise and air-gapped configurations, models run on hardware you control, and no prompts or documents leave your infrastructure. In private cloud configurations, data stays within your tenancy.
Which architecture fits depends on your data sensitivity, performance needs and budget — we design this per project and document exactly where data travels.
Typically: architecture design, hardware and model recommendations, a local inference environment, a private knowledge/RAG system, role-based access control, an internal API for your tools, monitoring, documentation, and deployment support. Projects typically start from €20,000, depending on hardware, integrations, security requirements and support.
No. Cloud models are often more capable per euro and easier to scale. Local AI wins when data must not leave your infrastructure, when regulation demands it, when latency or cost profiles favor it at volume, or when you need guaranteed availability independent of a provider.
Many clients run a hybrid: sensitive workloads local, everything else in the cloud. We will recommend the boring, correct option — not the impressive one.
Yes. We design for data sensitivity from the first architecture sketch: data minimization, role-based access, encrypted secrets, audit logging, and — where required — fully local processing. We are happy to sign an NDA before technical discovery begins.
It depends on data access, integrations, security requirements, and process complexity. A contained workflow prototype can be a matter of weeks; a private AI infrastructure project takes longer. After discovery we commit to a concrete plan with decision gates — we do not quote fixed timelines before understanding the process.
You do. Deliverables include documented workflows, configuration, and code with a proper handover. You are not locked into us — although most clients keep us involved for improvements and operations.
Every system includes monitoring and documentation at launch. From there you can operate it yourself, or engage an AI Operations Retainer: we watch performance and costs, handle failures, evaluate model updates, and build the next workflows on the same foundation.
Yes — we recommend it. One well-chosen process proves the approach, builds internal trust, and creates the technical foundation (integrations, monitoring, approval patterns) that every following workflow reuses.
Each workflow logs its runs: inputs handled, decisions made, approvals requested, failures and costs. You see this in dashboards and periodic reviews. Monitoring is part of the initial build, not an upsell.
Honestly: no one can guarantee a model never produces a wrong answer, and you should distrust anyone who claims otherwise. What we can do is make wrong answers inconsequential: structured outputs with schema validation, business-rule checks, permission boundaries, human approval for consequential actions, test suites against representative cases, and monitoring that catches drift.
The model proposes; the system verifies; people approve what matters.
Single production workflows are project-based and scoped after discovery. Private AI infrastructure typically starts around €20,000, depending on hardware, integrations, security requirements and support. An AI Opportunity Audit is the lower-commitment way to find and prioritize the right first project. We will tell you early if the economics do not make sense.
No. We handle design, build, and deployment, and we document everything for handover. An internal contact who knows the process well matters far more than internal developers. For on-premise deployments, someone responsible for your infrastructure is helpful; we plan maintenance together.
Yes. For sensitive projects we can sign an NDA before any technical discovery. Mention it when you book a call and we will arrange it up front — yours or ours.
Yes — that is how the best systems grow. The first workflow establishes integrations, approval patterns and monitoring. Later workflows plug into that foundation, which makes each subsequent automation faster and cheaper than the first.
We are deliberately model-agnostic. Depending on requirements we work with leading commercial models, open-weight models served locally (via e.g. Ollama or llama.cpp), and orchestration platforms such as n8n or custom code where a platform is the wrong fit.
The selection criteria are boring and correct: capability for the task, data-handling requirements, cost per run, and maintainability — in that order.
Bring us one recurring process, one operational bottleneck, or one sensitive AI requirement. We will help determine whether it should become an agent, a workflow, a private AI system—or remain human.