Your business runs on text. Contracts, tickets, reviews, reports, emails, filings. Humans read maybe 5% of it. The other 95% contains patterns, risks, and opportunities that nobody has time to find. We build NLP systems that read every word and surface what matters.
Document intelligence. Sentiment analysis. Entity extraction. Classification at scale.
Build Your Language SystemNLP is not one capability. It is a stack of technologies that build on each other. Tokenization feeds entity recognition. Entities feed classification. Classification feeds routing. Each layer makes the next one smarter.
Before AI can understand language, it needs to decompose it. Splitting text into tokens, identifying sentence boundaries, resolving co-references. The invisible foundation that determines whether everything above it works or fails.
Extract people, companies, dates, dollar amounts, locations, product names, and custom entities from unstructured text. Not keyword matching. Contextual understanding that knows 'Apple' the company from 'apple' the fruit.
Beyond positive and negative. Detect frustration, urgency, sarcasm, purchase intent, churn risk, and satisfaction. The difference between a customer who says 'fine' and means it, and one who says 'fine' and is about to leave.
Categorize incoming text into dozens of buckets simultaneously. Support tickets by urgency and topic. Emails by intent and department. Documents by type and required action. Humans set the categories. AI handles the volume.
Turn a 40-page report into 3 paragraphs. A 2-hour call transcript into action items. A month of customer reviews into trends. The AI reads everything so your team only reads what matters.
Not word-for-word substitution. Domain-aware translation that preserves meaning, tone, and technical accuracy. Legal terms that translate to the correct legal terms. Medical terminology that stays medically precise.
Every NLP project starts with the same question: how much does the AI need to understand? Basic pattern matching solves simple problems. Deep language models solve hard ones. The right approach depends on your text, your domain, and what you need the system to do.
A compliance team needs to find every mention of a deadline in 10,000 contracts. The format is always similar: 'within X days,' 'no later than DATE,' 'by the end of Q3.' Patterns are consistent. Machine learning would be overkill.
Regular expressions, keyword lists, and hand-written rules. Zero training data required. 100% predictable behavior. When your text follows known patterns, rules are faster to deploy and easier to audit than any ML model.
An insurance company needs to classify claim descriptions into 40 categories. The language is messy, inconsistent, and full of abbreviations only adjusters understand. Rules cannot handle the variation.
Take a pre-trained language model and teach it your domain. It learns your vocabulary, your abbreviations, your edge cases. Feed it thousands of labeled examples from your actual data, and it starts classifying, extracting, and routing text like your best domain expert.
A global bank processes documents in 14 languages containing a mix of structured tables, free-text paragraphs, and handwritten annotations. No single model handles all three. The pipeline needs to orchestrate multiple models working together.
When your problem spans multiple NLP tasks, languages, or document types, we design a pipeline: OCR feeds NER feeds classification feeds extraction. Each component is optimized for its specific job. The architecture is built around your data, not around what a single model can do.
Stranger video chat platforms attract bad actors. Reports pile up faster than human moderators can read them. A new platform needed to detect and block high-risk users before they harmed the community. But user reports are messy text: varied language, slang, abbreviations, vague descriptions. No off-the-shelf classifier understands “this user’s behavior.”
Starting from user reports of inappropriate content, we built a classification system that extracted structured behavioral signals from unstructured text. The AI learned to identify patterns in report language, frequency, and context that predicted high-risk users with high accuracy. Not keyword filtering. Contextual understanding trained on the platform’s actual data.
This is NLP at its most consequential. Not summarizing documents. Not classifying emails. Keeping real people safe by reading the signals that human moderators miss at scale.
Read the full case study →Adjusters spend 60% of their time reading claim descriptions, not evaluating them. NLP reads the description, extracts the incident type, estimates severity, flags potential fraud indicators in the language, and routes the claim to the right adjuster with a structured summary. Fraud patterns hidden across thousands of claims become visible.
60% of adjuster reading time redirected to decision-making
A news organization publishes 300 articles per day. Tagging, categorizing, and linking related content is a full-time job for three editors. NLP reads every article, extracts entities, assigns topic tags, detects duplicate coverage, links related stories, and identifies trending narratives across your entire archive. Your editors curate. The AI organizes.
Automated tagging and entity linking across 300+ daily articles
A proposed regulation generates 50,000 public comments. A team of 15 analysts spends 4 months reading and categorizing them. NLP reads all 50,000 in hours, clusters them by theme, detects coordinated campaigns, extracts unique concerns, and produces a structured summary that meets federal documentation requirements.
50,000 public comments analyzed in hours, not months
When a customer calls to 'discuss their plan,' they might want an upgrade, a discount, or they are about to cancel. NLP on call transcripts and chat logs detects the real intent behind vague language, classifies every interaction by outcome probability, and routes high-risk conversations to retention specialists before the customer asks to leave.
