ZTABS builds NLP systems for Boston institutions that extract intelligence from unstructured text at scale — clinical notes locked in Epic EHRs, legal briefs across thousands of case files, financial filings that hide alpha signals in 10-K footnotes, and research papers that accumulate faster than any team can read.
Natural Language Processing is the branch of AI that enables computers to understand, interpret, and generate human language. For Boston's dominant industries — healthcare, legal, financial services, and academic research — NLP is transformative because these industries run on text. Enormous volumes of unstructured, human-written text that contains critical information but is effectively inaccessible to computational analysis without NLP. The scale of the unstructured text problem in Boston: Healthcare — a single teaching hospital generates 15-20 million clinical notes per year. These notes contain: diagnoses, treatment rationale, disease progression, adverse reactions, social determinants of health, and clinical observations that never make it into structured EHR fields. Researchers estimate that 80% of clinically useful information in the EHR is locked in unstructured text — meaning that population health analytics, quality measurement, and clinical research operate on only 20% of the available information.
Legal — a major Boston law firm manages 50,000+ active matters, each containing hundreds to thousands of documents: contracts, briefs, correspondence, deposition transcripts, court filings, and regulatory submissions. Lawyers spend 30-40% of their time reading and analyzing documents — a task that NLP can accelerate by 5-10x for many document types. Financial Services — investment analysts read: 10-K and 10-Q filings (each 50-200 pages), earnings call transcripts (each 5,000-10,000 words), analyst reports, news articles, regulatory filings, and patent applications. A quantitative research team at a firm like Fidelity or Wellington might need to process 50,000+ documents per quarter to maintain comprehensive market coverage — far beyond human reading capacity. Academic Research — Boston's research institutions publish 30,000+ papers per year. A researcher trying to stay current in their field must read hundreds of papers annually — and the pace of publication is accelerating.
NLP-powered literature analysis can: identify relevant papers, extract key findings, detect contradictions across studies, and generate summaries that compress a 20-page paper into 500 words. Boston's NLP opportunity is not just about building chatbots or classifiers. It is about unlocking the intelligence trapped in millions of documents that Boston's institutions have accumulated over decades — making that intelligence searchable, analyzable, and actionable. The technical landscape has shifted dramatically: pre-2018, NLP required hand-crafted features, domain-specific rules, and limited accuracy. The transformer revolution (BERT in 2018, GPT-3 in 2020, GPT-4 in 2023) fundamentally changed what NLP can do — enabling systems that understand context, handle ambiguity, and extract meaning from complex, domain-specific text with near-human accuracy. But for regulated industries, the technology alone is not enough.
Clinical NLP must handle medical abbreviations, negation detection ("patient denies chest pain" means the patient does NOT have chest pain), and the distinction between the patient's current condition and their medical history. Legal NLP must understand: jurisdiction-specific terminology, contractual language precision, and the hierarchical relationship between statutes, regulations, and case law. Financial NLP must distinguish between: forward-looking statements and historical facts, management spin and material disclosures, and the nuanced language that signals risk without explicitly stating it. These domain-specific challenges are why Boston institutions need NLP development partners with both technical capability and domain expertise.
Boston's NLP demand spans clinical text mining, legal document analysis, financial text analytics, and research automation.
Clinical NLP: Boston's hospitals and health systems need NLP for: Clinical Documentation Mining — extracting structured data from unstructured clinical notes: named entity recognition (identifying: medications, dosages, diagnoses, procedures, lab values, and anatomical locations in clinical text), assertion classification (determining whether an entity is: present, absent, hypothetical, historical, or related to someone other than the patient — "Mother had breast cancer" is a family history item, not a patient diagnosis), relation extraction (identifying relationships between entities: "Metformin 500mg prescribed for Type 2 Diabetes" connects a medication, dosage, and diagnosis), temporal reasoning (understanding the timeline of clinical events: "Patient was diagnosed with hypertension in 2019, started lisinopril in 2020, and blood pressure has been well-controlled since" establishes a temporal sequence that is clinically meaningful), and section detection (identifying the section of the clinical note — HPI, ROS, Physical Exam, Assessment, Plan — because the same text means different things in different sections).
