Anonymised client
PilotEnergy supplier, Romania
Customers leaving in a liberalised retail market.
Churn propensity scoring with explainable drivers and a four-tier retention playbook.
Enterprise AI · Built on ContextLab
Make your Enterprise AI-ready.
AI agents fail when they don't understand how your business works. ContextLab captures your processes, rules and expert know-how as structured context, so AI can act inside them: reliably, auditably, on your own infrastructure.
Ontology · Process · Formal Knowledge · Tacit Capture
01 / The problem
AI is only as good as the context it works in.
Most enterprise AI projects stall between the demo and production. The model works; the organisation around it is invisible to it.
01
It doesn't speak your language.
A “client”, an “order” or a “claim” means something specific in your company. Generic models guess.
02
It ignores your process.
Without the process as a boundary, an agent can propose steps your organisation would never allow.
03
It forgets your experts.
The judgment of your best people lives in their heads, not in any dataset.
02 / The technology
ContextLab: the context layer for enterprise AI.
Four layers turn the way your organisation works into something AI can reason within.
Layer 1
Ontology
Your entities, terms and relationships, defined once and versioned.
Layer 2
Process
Your workflows as explicit models: steps, owners, exceptions. The AI may act only where the process allows.
Layer 3
Formal Knowledge
Policies, rules, contracts and case history, retrievable and applied at the right step.
Layer 4
Tacit Capture
Expert judgment captured from real decisions and proposed as new rules, always confirmed by a human.
03 / The consequence
Context first. Agents follow.
Once your context is structured, agents stop being experiments. Each agent is assembled from the same four layers, plus the AI technology that fits the task: predictive models, language models, forecasting or document understanding. That is why they behave predictably, and why the second agent costs far less than the first.
Your context
Language, processes, rules, judgment
ContextLab
Four layers + AI Technologies
Enterprise AI Agents
Predictable, auditable
04 / Enterprise AI agents
Agents that work inside your processes.
Customer Intelligence
Know which customers are at risk, what to offer next, and why.
7 agents →Operational Excellence
Procurement, production, order intake and service flows that run with less manual effort.
3 agents →Risk & Claims
Find leakage, fraud signals and underwriting inefficiency before they reach the P&L.
2 agents →ESG & Reporting
Collect, check and structure sustainability data for regulatory reporting.
1 agents →05 / In production
Built with real clients, not in a lab.
Every part of ContextLab was developed through implementation at client sites. Names are withheld; the work is real.
Anonymised client
Food producer
Phone orders transcribed by hand at peak times.
Voice agent taking phone orders, about 200 per day.
Anonymised client
PilotInternational BPO company
Sustainability data spread across sites and spreadsheets.
ESG reporting pilot for FY2026 data.
ResultIn progress
06 / Why us
Why Axiobit.
- Process as a boundary.
The process model constrains what the AI may output at each step. The AI reasons inside the process; it does not improvise around it.
- Built for small data.
ML plus LLMs designed for the data volumes real mid-size and large companies actually have.
- Runs on your infrastructure.
Designed for deployment on company infrastructure, on-premise or in a private European cloud. Deployment requirements are agreed for each engagement.
- Auditable.
Ontology, process and rules are versioned artefacts a compliance reviewer can read without looking inside a model.
- 30+ years of process engineering.
ContextLab's process layer builds on decades of BPM engineering, not on a model vendor's roadmap.
07 / AI-readiness
How AI-ready is your organisation?
Twelve questions, about six minutes. You get a score for each ContextLab layer and the three steps that would move you forward fastest.
Let's look at one process together.
Bring one process that matters. We'll show you what it takes to make it AI-ready.