Assistants on your data
Answers grounded in your own documents and systems, with sources, not general web knowledge.
Liox AI Platforms
AI that does a job,
AI that does a job,
not a demo.
Most AI projects stall because they start with the technology instead of the task. We start with a process that costs you time or money, and build the smallest thing that fixes it.
Overview
AI Platforms
There is a large gap between an impressive demo and something a team relies on every day. The demo ignores messy data, edge cases, permissions and the question of who is accountable when the model is wrong. Production cannot.
We build AI into workflows that already exist: support that drafts answers from your own documents, operations that classify and route automatically, internal assistants that search knowledge nobody can find. The measure is whether the work gets faster, not whether the output sounds clever.
What we build
What this includes.
Process automation
Classification, extraction and routing for the repetitive work that fills your team’s day.
AI inside your platform
Features built into the product your customers already use, rather than a separate tool.
Evaluation and guardrails
Measurable accuracy, defined limits, and a human in the loop where the stakes require one.
Why Liox
How we work.
Your data stays yours
Clear boundaries on what is sent where, and what is retained.
Costed honestly
Usage-based costs modelled before you commit, not discovered on the first invoice.
Scoped small first
One workflow proven properly beats five half-built experiments.
Arabic and English
Built and evaluated in both languages, which most implementations skip.
Questions
Common questions.
Do we need our own AI model?
Almost never. Most business value comes from applying existing models well to your specific data and workflow. Training a model from scratch is rarely the right first step.
What does AI actually cost to run?
It depends on volume, but for most internal workflows the running cost is a small fraction of the time saved. We model expected monthly usage during scoping so there are no surprises.
Is our data safe?
We scope data handling explicitly at the start: what leaves your systems, what is retained, and what stays entirely internal. Those choices are yours, and they are written down.
Where should we start?
With one repetitive, high-volume task that a person currently does by hand. That is where the return is easiest to measure and the risk is lowest.
Ready to get started?
Let’s build something
extraordinary together.
Tell us a little about your project and we’ll get back to you to discuss the right direction.