Evolved360 AI
Text Your Business Generates
Is Data You're Not Using.
Sentiment Analysis. Entity Extraction. Document Intelligence. Intent Classification.
Customer emails, support tickets, survey responses, contracts, and intake forms contain structured insight — if you have a system to extract it. NLP turns unstructured text into queryable data, automates document review, and enables intelligent routing and classification at scale.
Your NLP Partner
Your support team reads 500 emails a day looking for the same 15 issue types. A classifier can do that in milliseconds.
NLP is the AI layer that processes language — reading, understanding, and extracting meaning from text at a speed and scale that no human team can match. The business applications are concrete: routing support tickets before a human reads them, extracting structured data from contracts, scoring sentiment in customer feedback, and classifying intent in chat conversations. Every application reduces manual processing overhead and improves response speed.
1000x
Faster than manual text review
95%+
Classification accuracy achieved
10+ yrs
AI implementation experience
SOC 2
Type 2 certified team
What Changes
What operations look like when language becomes structured, searchable data.
Automated Triage & Routing
Support tickets, emails, and inbound messages classified and routed to the right team or agent automatically — before a human reads the first line.
Sentiment at Scale
Customer feedback scored for sentiment across thousands of responses simultaneously. Identify satisfaction trends, product issues, and service failures without manual review.
Document Data Extraction
Pull structured data from contracts, invoices, intake forms, and applications automatically. Eliminate manual keying and downstream data quality errors.
Intelligent Search
Search your document library by meaning, not just keywords. Surface relevant content even when users can't remember the exact terminology used.
What We Build
NLP applications built for specific, high-volume text workflows.
Text Classification
Multi-class models that categorize inbound text by topic, intent, urgency, or department. Trained on your specific vocabulary and ticket types — not generic categories.
Named Entity Recognition
Extract names, organizations, dates, monetary values, locations, and custom entity types from unstructured text. Output structured data from documents that previously required manual review.
Sentiment & Tone Analysis
Score customer feedback, support interactions, and social mentions for sentiment polarity, emotional tone, and satisfaction indicators — at any volume.
Document Intelligence & OCR
Combine optical character recognition with NLP to extract structured fields from scanned documents, PDFs, and handwritten forms with high accuracy.
Summarization & Extraction
Automatic summarization of long documents, call transcripts, and meeting notes. Extract key decisions, action items, and topics without reading every word.
LLM Integration & Fine-Tuning
Integrate large language models (GPT-4, Claude) into your workflows, or fine-tune open-source models on your proprietary data for tasks that require domain-specific language understanding.
NLP classification systems reduce manual triage time by 80–90% in high-volume support and document processing operations.
Book NLP AssessmentFull AI Coverage
NLP is most powerful when connected to your full AI stack.
Text classification feeds automation workflows. Sentiment data feeds your CRM. Extracted entities feed your reporting. We integrate NLP outputs into the systems that act on them.
What Changes
What support and document operations look like when text stops requiring human eyes for every line.
Client result
“Our support team was spending 30% of their time triaging tickets and routing them to the right queue. The NLP classifier handles that now — 94% accuracy out of the gate, improving over time. That's half a person's work back into actual support resolution instead of routing.”
Head of Customer Support · SaaS Platform · ETG client since 2023
The Case for NLP
Why unstructured text is one of the highest-value untapped data sources in most businesses.
Most business data is structured — numbers, dates, categories in databases and spreadsheets. But most business information is unstructured — written in emails, tickets, contracts, transcripts, and notes. NLP is the technology that bridges this gap, converting the language your business generates into structured data that can be queried, aggregated, and acted on programmatically.
The highest-value NLP applications tend to be in two categories: high-volume classification (routing hundreds of support tickets per day) and document extraction (pulling structured fields from contracts, invoices, or forms that were previously reviewed manually). Both categories have clear, measurable ROI: the time saved per processed item multiplied by volume. For most businesses with meaningful document or ticket volume, the ROI calculation is straightforward and the payback period is under six months.
The NLP landscape has changed significantly with large language models. Many tasks that previously required careful fine-tuning of a custom model can now be accomplished with few-shot prompting of a hosted LLM. We assess which approach — custom trained classifier, fine-tuned model, or LLM with structured prompting — is appropriate for your specific task, volume, latency requirements, and data sensitivity before recommending an architecture.
“The NLP question I ask clients is: what text does your team read every day, and what decision does reading it produce? If the answer is consistent — the same types of text always produce the same category of decision — that's automatable. The pattern exists. We just need to teach the model to see it.”
Kevin Nishimura, CTO — Evolved Technology Group · SOC 2 Type 2 Certified · 10+ Years AI Implementation
Common Questions
Frequently asked questions.
Ready to turn your unstructured text into structured intelligence?
Book a free NLP assessment. We'll review your text workflows, identify the highest-ROI classification or extraction problem to solve first, and show you what an implementation looks like for your volume and data sensitivity.