Book a Meeting↗
NLP Development

Language AI tuned for your domain,
not the internet.

Generic LLMs perform poorly on specialized text. We fine-tune and build custom NLP systems on your data - legal, clinical, financial, or operational - so accuracy is measured against your domain, not a public benchmark.

97%
avg. F1 on production systems
6 wks
to production NLP pipeline
10+
domain verticals shipped
Discuss your NLP project

NLP tasks we handle

Text Classification

Route support tickets, tag documents, detect intent. Single-label or multi-label, fine-tuned to your taxonomy.

Support triage at 94% accuracy
Named Entity Recognition

Extract people, orgs, dates, amounts, and custom entities from unstructured text at scale.

Extracting parties & clauses from contracts
Text Summarization

Abstractive and extractive summarization for long documents - earnings calls, legal briefs, clinical notes.

500-page contracts in under 2 minutes
Sentiment Analysis

Beyond positive/negative - aspect-level sentiment with domain-aware fine-tuning for your content type.

Product review aspect analysis
Semantic Search

Vector-based retrieval that understands meaning, not just keywords. Beats BM25 on domain-specific corpora.

Internal knowledge base search
Document Extraction

Structured data out of unstructured documents - invoices, forms, tables, PDFs. Schema-defined output.

Invoice line-item extraction at 97% F1

Pre-trained vs. fine-tuned

Not every problem needs a custom model. Here's how we decide.

Pre-trained model (zero-shot)
Use when
  • →General text, broad categories
  • →Low data volume (<500 examples)
  • →Rapid prototyping / POC phase
  • →Non-critical applications
Avoid if

Specialized terminology, regulatory accuracy requirements, domain-specific entity types.

Fine-tuned custom model
Use when
  • →Domain-specific vocabulary (legal, clinical)
  • →High accuracy requirements (>95% F1)
  • →Structured output from complex documents
  • →Private data that can't leave your infra
Avoid if

Simple classification tasks with abundant generic training data.

Domain adaptation process

How we teach a model to speak your language - legal, clinical, or financial.

01
Data collection

We audit your existing corpus - contracts, records, tickets - and identify gaps. We source supplementary domain data if needed, always respecting data governance requirements.

Corpus auditData governanceGap analysis
02
Annotation & labeling

Domain experts - not generalist annotators - label your data with inter-annotator agreement tracking. We build task-specific guidelines calibrated to your edge cases.

IAA trackingExpert annotatorsLabel guidelines
03
Fine-tuning cycle

Iterative training with active learning - the model tells us which examples to label next. We run A/B tests comparing each checkpoint against the baseline to measure real improvement.

Active learningA/B checkpointsPEFT/LoRA

Evaluation methodology

01
Precision, Recall & F1

Class-level breakdown, not just aggregate accuracy. We never hide performance on minority classes.

02
Human Evaluation

Blind A/B tests with domain experts comparing model output to ground truth on 200+ samples.

03
Domain Expert Review

Subject matter experts review edge cases - not just annotators. Especially critical for medical and legal applications.

04
Benchmark vs. Baseline

We compare against the best available baseline - GPT-4, Claude, or domain-specific SOTA - so you know what you're actually getting.

05
Latency & Throughput

NLP quality is useless at 30s/doc. Every evaluation includes latency percentiles (p50, p95, p99) and batch throughput.

Use cases by industry

Healthcare
Clinical NLP

Extract diagnoses, medications, and procedures from clinical notes. HIPAA-compliant pipelines. Models trained on MIMIC and de-identified EHRs.

Legal
Contract Intelligence

Clause extraction, obligation mapping, and risk flagging across MSAs, NDAs, and SOWs. Fine-tuned on curated legal corpora.

Finance
Earnings Analysis

Sentiment and topic modeling on earnings calls, SEC filings, and analyst reports. Real-time signal extraction for trading and research.

E-commerce
Product Content AI

Automated description generation, attribute extraction, and catalog enrichment at millions of SKUs. Fine-tuned on your product taxonomy.

Case Study - Legal Tech

Legal NLP system that processes 500-page contracts in under 2 minutes.

Fine-tuned BERT variant on 50,000 annotated contract clauses. Extracts parties, obligations, termination rights, and risk flags. Reviewed by practicing attorneys before deployment.

<2 min
Per 500-page contract
96.4%
Clause extraction F1
80%
Reduction in review time
How it works

From raw text to production NLP

01
Data audit
Review your existing text corpus, identify annotation needs, and set accuracy targets.
→
02
Annotation
Domain experts label examples with task-specific guidelines and IAA tracking.
→
03
Fine-tuning
Iterative training with active learning and A/B checkpoint comparisons.
→
04
Evaluation
F1, precision/recall, human eval, and latency benchmarks against SOTA baseline.
→
05
Deployment
Containerised inference endpoint with monitoring, versioning, and rollback.
Clinical NLP result
"Our NLP pipeline reads a patient chart in 4 seconds and surfaces every relevant diagnosis, medication, and risk flag - work that took a coder 25 minutes."
4s
Per chart processed
94.8%
Entity extraction F1
25 min
Manual time replaced
HIPAA
Compliant deployment
Architecture decision

Which model architecture fits your task?

ArchitectureBest forLatencyData needed
BERT / RoBERTa (fine-tuned)Classification, NER, extractive QA~20–80ms500–10k labelled examples
GPT-4 / Claude (zero-shot)General tasks, summarization, drafting~500ms–3sNone (prompt only)
Domain-specific LLM (fine-tuned)High-accuracy domain NLP at scale~100–400ms5k–50k examples
Encoder + rule engine hybridRegulated environments, auditable output~10–50ms500+ labelled + rules
Bi-encoder (semantic search)Retrieval, similarity, semantic matching~5–30ms (indexed)Query–document pairs
Common questions

NLP FAQ

Can you work with sensitive or regulated data?
Yes. We work with HIPAA-regulated clinical data, GDPR-scoped European datasets, and financial data under SOC 2 requirements. We sign BAAs where required and can work in air-gapped or private cloud environments.
How much labelled data do we need to start?
Less than you think. With modern transfer learning and active learning, 300–500 high-quality annotated examples can yield a strong production model for most classification tasks. We'll assess your data volume before committing to an architecture.
How long does a custom NLP project take?
A typical production NLP pipeline - including data annotation, fine-tuning, evaluation, and deployment - takes 6–10 weeks. Complex multi-task systems or custom model architectures may take 12–16 weeks.
Do you hand off the model or maintain it?
Either. We can hand off a fully documented, containerised model with retraining pipelines and evaluation harnesses for your team to own. Or we can manage ongoing monitoring, retraining, and model updates under a retainer.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in TouchSee Our Work

What happens next

1

Send us a message

Tell us what you're building or what's broken.

2

Discovery call (30 min)

We ask hard questions. You get honest answers.

3

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Contact Us

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

  • Agentic AI development and multi-agent systems
  • Generative AI consulting and LLM integration
  • RAG development and custom model deployment
  • Data engineering, MLOps and custom software
[email protected]

We respond within one business day. Your data is handled in accordance with our privacy policy.