01 — Services
We build the AI systems companies actually run on: agents, RAG, custom LLMs and process automation. Ten years shipping what works in production, not what sounds good in a proposal.
01 — Services
We build the AI systems our clients run their business on. Over ten years across AI and big data projects have taught us what works in production and what doesn't.
Let's talk about your caseRe-engineering operational flows with AI built in, from ingestion through to decision-making.
Proprietary models, fine-tuning, and inference stacks designed for your data and constraints.
Multi-agent orchestration with rigorous evaluation, observability, and security controls.
Production-grade retrieval over complex corporate corpora: accurate, cited, and governed.
From hypothesis to launch: opportunity sizing, roadmap, and experienced delivery.
Deployment pipelines, serving, observability, and controls to operate models reliably in production.
04 — Use cases
Examples of the type of projects we take on across our two service areas. If your situation looks familiar, we can probably help.
An agentic AI system is an architecture in which one or more large language models (LLMs) can plan tasks, use external tools — such as databases, APIs, or search engines — and execute actions autonomously to achieve a goal. Unlike a conventional chatbot, an agent can complete multi-step workflows without human intervention at each stage.
RAG (Retrieval-Augmented Generation) is a technique that combines a language model with an information retrieval system. Instead of relying solely on the model's pre-trained knowledge, the system first retrieves the relevant documents or passages from a corporate corpus and delivers them to the LLM as context. The result is accurate, cited, and updatable answers without retraining the model.
Cost depends on scope, data complexity, and existing infrastructure. A conversational assistant or RAG pipeline over internal documentation can be delivered in four to twelve weeks. More complex agentic architectures or integrations with core systems require longer timelines and larger budgets. KAIX LAB works with a fixed scope agreed in writing before starting.
A basic chatbot or assistant connected to internal data can be live in two to four weeks. A production RAG system with evaluation, guardrails and observability requires four to eight weeks. Complex agentic architectures with multiple tools and security review take between eight and sixteen weeks. Timelines are agreed in writing during the diagnosis phase.
Robotic Process Automation (RPA) executes fixed rule sequences over interfaces or APIs; it does not understand context or handle variability. AI — especially LLM-based systems — can reason over natural language, interpret unstructured documents, adapt to variable inputs and make context-dependent decisions. For repetitive, well-defined flows, RPA is sufficient and cheaper; for processes with variability or that require semantic understanding, AI is necessary.
In-house training
We run in-house technical training, not open courses. Each course is designed around the client's case: RAG, agentic systems and MLOps on your team's stack, with the length and depth the team asks for.
We respond within 24 – 48 business hours. We'll suggest a first call to understand your case.
[email protected]