Clinical Trial Matching — NLP-powered patient identification: parsing inclusion/exclusion criteria from clinical trial protocols (translating human-readable criteria like "Adults 18-65 with confirmed HER2-positive breast cancer who have not received prior trastuzumab" into computable queries), extracting patient characteristics from clinical notes (identifying patients who meet the criteria — even when the relevant information is only recorded in unstructured notes, not structured EHR fields), and matching patients to trials (ranking eligible patients by fit, flagging potential exclusions, and generating summaries for clinician review).
Quality Measurement — NLP-powered quality metric extraction: identifying quality-relevant events in clinical notes (falls, infections, readmission risk factors, medication errors, near-misses — events that are often documented in notes but not coded in structured fields), sentiment analysis on patient feedback (analyzing patient satisfaction surveys, complaint letters, and online reviews to identify themes and trends), and social determinants of health extraction (identifying housing instability, food insecurity, transportation barriers, and social isolation from clinical notes — information that affects health outcomes but is rarely captured in structured fields).
Legal NLP: Boston's law firms (Ropes & Gray, WilmerHale, Goodwin Procter, Mintz Levin, Foley Hoag) need NLP for: Contract Analysis — automated review and extraction: clause identification (finding specific clauses across thousands of contracts: termination, indemnification, limitation of liability, change of control, IP assignment, non-compete), key term extraction (extracting: party names, effective dates, termination dates, renewal terms, payment terms, and governing law from contracts of varying formats and styles), risk flagging (identifying clauses that deviate from the firm's standard terms or contain unusual provisions that require attorney review), and comparison (comparing a proposed contract against the firm's preferred terms, the counterparty's prior contracts, and industry benchmarks).
Litigation Document Review — NLP-powered document analysis for discovery: relevance classification (classifying documents as relevant, potentially relevant, or not relevant to the litigation — reducing the volume that attorneys must manually review), privilege detection (identifying documents that may be protected by attorney-client privilege or work product doctrine — flagging them for attorney review before production), key document identification (finding the "smoking gun" documents — communications, memos, and reports that are most likely to contain material evidence), and timeline construction (extracting dates, events, and participants from documents to automatically construct a chronological narrative of relevant events).
Financial NLP: Boston's financial institutions need NLP for: SEC Filing Analysis — structured extraction from financial disclosures: risk factor extraction (identifying and tracking changes in risk factor disclosures across quarterly filings — changes in risk factor language often signal emerging business challenges before they appear in financial results), management discussion parsing (extracting forward-looking statements, guidance changes, and tone shifts from MD&A sections — quantifying management sentiment and comparing it to actual performance), footnote mining (analyzing financial statement footnotes for: off-balance-sheet obligations, related party transactions, accounting policy changes, and contingent liabilities — the footnotes often contain the most material information in a filing), and earnings call analysis (processing earnings call transcripts to: extract guidance, identify analyst concerns, measure management confidence through linguistic analysis, and compare management statements to prior guidance and actual results).
Our NLP methodology for Boston institutions combines modern transformer-based approaches with domain-specific customization and regulatory compliance.
Phase 1 — Data and Domain Assessment: understanding the text corpus and domain requirements: corpus analysis (analyzing the target text corpus: volume, document types, language characteristics, quality, and variability — understanding what the NLP system will actually encounter in production), domain terminology mapping (building a domain-specific vocabulary: medical terminology for clinical NLP, legal terminology for legal NLP, financial terminology for financial NLP — identifying terms that general-purpose models handle poorly), annotation strategy (designing the training data pipeline: what needs to be annotated, who should annotate it, what annotation guidelines ensure consistency, and how much annotated data is needed — for clinical NLP, annotation requires clinicians with domain expertise; for legal NLP, it requires attorneys), and regulatory requirements (identifying data handling constraints: HIPAA for clinical text, attorney-client privilege for legal documents, material non-public information for financial documents — these constraints affect how data is stored, processed, and shared during development).
Phase 2 — Model Selection and Customization: choosing and adapting the right models for the task: base model selection — we evaluate models along multiple dimensions: for clinical NLP: ClinicalBERT, PubMedBERT, BioGPT, and general-purpose models fine-tuned on medical text, for legal NLP: Legal-BERT, Pile of Law models, and general-purpose models fine-tuned on legal text, for financial NLP: FinBERT, Bloomberg GPT concepts, and general-purpose models fine-tuned on financial text, and for general extraction: GPT-4, Claude, and open-source alternatives (Llama, Mistral) — evaluated for accuracy, cost, latency, and data privacy requirements.