Intent classification that catches churn signals in real time
A drug development team needs to track every published paper, clinical trial result, and adverse event report related to their compound. 200 new papers appear every week. NLP monitors publication feeds, extracts relevant findings, links them to your internal research, flags contradictions with your data, and surfaces the papers that actually matter for your trial.
200 papers per week monitored and relevant findings surfaced automatically
A commercial real estate firm manages 400 active leases. Each one contains different escalation clauses, renewal terms, and maintenance obligations buried in 50-page documents. NLP extracts every key term, builds a searchable database of obligations, alerts your team 90 days before every deadline, and flags non-standard clauses across your entire portfolio.
400 leases with every clause extracted and every deadline tracked
Every contract, every report, every filing contains insights trapped in unstructured text. NLP unlocks them at a scale no human team can match.
Extract key terms, obligations, deadlines, and liability clauses from contracts in seconds. Compare new contracts against your standard terms. Flag deviations that need legal review. Your lawyers focus on judgment calls, not document review.
Extract figures from earnings reports, 10-Ks, and financial statements. But also the narrative: management sentiment, risk language, forward-looking statements. Structured data from unstructured filings, ready for your models.
Parse clinical notes, discharge summaries, and pathology reports. Extract diagnoses, medications, procedures, and outcomes. Handle abbreviations, misspellings, and the shorthand that only clinicians understand.
Classify by urgency, product area, sentiment, and root cause. Identify emerging issues before they become trends. Route to the right team without human triage. Track resolution patterns across thousands of tickets.
Generic NLP models were trained on Wikipedia and news articles. Your industry has its own language. Here is what happens when you try to use general-purpose tools on specialized text.
You have thousands of reviews, support tickets, survey responses, and social mentions. Your team reads maybe 2% of them. NLP reads 100%.
NLP clusters customer feedback into themes without predefined categories. It finds what customers are talking about, not what you assumed they would talk about. New themes surface automatically as customer language shifts.
A one-time NPS snapshot tells you where you are. NLP-powered sentiment tracking tells you where you are headed. Track sentiment by product, by feature, by customer segment, by week. Spot the trend before it reaches your retention numbers.
Your competitors' customers write reviews, post on forums, and comment on social media. NLP reads all of it and tells you what their customers love, what they hate, and what they wish existed. Your product roadmap, informed by their feedback.
A single complaint is noise. Five complaints about the same issue in a week is a signal. NLP detects complaint clusters in real time, tracks escalation patterns, and alerts your team the moment a new issue begins trending. You respond to problems when they affect 5 customers, not 500.
A medical NLP system misreads “denies chest pain” as “chest pain.” A compliance scanner flags a safe contract clause as high-risk, burying your legal team in false alarms. A sentiment analyzer marks sarcasm as praise, and your team celebrates a product that customers actually hate. NLP accuracy is not a nice-to-have metric. It is the entire point.
Off-the-shelf NLP gives you a confidence score and hopes for the best. We engineer systems where errors are caught, measured, and systematically eliminated.
Every NLP task has a trade-off: catch everything (high recall, more false positives) or only flag certainties (high precision, some misses). We tune this dial to your risk tolerance. A fraud detector needs high recall. A legal clause extractor needs high precision. You set the threshold. We engineer the system to hit it.
We evaluate models on your data, not benchmark datasets. F1 scores on academic corpora mean nothing if the model fails on your actual documents. Every model we ship comes with evaluation metrics measured on your text, your edge cases, your domain-specific vocabulary.
When the model is uncertain, it flags the case for human review. Your team's corrections become tomorrow's training data. The model improves continuously from the cases it finds hardest. Six months in, the edge cases that tripped it up on day one are handled automatically.
Every prediction is logged with its confidence score and the features that drove the decision. When errors occur, you can trace exactly why the model made that call. Not a black box. An auditable, explainable system that your compliance team can inspect.
Every system we deploy includes accuracy monitoring that alerts your team when performance drops below the threshold you set. Not after a quarterly review. The moment it happens.
What text data do you have? What is the domain? What are you trying to extract, classify, or understand? We audit your corpus, design the annotation schema, and define the evaluation metrics that matter for your use case.
We prepare your text data: cleaning, normalization, deduplication. For supervised tasks, we set up annotation workflows with quality control. For domain-specific work, we build the custom vocabulary and entity definitions your model needs.
We select the right architecture for your task, train on your data, and iterate. Every model is evaluated against your real documents, not benchmark datasets. We optimize for the metrics you defined: F1, precision, recall, BLEU, ROUGE, whatever measures success in your domain.
The model gets wrapped in a production pipeline: input validation, preprocessing, inference, post-processing, and output formatting. Connected to your document sources, your databases, your workflows. Not a standalone tool. An integrated part of your systems.
Language changes. New terms appear. Customer communication patterns shift. Regulations add new terminology. We monitor model accuracy, detect vocabulary drift, and retrain when performance degrades. Your NLP system stays current as your language evolves.
Your text data is your most underused asset. Time to read all of it.
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