NLP that reads what your team can't
Every business is sitting on text it can't read fast enough: support tickets, contracts, reviews, call transcripts, intake forms, emails. We build natural language processing systems that read all of it, pull out what matters, and route it, in English, Spanish, and Portuguese.
Text is data. Most companies just can't use it yet
The information is already in your inbox and your files. NLP is how it becomes something you can act on.
Book a strategy session ($350)A good fit if you
- Receive more messages, documents, or reviews than a person can read carefully
- Have contracts, forms, or records where a few fields matter and the rest is noise
- Want to know what customers are actually saying across thousands of reviews and calls
- Route requests by hand and get it wrong often enough to notice
- Operate in more than one language
Natural language processing is the branch of AI that works with human language: reading it, sorting it, pulling facts out of it, summarizing it, and judging its tone. It's the technology behind a system that reads a thousand support emails and files each one correctly, or scans a stack of contracts and pulls the renewal date and the liability cap from every one.
We build NLP systems for a specific job with a specific output. Classification that puts every incoming request in the right queue. Extraction that turns a PDF into structured fields in your database. Summarization that gives a manager the five-line version of a forty-minute call. Sentiment analysis that tells you what shifted in customer opinion last week, and about what.
Because we work in South Florida and Latin America, our systems are built multilingual from the start. A support inbox that gets English, Spanish, and Portuguese messages is handled by one system, not three, and the Spanish is understood as Spanish rather than translated first and understood second.
What we build
Each capability is delivered as a working system connected to where your text lives and where the output needs to go.
Tools and stack we work in
- OpenAI
- Anthropic
- Google Gemini
- spaCy
- Hugging Face
- LangChain
- Pinecone
- pgvector
- Elasticsearch
- Azure AI Language
- AWS Comprehend
We choose between large language models and lighter purpose-built models based on accuracy, cost, and how much of your data should leave your environment. Often the right answer is both.
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Text classification and routing
Emails, tickets, chats, and forms sorted by topic, urgency, and department, and sent to the right person with a confidence score.
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Information extraction
Names, dates, amounts, policy numbers, addresses, and clauses pulled from documents into structured fields in your system of record.
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Document analysis
Contracts, applications, and reports read for the fields and risks you define, flagged when something is missing or unusual.
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Summarization
Long calls, threads, and documents condensed into the version a busy person needs, with the source one click away.
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Sentiment and intent analysis
What customers feel and what they want, across reviews, surveys, social mentions, and transcripts, tracked over time.
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Search and retrieval
Find the answer inside your own documents by asking a question, not by guessing the file name.
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Call and chat transcript analysis
What your sales and support conversations reveal about objections, pricing questions, and missed opportunities.
Where NLP pays off
Anywhere language arrives faster than people can process it.
Insurance agencies
Application and claims documents read for completeness, policy details extracted, and inbound requests routed by type and urgency.
Legal and professional services
Contract review for defined clauses, intake questionnaires classified by matter type, and discovery documents searched by question.
Medical and aesthetic practices
Patient messages triaged by urgency, intake forms structured into the record, and review sentiment tracked by provider and location.
Home services
Service requests classified by trade and urgency from whatever channel they arrive on, with address and problem extracted before dispatch sees it.
E-commerce
Product reviews mined for defects and feature requests, support tickets routed and drafted, and returns reasons categorized automatically.
Hospitality and multi-location businesses
Reviews across every location and platform analyzed for what's changing, where, and why, before it shows up in revenue.
What we found in the transcripts
We run call tracking on marketing campaigns, which means every client has hours of recorded conversations nobody has time to listen to. That's where NLP earns its keep first. Processed at scale, those calls reveal the objection that keeps coming up, the pricing question the website never answers, and the service the ads promote that callers rarely ask about.
That's still how we think about NLP. It's not a technology demo. It's the fastest way to find out what your customers are telling you, in their words, at a scale no team can read by hand. Then we act on it, in the product, in the operation, and in the marketing.
How we work
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Discovery and data audit
We learn how the business makes money, where the decision or the workflow actually breaks, and what data exists to fix it. If the data isn't there yet, that becomes step one.
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Roadmap and architecture
One plan that names the outcome, the integrations, the guardrails, and the budget. Sequenced so the fastest win funds the longer build.
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Build and integrate
We build inside your systems and your accounts, connect to the tools you already run, and test against real data before anything touches a customer.
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Measure and improve
Monthly reviews on the business number the system was built to move, not on model accuracy in isolation. We keep tuning after launch.
The same standards as everything else we do
You own the code and the data
Source, models, prompts, pipelines, and accounts are yours. If we ever part ways, everything we built stays with you.
Revenue model first
We ask how the company makes money before we ask which model to use. AI that doesn't move a business number is a science project. It's how we work on everything.
No hidden fees
Scope is written before it's priced. Cloud and API costs are passed through at cost, not marked up.
Senior people, always
The engineer who scopes your system is the one who builds it. No handoff to a junior pod after the contract.
Guardrails by default
Human review where decisions carry risk, logging on every automated action, and a kill switch you control.
Three languages
Systems that read, write, and talk in English, Spanish, and Portuguese, because your customers do. See where we operate.
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Questions we get asked
A chatbot uses NLP, but NLP is much broader. Most of what we build with it never talks to a customer: it reads documents, routes tickets, analyzes reviews, and extracts data. Conversational systems are covered under AI Customer Engagement.
Yes, natively. Our systems process each language directly rather than translating to English first, which matters for accuracy and for tone. Spanish as written in Miami, Bogotá, and Madrid is handled as such.
It depends on the task and the data. Classification and extraction on well-defined fields routinely exceed what a rushed human achieves. We measure accuracy on your data before launch, report it plainly, and route low-confidence cases to a person.
Only if you decide they should. For sensitive material we can run models inside your cloud or on infrastructure you control. Where a hosted model is appropriate, we use providers with contractual no-training guarantees.
A single classification or extraction pipeline typically ships in three to six weeks. Broader systems, like searching across your entire document store by question, are phased projects.
Scanned and handwritten documents go through OCR first, which is covered under our computer vision work. The two are often built together: vision reads the page, NLP understands it.
Your customers are telling you. Start listening at scale.
Bring the inbox, the document type, or the review set that's overwhelming your team. Thirty minutes tells us whether NLP fixes it and what it would take.
Book a strategy session ($350)