Fine-tuning strategy — domain-specific fine-tuning on the institution's own data: continued pre-training (further training the base model on the institution's text corpus — teaching it the specific vocabulary, style, and patterns of the institution's documents), task-specific fine-tuning (training the model on annotated examples of the specific task — entity extraction, classification, summarization — using the annotation data from Phase 1), and few-shot and prompt engineering (for tasks where fine-tuning data is limited, designing prompts that leverage the model's existing knowledge while guiding it toward domain-appropriate outputs). Evaluation framework — rigorous, domain-specific evaluation: standard metrics (precision, recall, F1 for extraction tasks; accuracy, AUC for classification tasks; ROUGE, BERTScore for summarization), domain-specific metrics (for clinical NLP: does the system correctly handle negation, uncertainty, and temporal reasoning? For legal NLP: does it correctly identify the jurisdiction, parties, and governing law? For financial NLP: does it distinguish between historical and forward-looking statements?
), and expert evaluation (domain experts — clinicians, attorneys, analysts — reviewing system output for: factual accuracy, completeness, and actionability). Phase 3 — System Development: building the complete NLP pipeline: text preprocessing (document parsing, section detection, sentence segmentation, tokenization — handling the messy reality of institutional documents: scanned PDFs, faxes, multi-column layouts, tables, and headers/footers), NLP processing pipeline (the core extraction or analysis: named entity recognition, relation extraction, classification, summarization — chained together in a pipeline that produces structured output from unstructured input), post-processing and validation (confidence scoring, cross-reference validation, consistency checking — ensuring that the system's outputs are reliable before presenting them to users), and user interface (designing the interface for how domain experts interact with NLP output: search interfaces for querying extracted information, review interfaces for validating and correcting system outputs, dashboard interfaces for aggregate analysis and trend visualization, and export interfaces for downstream system integration).
Phase 4 — Deployment and Continuous Improvement: ongoing refinement based on production performance: human-in-the-loop deployment (the system presents NLP results to domain experts who validate, correct, and supplement the output — corrections feed back into model improvement), active learning (identifying cases where the model is uncertain and routing them for expert annotation — maximizing the value of expert annotation time by focusing on the most informative examples), performance monitoring (tracking model accuracy in production — detecting concept drift, vocabulary changes, and new patterns that require model updates), and model retraining (periodic retraining on accumulated corrections and new annotated data — maintaining and improving accuracy over time).
Free consultation — no commitment required.
A Boston academic medical center (850 beds, 4,200 physicians, 1.2 million patient encounters per year, $3.8 billion annual revenue) needed to extract structured data from clinical notes at scale — to support clinical research, quality measurement, and population health analytics. The hospital's EHR (Epic) contained 47 million clinical notes accumulated over 12 years — representing 1.2 million unique patients. Researchers estimated that these notes contained information about: 340,000 patients with undiagnosed or undocumented conditions (conditions mentioned in notes but not captured as structured diagnoses), 180,000 patients with social determinants of health documented in notes but not in structured fields (housing instability, food insecurity, substance use, social isolation), and clinical observations, treatment decisions, and outcomes that could inform thousands of potential research studies — if the data could be extracted and structured.
Specific problems: (1) Research study feasibility — the clinical research office received 200+ study feasibility requests per year from researchers asking: "How many patients in our system have [condition X] who also have [characteristic Y]?" When the condition and characteristic were captured in structured EHR fields, the query was straightforward. But 60% of feasibility requests involved criteria that were only documented in clinical notes — requiring manual chart review. A single feasibility assessment requiring chart review took 2-4 weeks and cost $8,000-$15,000 in research coordinator time. The research office could only process 40% of feasibility requests within the researchers' timelines — the rest were abandoned or delayed. (2) Quality measurement gaps — the hospital reported 87 quality metrics to CMS, insurance companies, and accreditation bodies. For 23 of these metrics, the relevant clinical information was partially or fully documented in unstructured notes rather than structured fields.
The quality team employed 12 abstractors who manually reviewed charts to extract quality metric data — a process that was: slow (abstractors reviewed 15-20 charts per day), expensive ($1.1 million per year in abstractor salaries and benefits), and error-prone (inter-rater reliability was only 82% — meaning abstractors disagreed on 18% of chart reviews). (3) Social determinants of health (SDOH) — the hospital had committed to screening patients for social determinants but progress was slow. The structured SDOH screening tool captured information for only 23% of patients. However, clinicians documented SDOH information in their notes for an estimated 60% of patients — phrases like "patient reports difficulty affording medications," "lives alone, limited social support," and "housing situation unstable, currently staying with friends." This information was invisible to population health analytics and care management programs. (4) Clinical trial recruitment — the hospital participated in 450+ clinical trials.
