What We Do
Your support team reads 500 tickets a day and manually tags each one. Your legal team reviews contracts clause by clause looking for non-standard terms. Your product team scrolls through thousands of app store reviews hoping to spot patterns. All of that is human time spent on work a machine can do faster and more consistently. Natural language processing automates the reading, classifying, and routing of text at volumes no team can match manually.
A support ticket arrives and the system classifies it by category, detects urgency from the language, extracts the order number, and routes it to the correct queue in seconds. A contract uploads and the system flags deviation from standard terms, extracts renewal dates and liability caps, and surfaces the three clauses that need attorney review. Customer reviews across every platform are scored for sentiment in real time, clustered by topic, and surfaced as a weekly digest showing exactly what customers love and what is driving complaints. The text your business already generates becomes structured, searchable, and actionable without anyone reading it line by line.
How We Work
We start by mapping your document landscape: what text types come in, how many per day, what information needs to be extracted from each, and where the results need to go. That inventory determines the pipeline architecture. For classification tasks like ticket routing or document triage, we label a representative sample of your actual documents, train a classifier on your vocabulary and categories, and validate accuracy against a held-out test set before deployment. For extraction tasks like pulling dates, dollar amounts, names, or contract terms from unstructured text, we configure entity recognition models tuned to your specific document formats. Legal contracts, medical intake forms, and purchase orders each have different structures, and the extraction model reflects that.
For sentiment and topic analysis, we calibrate scoring models against your domain language. A five-star review that says the product is fine means something different in hospitality than it does in enterprise software. The processing pipeline handles your actual daily volume with monitoring that detects accuracy drift over time. When new document formats appear or language patterns shift, the system flags degradation and triggers retraining. Output is delivered wherever your team needs it: written directly to your CRM, pushed to a Slack channel, exposed via API for your internal tools, or compiled into scheduled reports.