Make Sense of All Your Text, at a Scale Humans Cannot

Your business generates and receives oceans of text, emails, reviews, tickets, contracts, documents, and it holds meaning and facts no human team has time to read through. Natural language processing reads it all, understands it, and pulls out what matters. We build text analysis, sentiment analysis, document processing and information extraction.

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100+Products in production
9+Years building
25+Senior engineers
98%Client retention
apptosmart
baba
banoun
basafr
docscanner
famooshed
idcu
joinbeet
kora
lionstudio-ai
lokal
ludusio
maamar
mcokie
me-by-mind
mudeer
mukaab
my-meeting-card
paaet
pixi
playground
prezence
raceboard
recu
riciclario
schutzwinkel
shishr
spendulge
trainaero
trendzfans
zatara
apptosmart
baba
banoun
basafr
docscanner
famooshed
idcu
joinbeet
kora
lionstudio-ai
lokal
ludusio
maamar
mcokie
me-by-mind
mudeer
mukaab
my-meeting-card
paaet
pixi
playground
prezence
raceboard
recu
riciclario
schutzwinkel
shishr
spendulge
trainaero
trendzfans
zatara

The Text Your Business Cannot Read Fast Enough

Engineering discipline over hype — that is the thread through every kind of AI development we do.

Every business generates more text than it can realistically process. Customer emails, reviews, support tickets, contracts, invoices and reports contain valuable insights, but manually reading and analysing everything is slow, expensive and often impossible at scale.

Natural Language Processing (NLP) helps businesses understand and organise text automatically, and forms part of our wider AI data & intelligence practice. It can analyse customer feedback, process documents, extract key information and identify patterns across thousands of records in seconds. Instead of drowning in unstructured data, businesses gain clear, actionable insights.

Modern NLP goes far beyond simple keyword matching. It understands context, intent and meaning, making it possible to analyse conversations, identify sentiment, process documents and uncover trends with far greater accuracy. Whether your content is in English, Dutch or multiple languages, NLP transforms text into information your business can actually use, the same language strengths behind our Careslate medical translation app.

This page explores four key NLP applications: Text Analysis, Sentiment Analysis, Document Processing and Information Extraction.

The NLP Work We Build

Every business generates valuable information hidden inside text. Our NLP solutions uncover patterns, analyse sentiment, process documents and extract critical data automatically, transforming unstructured content into intelligence your team can act on with confidence.

Text Analysis

Text analysis reads through large volumes of text and surfaces the patterns, themes and meaning inside it, what topics come up most, how they relate, what is changing over time. Instead of someone skimming a fraction of your reviews or tickets and forming an impression, you get an analysis of all of it, grounded in the full picture rather than a hunch from a sample.

We build text analysis tuned to your specific text and questions, whether that is understanding what customers write about, categorising incoming messages, or tracking how themes shift across thousands of documents. The value is in seeing the whole rather than a sample: the complaint that seemed rare turns out to be common, the emerging issue gets spotted while it is still small, the real themes in customer feedback become visible instead of guessed at. For any business with more text than it can read, text analysis turns that text from an unread pile into genuine understanding.

Text Analysis
  • Reads all your text, not a skimmed sample
  • Surfaces themes, topics and patterns across large volumes
  • Tuned to your specific text and questions
  • Spots emerging issues while they are still small

Sentiment Analysis

Sentiment analysis reads the emotion and attitude behind text, whether a customer is happy, frustrated, or angry, and how that is trending across all your feedback. It turns the vague sense of how customers feel into something you can actually measure and track, across reviews, support messages, surveys and social mentions, at a scale no team could read through by hand.

We build sentiment analysis that understands nuance rather than just counting positive and negative words, modern models grasp sarcasm, mixed feelings and context that simple tools miss entirely. That means you can track satisfaction over time, catch a rising wave of frustration before it becomes a crisis, see exactly which products or issues drive negative sentiment, and measure whether a change actually improved how customers feel. For businesses that care about customer experience but only had anecdotes to go on, sentiment analysis turns feeling into evidence you can act on.

Sentiment Analysis
  • Measures the emotion behind text, not just keywords
  • Understands nuance: sarcasm, mixed feelings, context
  • Tracks satisfaction trends and catches rising frustration early
  • Turns vague impressions into evidence you can act on

Document Processing

Document processing handles the paperwork that eats up staff time, reading documents, understanding them, and pulling out or routing what matters, automatically. Invoices, forms, applications, contracts, reports, the documents that someone currently opens, reads, and re-keys by hand can instead be processed by AI in a fraction of the time, consistently and without the errors that creep in when people do repetitive data entry.

We build document processing tuned to your actual documents and what you need from them, whether that is extracting the figures from invoices, pulling key terms from contracts, sorting incoming documents, or turning forms into structured data in your systems. Modern NLP handles the variation in real documents, the different layouts, wordings and formats, far better than the rigid template-based tools of the past. For businesses that move a lot of paperwork, this removes a slow, tedious, error-prone bottleneck and frees staff from data entry for work that needs human judgement.