Trial enrollment was chronically below target: only 34% of trials met their enrollment goals. The primary barrier was identifying eligible patients — trial coordinators relied on: structured EHR queries (which missed patients whose eligibility criteria were documented only in notes), physician referrals (which depended on individual physicians remembering trial criteria during busy clinical encounters), and manual chart review (which was too slow to screen the volume of patients needed).
We built a clinical NLP platform over 16 weeks that processed the hospital's 47 million clinical notes and extracted structured, queryable data for research, quality, and population health. NLP Pipeline Architecture: Document Processing Layer: note type classification (automatically identifying: progress notes, discharge summaries, operative notes, radiology reports, pathology reports, consult notes, and nursing assessments — each requiring different extraction strategies), section segmentation (dividing notes into standard sections — History of Present Illness, Review of Systems, Physical Exam, Assessment, Plan — using both header detection and content-based classification for notes that lacked standard headers), and de-identification (removing patient identifiers from notes before NLP processing — using a combination of rule-based pattern matching for structured identifiers like MRN and date of birth, and NER-based detection for names, addresses, and other quasi-identifiers — achieving 99.7% de-identification accuracy as measured by manual review of 2,000 randomly sampled notes).
Clinical NLP Engine: named entity recognition — a fine-tuned PubMedBERT model that identified: medical conditions (ICD-10-level specificity — not just "diabetes" but "Type 2 diabetes mellitus with diabetic chronic kidney disease, Stage 3a"), medications (drug name, dose, route, frequency, and indication — "Metformin 1000mg PO BID for T2DM"), procedures (CPT-level specificity — surgical procedures, diagnostic tests, therapeutic interventions), lab values (test name, numeric result, units, and reference range interpretation — "HbA1c 7.8% (above target of 7.0%)"), and anatomical locations (body sites, laterality, and specificity — "left anterior descending artery" not just "heart").
Assertion classification — determining the clinical context of each entity: present (the patient currently has this condition), absent (the patient does not have this condition — negation detection: "denies chest pain," "no evidence of malignancy"), historical (the patient had this condition in the past — "history of appendectomy in 2015"), hypothetical (the condition is being considered but not confirmed — "rule out pulmonary embolism"), family history (the condition applies to a family member — "mother diagnosed with breast cancer at age 52"), and conditional (the condition is contingent on something — "if blood pressure remains elevated, will consider starting ACE inhibitor"). This assertion classification was critical — without it, the system would treat "denies diabetes" the same as "has diabetes," producing dangerous inaccuracies.
Relation extraction — connecting entities to each other: medication-condition relations (which medication treats which condition), procedure-condition relations (which procedure addresses which condition), temporal relations (when events occurred relative to each other), and causal relations (what caused what — "pneumonia resulting in respiratory failure"). Social determinants of health extraction — a specialized module that identified: housing (stable housing, homelessness, housing insecurity, shelter use), food (food security, food insecurity, food bank use, nutrition programs), transportation (transportation access, transportation barriers, missed appointments due to transportation), social support (social isolation, caregiver status, family support, community connections), substance use (tobacco, alcohol, cannabis, opioids, other substances — with quantity and frequency when documented), and employment/financial (employment status, financial stress, insurance status, disability status). Each SDOH entity was classified as: current, historical, or at-risk — and linked to the patient's structured record.
Knowledge Base and Query Interface: structured data store (all extracted entities, assertions, relations, and SDOH data stored in a PostgreSQL database with a FHIR-compliant data model — enabling both clinical and research queries), research query interface (a web-based query builder that allowed researchers to: search for patients by extracted clinical characteristics, combine structured EHR data with NLP-extracted data, specify temporal constraints, and estimate cohort sizes — replacing the manual feasibility assessment process), quality metric extraction (automated extraction of quality-relevant data from notes — replacing manual chart abstraction for the 23 metrics that relied on unstructured documentation), and population health dashboard (aggregate SDOH data visualized by: geography, primary care panel, insurance type, and clinical risk category — enabling targeted care management interventions).
