Invoice & receipt extraction
Pull line items, totals, dates, vendors, and tax codes from scanned or digital documents — straight into your ERP or accounting tool.
AI LAB · 05
Turning paper into structured data
NLP and document AI that turns chaos into clean rows.
Contracts, invoices, support tickets, clinical notes, transcripts — anything textual becomes searchable, classifiable, and actionable. We combine modern OCR, transformer models, and LLM-based extraction with the boring engineering it takes to make accuracy survive contact with reality.
What we build
Software that reads.
Six NLP and document AI capabilities we ship — turning unstructured text, scans, and PDFs into structured data your systems can act on.
Contract analysis
Surface key clauses, dates, parties, obligations, and renewal terms across thousands of agreements. Search across them in plain English.
Semantic search
Embedding-based search over your docs, tickets, transcripts, and code. Users find what they meant, not what they typed.
Summarisation
Long-form summarisation with controllable length and tone. Meeting notes, research papers, customer-call transcripts, policy docs.
Sentiment & intent
Classify ticket sentiment, detect intent in chat, score brand mentions, and flag escalation risk in real time.
Multilingual NLP
Translation, language detection, and cross-lingual search across 100+ languages — useful for global support and content workflows.
Use cases
Where text becomes signal.
Three deployments where unstructured docs stopped being a bottleneck.
Contract intake automation
An NLP pipeline reads incoming contracts, extracts 40+ structured fields, flags non-standard clauses, and pushes everything into the firm's matter management system. Lawyers review exceptions, not data entry.
8hsaved per contract
98%extraction accuracy
Ticket classification & routing
Inbound tickets are classified by topic, priority, and sentiment in real time. Urgent issues skip the queue; routine ones get auto-responses with the right help-doc link.
−54%time to first reply
+19CSAT score
Clinical note summarisation
Doctors dictate visit notes; a fine-tuned model produces structured summaries for the EMR — chief complaint, plan, follow-up. Doctors review and sign, instead of typing.
12 minsaved per visit
95%summary acceptance
The stack we use
OCR meets LLMs meets engineering.
The best document AI pipelines combine classical OCR, modern transformers, and LLM-based extraction — picking each tool where it earns its place.
OCR & doc extraction
- Tesseract, EasyOCR
- AWS Textract
- Azure Form Recognizer
- Docling, Unstructured
NLP models
- Hugging Face Transformers
- spaCy
- Sentence Transformers
- Fine-tuned BERT, RoBERTa
LLM-based extraction
- GPT-4o structured output
- Claude function calling
- Instructor library
- Outlines, Guidance
Search & retrieval
- Elasticsearch / OpenSearch
- Pinecone, Weaviate
- pgvector
- Hybrid BM25 + dense
How we work
Six steps to reliable extraction.
We always start with the question: 'what counts as good enough?'. Then we work backwards from there.
01
Schema design
Define exactly what you want extracted, how it should be structured, and what counts as confident-enough.
02
Sample & label
Curate a representative sample, label a clean ground truth, design the eval harness.
03
Baseline
Often an LLM with structured output is a great starting baseline — ship it, measure it, then decide if a custom model is worth the effort.
04
Iterate
Fine-tune, add domain prompts, layer in OCR and pre-processing where they help.
05
Confidence & review
Add confidence scores and a human-review queue for low-confidence extractions. Accuracy without humility is dangerous.
06
Productionise
Build the ingestion pipeline, the extraction service, and the dashboards that show you how the system is actually performing.
Frequently asked
NLP and document AI questions.
What is document AI?
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Document AI is the use of machine learning to extract structured data from unstructured documents — PDFs, scans, emails, contracts, invoices, forms. At Appsmediaz, we build document AI pipelines that combine OCR, layout understanding, and LLM-based extraction to turn paper and PDFs into clean database rows.
Can NLP read handwritten documents?
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Modern OCR handles printed text reliably. Handwriting is harder but increasingly viable — modern multimodal LLMs like GPT-4o and Claude can read most clear handwriting. For high-stakes use cases, we add a human review queue for low-confidence extractions.
How accurate is document extraction?
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On standard documents (invoices, receipts, structured forms), well-engineered pipelines hit 95 to 99% field-level accuracy. On freeform documents (contracts, medical notes), 85 to 95% is realistic. We always report per-field accuracy so you know exactly where to put humans in the loop.
How long does an NLP project take?
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A focused extraction or classification project ships in 4 to 8 weeks. Complex document understanding projects with custom schemas, multi-language support, or integration into existing workflows take 8 to 14 weeks. We always start with a feasibility prototype.
How much does NLP and document AI cost?
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NLP and document AI projects at Appsmediaz typically range from $10,000 for a focused single-document-type extractor to $80,000+ for enterprise document intelligence platforms with custom schemas and review tooling. We provide fixed quotes after a discovery sprint.
Explore the rest of the AI Lab
Drowning in PDFs?
Send us a handful of sample documents. We'll come back with a feasibility prototype, a rough cost, and a clear view of what's realistic.
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