Document Processing
  • Reads and processes documents automatically, at speed
  • Handles real-world variation in layout and wording
  • Extracts and routes what matters, into your systems
  • Removes slow, error-prone manual data entry

Information Extraction

Information extraction pulls specific facts out of unstructured text and turns them into structured data you can use, names, dates, amounts, terms, entities, whatever matters to you, lifted from free-flowing text into clean fields. It is the bridge between text that humans write and data that systems can work with, and it unlocks information that was effectively trapped in prose.

We build extraction tuned to the specific facts you need from your specific text, whether that is pulling key terms and dates from contracts, extracting entities from reports, or turning unstructured records into structured database entries. This is what lets the rest of your data systems work with information that previously sat locked inside documents and messages. Once the facts are extracted into structure, they can be searched, analysed, reported on and fed into other systems, so text that was a dead end becomes live, usable data. For businesses sitting on valuable information trapped in unstructured form, extraction is what sets it free.

Information Extraction
  • Pulls specific facts from unstructured text into clean data
  • Tuned to the exact facts you need from your text
  • Turns prose into searchable, analysable, structured data
  • Frees information that was trapped in documents and messages

Apps We Took From Idea to Launch

A few products that show what one accountable team can ship, built to be genuinely used.

Baba — Hebrew Translation App

Baba — Hebrew Translation App

Gender-aware AI Hebrew translation app with six gender modes, dual-model fallback (OpenAI + Gemini) and sub-second results. Cross-device history and favourites sync in real time across iOS and Android.

Careslate — Medical Translation App

Careslate — Medical Translation App

HIPAA-compliant real-time medical translation app for healthcare professionals. Three-stage audio pipeline cleans, transcribes and validates translations across 10+ languages in under three seconds — no account, no stored recordings.

ReelEats — Food Discovery App

ReelEats — Food Discovery App

Turn food reels into real restaurant visits. Share any reel, screenshot or map link and AI extracts the spot using OCR and Google Places enrichment. Saved spots organise into smart collections you can share or visit on the map.

TrainAERO — AI Fitness Coaching App

TrainAERO — AI Fitness Coaching App

AI-powered fitness coaching app that personalises every workout based on performance data, injury history and recovery signals. Built with Flutter for iOS and Android with adaptive programming, breathwork sessions and multi-mode training timers.

Browse all case studies

The Technology Behind Our NLP

NLP work draws on both specialised language tools and leading language models, chosen to fit the task, precise extraction needs different tools than broad understanding. We are transparent about the stack so nothing is a black box.

For understanding, nuance and flexible tasks, we use leading large language models, GPT from OpenAI, Claude from Anthropic, Gemini from Google, and strong open-source models where data must stay in-house.

For precise, structured tasks like entity extraction and classification we use established NLP libraries and frameworks, often alongside language models for the best of both.

For document processing we add OCR and document-parsing tools that turn scans and PDFs into text the models can work with, handling real-world document formats.

The work runs on Python, the standard for NLP, on cloud infrastructure sized to the volume of text involved.

OpenAI GPT
Anthropic Claude
Google Gemini
Open-source LLMs

NLP That Works in Dutch, Not Just English

Language matters. Our NLP solutions are designed and tested for Dutch, English and multilingual business environments, ensuring accurate results where generic language models often fall short.

01

Where Dutch NLP quietly falls down

This deserves its own section because it is where a lot of NLP quietly falls down for Netherlands businesses. Much NLP tooling is built and tuned primarily for English, and performance can drop noticeably on Dutch text, the sentiment is misread, the extraction misses things, the analysis is subtly off. If your reviews, documents, tickets and contracts are in Dutch, English-tuned NLP gives you a distorted picture.

02

How we handle Dutch properly

We build NLP that handles Dutch properly, as well as English and other languages your business works in. That means choosing and configuring models that perform well on Dutch, testing on your actual Dutch text rather than assuming it works, and handling the mix of languages that is normal for businesses operating in the Netherlands and across the EU. The result is analysis and extraction you can actually trust on your real text, whatever language it is in.

03

Why accuracy on your real text matters

This matters more than it might seem. An NLP system that works beautifully in demos on English text and then quietly underperforms on your Dutch reviews is worse than useless, because it gives confident, wrong results. Building and testing for the languages you actually operate in is part of doing this work honestly, and our base in the Netherlands means working in Dutch is a normal expectation for us, not an afterthought.

NLP Across Different Businesses

From customer feedback and support tickets to contracts and financial documents, NLP creates value wherever businesses need to understand, process and act on large volumes of text efficiently.

Retail and ecommerce

Reading and analysing every product review and customer message to understand what people actually love and complain about, and tracking how sentiment shifts after changes, across far more feedback than a team could read.

Legal and professional services

Extracting key terms, dates and obligations from large volumes of contracts and documents, turning slow manual review into fast, consistent extraction with humans focused on judgement rather than reading.

Finance and insurance

Processing forms, applications and documents automatically, extracting the structured data needed, and flagging anything unusual for human review, removing a heavy manual bottleneck.

Any support-heavy business

Analysing support tickets to find the real drivers of contact, categorising incoming messages automatically, and tracking customer sentiment across the whole support queue rather than a sampled impression.