The clinical NLP platform processed 47 million notes in the initial batch run (72 hours of processing on a 32-GPU cluster) and then processed new notes daily in near-real-time (average 15,000 notes/day with a 4-hour processing lag). NLP Accuracy: the system achieved, as measured against 5,000 expert-annotated notes: condition extraction: 94.2% F1 (precision 95.1%, recall 93.3%), medication extraction: 96.8% F1 (the most structured entity type — medications follow predictable patterns), assertion classification: 91.7% F1 (the most challenging task — negation and hypothetical detection in clinical language requires understanding context, tone, and clinical reasoning), relation extraction: 88.4% F1 (connecting medications to conditions, procedures to diagnoses), SDOH extraction: 87.3% F1 (the newest and most challenging module — SDOH language is highly variable and context-dependent), and de-identification: 99.7% recall (critical for patient privacy — the system erred on the side of over-redaction rather than under-redaction).
Research Impact: feasibility assessment turnaround: reduced from 2-4 weeks to 2-3 hours for queries involving NLP-extracted data. The research office could now answer 95% of feasibility requests within 1 business day (vs. 40% previously). New research enabled: in the first 6 months, the NLP platform enabled 23 research studies that would have been infeasible without NLP — including: a retrospective study of social determinants and 30-day readmission (using SDOH data extracted from notes for 180,000 patients — a study that would have required 3 years of manual chart review), a multi-condition comorbidity analysis linking undocumented conditions to outcomes (identifying 34,000 patients with conditions mentioned in notes but missing from problem lists — and demonstrating that these undocumented conditions were associated with 18% higher 30-day readmission rates), and a clinical trial recruitment study that identified 2,400 eligible patients for an oncology trial — 3.
2x more than the structured EHR query alone. Quality Measurement: chart abstraction time: reduced by 67% for the 23 NLP-supported quality metrics. The quality team reduced from 12 abstractors to 4 abstractors (with the NLP system doing initial extraction and abstractors performing validation review). Inter-rater reliability (NLP + human validation): improved from 82% to 94% — the NLP system applied consistent extraction logic, and human validators focused on reviewing and correcting rather than extracting from scratch. Annual savings: $680,000 (8 abstractor positions eliminated at $85,000 fully loaded cost each). Social Determinants: SDOH data coverage: increased from 23% (structured screening only) to 58% (structured + NLP-extracted) — more than doubling the hospital's understanding of patients' social needs.
Care management impact: the population health team used SDOH data to identify 4,200 patients with food insecurity and connected them with community food resources — resulting in a 12% reduction in emergency department visits among the connected patients over 6 months. Clinical Trial Recruitment: trial enrollment: increased by 28% across all active trials in the first year — driven by NLP-powered patient identification that found eligible patients missed by structured queries. Time to enrollment: reduced by 34% — coordinators received pre-screened patient lists instead of manually reviewing charts. Financial Impact: development cost: $386,000 (16 weeks). Annual platform cost: $142,000 (GPU compute for daily NLP processing, database hosting, monitoring). Annual savings: $680,000 (quality abstraction) + $280,000 (research feasibility — 12 FTE-weeks of coordinator time saved per year at $23,300/FTE-week) + estimated $1.2 million (clinical trial enrollment increase — trials meeting enrollment generate grant revenue and pharmaceutical company payments).
Total annual value: $2.16 million. First-year ROI: 309% on direct costs, with significant additional value from research studies enabled and patient outcomes improved through SDOH-driven interventions.
Beyond positive/negative — aspect-based sentiment analysis that tells you exactly what customers love or hate about specific features, with domain-specific calibration.
Custom NER models trained on your domain to extract people, organizations, products, dates, amounts, and domain-specific entities from any text.
Multi-label document classification into your custom taxonomy with confidence scores and automated routing based on classification results.
Extractive and abstractive summarization of long documents, meeting transcripts, research papers, and customer conversations — preserving key information.
Identify and extract relationships between entities in text — connecting people to organizations, products to features, or symptoms to diagnoses.
Cross-lingual models that work across 100+ languages, with specialized fine-tuning for your target languages and domains.
