AI and machine learning that ships
PathOr builds AI systems that go into production and stay there. Not proofs of concept that impress in a demo and quietly get switched off — working software that handles real volume, degrades gracefully, and someone other than its author can maintain.
Custom models
Trained on your data for your problem, not a generic API call.
NLP and automation
Understanding language well enough to act on it.
Intelligent analytics
Turning what the system sees into decisions you can act on.
Production first
Built to be deployed, monitored and maintained.
What we build
The most common request is automating something a person currently does by reading text and deciding what to do about it — triaging support enquiries, extracting structure from documents, routing work to the right team. That is natural language processing applied to a specific business process, and it is where the returns are clearest.
Beyond that: custom models trained on your own data where an off-the-shelf service will not do; conversational assistants built on the same platform as AvA; and analytics layers that make a model’s output legible to the people who have to act on it.
Why the AvA experience matters here
PathOr runs its own AI product. AvA is a live assistant platform with real users, real latency requirements and real failure modes, and the team maintaining it is the team that would work on your system.
That changes the advice you get. An agency that has only ever built AI for clients optimises for the demo, because the demo is where their involvement ends. A team that operates its own platform has had to answer for what happens at three in the morning in month eight.
How an engagement runs
It starts with the process you want changed, not the technology. If the honest answer is that your problem does not need machine learning — that a well-written rule or a fixed integration would do it faster and cheaper — that is the answer you will get.
Where AI is the right tool, work is scoped to a first deployable slice rather than a full platform. Something narrow in production teaches you more about whether the approach is right than a long build does.
Common questions
What kinds of AI projects does PathOr take on?
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Custom model development, natural language processing, intelligent automation of text-driven processes, conversational assistants, and analytics layers over model output. The common thread is systems intended for production rather than demonstration.
Do we need our own data to work with PathOr?
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Not always. Some problems are best served by a custom model trained on your data; others are better solved with existing models applied carefully to your process. Which one applies is part of the initial scoping conversation.
How does PathOr’s own product experience help?
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PathOr operates AvA, its own AI assistant platform, with live users. The team has had to run AI in production rather than only build it for handover, which shapes how systems are scoped, deployed and monitored.
Have a process worth automating?
Describe it and we will tell you whether AI is genuinely the right tool for it, or whether something simpler would serve you better.
Start a conversation