Natural Language Processing
Turn thousands of documents and calls into structured decisions.
Custom text and speech models for classification, extraction, and understanding — tuned to your industry's language, not generic sentiment scores.
- Legal, medical, financial ready
- Domain-tuned
- Call & voice analytics
- Speech-to-text
- Feeds directly into your systems
- Structured output
The problem
Unstructured text and calls hide decisions you're not making
Contracts, tickets, reviews, and call recordings contain patterns and risks no one has time to read manually — so they go unexamined until something goes wrong.
- Manual document review doesn't scale past a handful of contracts a day
- Generic sentiment tools miss domain-specific language and nuance
- Call center insight is trapped in recordings no one has time to review
- Compliance risk hides in documents that were never systematically checked
The solution
Language models tuned to how your industry actually talks
We build and fine-tune NLP models on your domain's real language — contracts, tickets, or calls — so extraction and classification hold up on the edge cases that matter.
Domain-tuned accuracy
Models adapted to your industry's specific vocabulary and structure.
Scales past manual review
Thousands of documents or calls processed consistently.
Feeds your systems
Structured output integrates directly into CRMs and warehouses.
Capabilities
What we build
From document review automation to call center analytics.
Text classification & extraction
Custom models pulling structured fields from unstructured text.
Document summarization
Long documents condensed into structured, reviewable summaries.
Sentiment & intent analysis
Voice-of-customer signal extracted from support and feedback data.
Speech-to-text pipelines
Call transcription feeding downstream analytics.
Domain fine-tuning
Models adapted to legal, medical, or financial language.
System integration
Structured output delivered into CRMs, helpdesks, and warehouses.
Technology
Technology we use
Modern NLP tooling matched to structured business outcomes.
Modeling
- spaCy
- Hugging Face
- PyTorch
Generative & speech
- OpenAI
- Whisper
Search & storage
- Elasticsearch
Architecture
How an NLP pipeline is structured
From raw text or audio to a structured, usable record.
01
Ingestion
Text or audio pulled from documents, tickets, or call recordings.
02
Transcription
Speech-to-text conversion when the source is audio.
03
Extraction
Domain-tuned models classify and extract structured fields.
04
Delivery
Structured results pushed into CRMs, warehouses, or dashboards.
Use cases
Where we've applied this
Legal
Contract review automation
Key terms and risk clauses extracted at scale.
SaaS
Support ticket analysis
Trend and root-cause analysis across thousands of tickets.
Contact Centers
Call center analytics
Transcription and quality analytics across recorded calls.
Financial Services
Compliance monitoring
Automated flagging of regulatory language in documents.
Process
How we deliver NLP systems
- 01
Understand the domain
Study the specific language, structure, and edge cases involved.
- 02
Build & fine-tune
Adapt models to your terminology and desired structured output.
- 03
Validate
Test extraction accuracy against real documents or calls.
- 04
Integrate & scale
Wire structured output into downstream systems.
Benefits
What structured language data buys you
Scale past manual review
Thousands of documents processed consistently, not sampled.
Earlier risk detection
Compliance and contract risks surfaced automatically.
Actionable customer insight
Support and feedback trends visible at a glance.
Faster operations
Structured data flows directly into the systems that need it.
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ExploreFAQ
Frequently asked questions
NLP work here is about structured extraction and analysis at scale — turning thousands of documents or calls into consistent structured data, not just answering one question at a time.
Have thousands of documents or calls no one has time to read?
Tell us what's buried in that text or audio — we'll show you what structured extraction could surface.