AI & Data
Language AI for the text your business already writes
Tickets, contracts, clinical notes, reviews. Most of it is read once and filed. We build NLP pipelines that extract, classify and route it, tuned on your vocabulary, scored per class and per language, and wired into the systems that act on the result.
What We Build
Text in. Structured decisions out.
Six services. Each starts from a task your text already supports and a metric your team signs off.
Sentiment Analysis & Opinion Mining
Sentiment tied to the product, feature or person it's about, across tickets, reviews, chats and news. "Negative" on its own tells nobody what to fix.
Named Entity Recognition & Information Extraction
Parties, dates, amounts, clauses, drug names and part numbers pulled into fields, using models tuned on your own vocabulary and abbreviations.
Custom Chatbot & Conversational AI
Assistants that keep context across a conversation, answer from your knowledge base, and hand over to a person rather than guess.
Automated Document Processing & Classification
Invoices, contracts, medical records and filings read, classified and routed, including scanned pages where OCR noise is half the problem.
Intent Classification & Ticket Routing
Every email, chat and ticket tagged by intent and sent to the team that can resolve it. Uncertain cases are flagged for a person.
Multilingual NLP & Cross-Language Intelligence
Classification and extraction across languages, reported language by language, because performance rarely holds evenly outside the major ones.
How It Runs
Six phases, from raw text to a running pipeline
Each phase is named for the artefact it produces, so you can see exactly where the project stands.
Use cases and the cost of a wrong label
We look at the text you already hold - tickets, contracts, notes, reviews - and the decisions it could feed. Each candidate gets a value, a risk and a measure of success.
Some candidates stop here, because a keyword rule already does the job. That is a good outcome.
DELIVERABLES
- Ranked use-case list
- Inventory of text sources and their owners
- Metric per task, agreed in writing
- Rules-versus-model decision per use case
- Privacy and data-handling constraints
- Phased plan for the chosen use case
How It Works
Inside an enterprise NLP pipeline
Four stages. The choices made in each one set the ceiling for the next.
Ingestion & Cleanup
Text arrives from email, PDFs, scans, chat logs and databases. Language detection, OCR cleanup, de-duplication and redaction of personal data happen before any model sees it. Messy input is the most common reason accuracy disappoints.
Approach Selection
Rules, classic classifiers, encoder models such as BERT, or a tuned LLM. The cheapest approach that clears the agreed metric wins, because it will also be the cheapest to run at volume.
Task Pipelines
Extraction, classification, summarisation and translation each need their own metric and latency budget. They are built as separate containerised services that combine into larger workflows.
Monitoring & Retraining
Drift detection on inputs and outputs, corrections flowing back from the review queue, and a retraining pipeline that promotes a new model only when it beats the current one on the same test set.
Domain-Tuned NLP vs Generic API
Where the difference shows up in practice
Broad
Trained for general text, not your tasks
- Text processed on third-party servers
- Weak on in-house terms and abbreviations
- Opaque when it gets something wrong
- Doesn't learn from your corrections
Tuned
Trained and tested on your own tasks
- Can run inside your own infrastructure
- Tuned on your vocabulary and formats
- Per-class results you can inspect
- Retrained from reviewer corrections
NLP Stack
From Rules to LLMs
From regular expressions to fine-tuned LLMs. The task, the data and the cost of running it at volume decide which layer does the work.
Foundation Models
Pipeline & MLOps
NLP Frameworks
Cloud & Deployment
Fine-Tuning & Training
Chosen per task. The benchmark picks the approach, not a partnership.
Where It Breaks
Three places NLP projects go wrong.
None of them is the model. All of them surface after launch unless someone looks first.
Labels Nobody Agrees On
If two experts would tag the same ticket differently, no model will do better than they do. Disagreement is measured before training, not discovered after it.
A Good Average Hiding a Bad Segment
A strong overall score can hide failure on one language, one product line or one rare class. That is why results are broken out by segment.
A Model Where a Rule Would Do
Some extraction is a pattern match. Training a model for it adds cost, latency and drift risk, and buys nothing.
Why Us
How we build language systems
Four habits that decide whether a text model survives contact with real documents.
01
Cheapest Approach First
Rules and small models are tested before large ones. You pay for model capacity only where the benchmark shows it's needed.
02
Results by Class and Language
Every report breaks performance down by category and by language, so weak spots are visible before your users find them.
03
Review Built Into the Flow
Uncertain outputs go to people, and their corrections become training data. The system improves from its own mistakes.
04
Data Stays Where It Must
Pipelines can run inside your cloud or on your own hardware, with personal data redacted before anything leaves the perimeter.
Industry Applications
NLP for industries that run on documents
Contracts, claims, clinical notes, filings. Select a card to see where language AI fits each sector.
Clinical and operational systems for healthcare
Clinical documentation, claims and connected patient data, with a clinician approving anything that affects care.
Banking, insurance and risk systems
Fraud, KYC, underwriting and claims models that risk and compliance teams can review, explain and sign off.
Engineering for automotive
Connected-vehicle data, telematics and dealer analytics, built around functional-safety and cybersecurity requirements.
Plant-floor systems for manufacturing
Predictive maintenance, vision inspection and forecasting that connect plant data to the business.
Data and AI for agriculture
Crop, sensor and imagery analytics designed for patchy connectivity and one growing season a year.
Retail operations and customer data
Demand forecasting, personalisation and unified customer data, tuned to the trading calendar.
FAQs
Common Questions
Use-case selection, labelling design, model selection and tuning, integration with your systems, validation, and monitoring after launch. The aim is to turn unstructured text into fields, labels and routes your systems can act on.
Generic APIs are trained on general text and process yours on someone else's servers. A custom pipeline is tuned on your vocabulary, scored on your own test set, and can run inside your infrastructure. Where a generic API already clears your metric, use it.
High-volume, repetitive reading with a clear right answer: ticket routing, document classification, field extraction from forms and contracts. Open-ended analysis comes later, once the basics are measured.
It depends on whether labelled data exists, how many tasks and languages are involved, and how deep the integration goes. Discovery produces a phased plan stating what each phase delivers and what it needs from your side.
Some, and it's worth planning for. Your experts write and test the annotation guidelines with us, then review a sample of labels. The labelling-design phase estimates the remaining hours from how often annotators agree.
Yes. Encoder models and open-weight LLMs can run on your own servers or in a private cloud, so sensitive text never has to reach an outside API. For health, finance and legal teams, that is often the deciding factor.
NLP covers all work with language: classifying, extracting, summarising, translating. Generative AI is the part that writes new text. Most enterprise systems use both - NLP to understand and route, generative models where a written answer is needed.
Whichever your data contains, measured one at a time. Major languages are usually well served by existing models; less-resourced ones often need more labelled data. We report results per language rather than quote a single count.
Pick one decision.
Measure before you build.
Tell us which decision a model should support and what a wrong answer costs. The first conversation covers your data and how the result will be judged. If an off-the-shelf model already passes, that is the answer you get.
Have a project in mind?
Bring the problem. The first call is about specifics, not slides.