These are illustrations, not limits. If your business generates or receives more text than your people can read and act on, NLP almost certainly has something to offer. We find the highest-value starting point with you during discovery.

What NLP Delivers

NLP transforms large volumes of unstructured text into actionable insights, helping businesses save time, improve accuracy and uncover information that would otherwise remain hidden.

What NLP delivers for your business

Key Benefits

05
  • Every document, email and message analysed, not just a small sample
  • Manual reading, sorting and data entry dramatically reduced
  • Trends, risks and opportunities identified earlier
  • Customer sentiment transformed into measurable insights
  • Key information extracted into structured, usable data
  • Consistent analysis at scale, without fatigue or human error

Build Intelligent Solutions With the Right Stack

From AI architecture to cloud deployment — design, engineering and infrastructure handled by one team. No coordination overhead, no gaps in quality.

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9+
Years delivering production software
250+
Clients across industries & geographies
100+
Products live in production at scale
99.9%
Uptime across deployed systems

What This Looks Like in Practice

NLP is easiest to understand when viewed through a real business problem. This example shows how language AI transforms overwhelming volumes of text into clear insights, faster decisions and measurable outcomes.

The scenario

Picture a business that receives thousands of customer reviews and support messages a month across several channels and two languages, Dutch and English. The team reads what it can, replies to the urgent ones, and forms a general sense of how customers feel, but nobody can actually read it all, and the general sense is really an impression built from whatever happened to cross people's desks.

The approach

With NLP in place, every message and review is read and analysed. Sentiment is tracked over time across both languages, so a rising wave of frustration about a specific issue is caught while it is still small. Text analysis surfaces the real themes, revealing that a complaint everyone thought was rare is actually common. Incoming messages are categorised automatically and routed to the right team. The Dutch text is handled as accurately as the English, because the system was built and tested for both.

The result

The business finally knows, with evidence drawn from all of its feedback rather than a sampled hunch, what its customers actually think and why. Action follows: the overlooked issue gets fixed, the team stops sampling and starts seeing the whole, and the effect of every change becomes measurable in the sentiment trend. That is the shape of NLP done well, it does not just process text faster, it lets a business understand its own text completely, often for the first time.

What Our Clients Say About Us

Real feedback from real clients. Here is what businesses say about working with Mobilions on their mobile and web products.

Alexander
Alexander
Netherlands

It was a wonderful experience working with Tushar, Ankit, and their team. They built a great mobile app for me and truly brought my vision to life. What stood out was not just their technical skill but their attitude: always positive, solution-oriented, and incredibly patient. They went above and beyond at every step, finding creative workarounds and staying committed even when things got challenging. Extremely professional and trustworthy. I would absolutely hire them again.

Frequently Asked Questions

Find answers to the most common questions about Natural Language Processing, document automation, sentiment analysis, information extraction and how NLP helps businesses unlock value from large volumes of text.

NLP, natural language processing, is AI that reads and understands human language, the way people actually write, rather than rigid keywords. It can read text at a scale no human team could manage, understand what it means including nuance and context, and pull out patterns, emotions or specific facts. In short, it turns the oceans of text a business generates into usable, structured information.

Yes, and we treat it as a first-class requirement, not an afterthought. A lot of NLP tooling is tuned for English and underperforms on Dutch, so we choose and configure models that handle Dutch well and test on your actual Dutch text. Given our base in the Netherlands, working properly in Dutch and across the mix of languages EU businesses use is a normal expectation for us.

Older tools largely matched keywords and missed meaning, they could not tell sarcasm from sincerity or understand context. Modern NLP, built on the same advances as large language models, understands nuance, context and intent far better. It grasps that two differently worded complaints are the same issue, or that a review is sarcastic, which makes the results far more accurate and useful than the keyword tools of a few years ago.

Yes, far better than the old template-based tools. Real documents vary in layout, wording and format, and modern NLP and document processing handle that variation rather than breaking the moment something does not fit a rigid template. We tune the processing to your actual documents and test on real examples, including the messy ones, so it works on what you really receive.

Yes. NLP often processes text containing personal or sensitive data, so we build to GDPR and AVG, keep data in EU regions where required, and are careful about which text flows to which model and how it is stored. For sensitive data we can use models that keep everything in-house. Our European base in Amstelveen means EU data protection is built into how we approach the work.

It is strong and getting stronger, but not perfect, and we are honest about that. For many tasks it is accurate enough to rely on directly; for higher-stakes ones we build it to flag uncertain cases for human review rather than acting alone. We test on your real text and show you the accuracy, so you know where it can run autonomously and where a human should stay in the loop.

A focused task, such as sentiment analysis on your reviews or extraction from a specific document type, can be delivered in a few weeks. Broader projects spanning multiple document types, languages or deep integration take longer. We usually start with one well-defined, high-value task, prove it on your real text, and expand from there once it has shown its worth.

Language, documents and the way customers write all shift over time, so we monitor performance and refine the system as part of ongoing support. New document formats appear, new topics emerge in feedback. We keep the system accurate against your real, changing text rather than letting it quietly drift, so it stays trustworthy over time instead of degrading after launch.