Turn Unstructured Text Into Something Usable
Support tickets, reviews, documents — most of it just sits there because nobody has time to read all of it. We build systems that actually process it.
The Problem
Most Text Data Just Sits There
Support tickets pile up, reviews go unread, documents get skimmed at best. There’s real signal in that text — what’s breaking, what people actually think, what needs attention — but reading all of it by hand doesn’t scale past a certain volume. Most of it just gets ignored.
What's Included
Text classification —
Routing and tagging text automatically, based on what it actually says.
Sentiment analysis —
Knowing what's negative, urgent, or worth escalating without reading every line.
Summarization —
Turning a pile of documents or tickets into something someone can actually review.
Chatbot/assistant development —
A system that can answer questions or handle requests directly, not just return search results.
How It Works
Use-case scoping —
What you're actually trying to extract or automate from the text.
Model or pipeline build —
Building the classification, extraction, or generation piece around your actual data.
Integration —
Connecting it to where the text already lives, not a separate tool nobody opens.
Evaluation —
Checking it against real examples before it's making decisions on its own.
Why moventAI
Text data is messy — typos, sarcasm, jargon specific to your industry, tickets that don’t fit any category cleanly. We build for that messiness from the start, instead of tuning against a clean sample set and hoping it holds up on real input.
Who It's For
- Support teams sitting on more tickets than they can manually triage.
- Product or ops teams with reviews, feedback, or documents nobody’s had time to actually process.