Boston's NLP market includes EHR vendors, specialized clinical NLP companies, legal tech firms, and general NLP development agencies. (1) Epic NLP tools — Epic offers built-in NLP capabilities (NoteReader, Cognitive Computing) that provide basic clinical NLP. These tools are convenient because they operate within Epic's data ecosystem, but they offer limited customization, cannot be extended to non-clinical text, and provide extraction accuracy below what custom-trained models achieve for institution-specific documentation patterns. (2) Clinical NLP companies — companies like Linguamatics (acquired by IQVIA), Clinithink, and Amazon Comprehend Medical offer clinical NLP as a service. These products provide good general accuracy but struggle with: institution-specific terminology and abbreviations, custom extraction tasks beyond standard entity types, and the integration with institutional workflows that makes NLP output actionable. (3) Legal tech NLP — companies like Kira Systems, Luminance, and Relativity offer NLP for legal documents.
These are excellent for specific use cases (contract review, eDiscovery) but cannot be extended to the multi-domain NLP needs that institutions with legal, clinical, and financial text require. (4) General NLP agencies — firms that build custom NLP solutions but lack domain expertise in Boston's regulated industries. The model architecture is the easy part — the hard part is understanding clinical reasoning, legal concepts, and financial semantics. ZTABS builds NLP systems for Boston institutions that combine state-of-the-art models with deep domain expertise in the industries that define Boston's economy.
NLP development in Boston ranges by corpus size, domain complexity, and accuracy requirements. NLP Proof of Concept (single document type, standard extraction, 1,000-document evaluation, 4-6 weeks): $60,000-$120,000. Production NLP Pipeline (multiple document types, custom extraction, annotation and fine-tuning, 10-14 weeks): $200,000-$400,000 — the standard engagement for clinical, legal, or financial NLP systems. Enterprise NLP Platform (multi-domain, custom models, continuous learning, regulatory compliance, 14-20 weeks): $350,000-$600,000 — for institutions processing millions of documents across multiple departments. Ongoing operation: $10,000-$25,000/month for daily processing, model monitoring, periodic retraining, and system maintenance. Annotation services (if domain expert annotation is required): $15,000-$40,000 per domain (clinical annotation requires clinicians at higher rates than legal or financial annotation). ZTABS delivers at $200-300/hr — NLP engineers with healthcare, legal, and financial domain expertise who build production-grade extraction systems for regulated environments.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract. We ship and operate our own software. 23 products in production.
Common questions about NLP & text analytics for Boston businesses
Modern clinical NLP achieves 90-97% accuracy on most extraction tasks — comparable to trained human abstractors and in some cases better (because the model applies consistent logic while humans are subject to fatigue and attention lapses). However, clinical NLP is not "reading" notes the way a clinician does — it is pattern-matching at enormous scale. The system excels at: identifying entities (medications, conditions, procedures), classifying assertions (present, absent, historical), and extracting relations (which medication treats which condition). It is less reliable for: complex clinical reasoning ("the patient's symptoms are most consistent with either lupus or sarcoidosis"), subtle nuance ("patient seems to be improving" vs. "patient claims to be improving"), and novel situations (unusual conditions, rare medications, or atypical documentation styles). This is why we deploy NLP with human-in-the-loop validation for high-stakes applications.
ZTABS is a remote-first NLP & text analytics agency serving Houston businesses — including sentiment analysis, named entity recognition, document classification. We work with Energy & Oil/Gas, Healthcare & Biotech, Aerospace & Defense companies in Houston, TX via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
NLP & Text Analytics in New York, NYZTABS is a remote-first NLP & text analytics agency serving New York businesses — including sentiment analysis, named entity recognition, document classification. We work with Finance & Fintech, Media & Advertising, Fashion & Retail companies in New York, NY via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
NLP & Text Analytics in Los Angeles, CAZTABS is a remote-first NLP & text analytics agency serving Los Angeles businesses — including sentiment analysis, named entity recognition, document classification. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Development in Boston, MAZTABS is a remote-first web development agency serving Boston businesses — including full-stack development, progressive web apps, api development. We work with Biotech & Pharma, EdTech, Fintech & Insurance companies in Boston, MA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Design in Boston, MAZTABS is a remote-first web design agency serving Boston businesses — including ui/ux design, responsive design, custom interfaces. We work with Biotech & Pharma, EdTech, Fintech & Insurance companies in Boston, MA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Development in Boston, MAZTABS is a remote-first AI development agency serving Boston businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with Biotech & Pharma, EdTech, Fintech & Insurance companies in Boston, MA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
NLP & Text AnalyticsLearn more about our NLP & text analytics services nationwide.
Partner with ZTABS for expert NLP & text analytics in Boston. Get a free consultation today.
