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Read More →OpenMalo builds custom natural language processing solutions for Australian businesses — from sentiment analysis and entity recognition to intelligent document classification, contract extraction, and automated text summarisation. Our Melbourne NLP team has delivered 40+ production NLP systems processing millions of documents per month for Australian enterprises across legal, financial services, healthcare, and retail.
Yes — Named Entity Recognition (NER) and document extraction models identify and extract structured data (names, dates, amounts, clauses, product codes) from unstructured text at scale. We fine-tune transformer models on your document types for 90–95% extraction accuracy on Australian legal, financial, and regulatory documents.
We build aspect-based sentiment analysis models that classify sentiment (positive/negative/neutral) at the level of specific product features, service attributes, or customer experience touchpoints — going beyond simple document-level sentiment to give you actionable insights by topic.
We fine-tune transformer-based classifiers (BERT, RoBERTa, or DistilBERT) on your labelled document corpus. The model routes incoming documents to the correct category — contract type, complaint classification, invoice type, support ticket category — automating manual triage workflows that previously required dedicated staff.
Yes — we build extractive summarisation (selecting key sentences) and abstractive summarisation (generating new summary text using LLMs) pipelines. For legal and financial documents, we extract the most material clauses and obligations into a structured summary — dramatically reducing document review time.
We implement hybrid search combining traditional BM25 keyword search with semantic vector search using sentence-transformer embeddings — providing search that understands synonyms, paraphrasing, and conceptual relevance. The result is a search experience that finds the right document even when users don't use the exact terminology.
Yes — we specifically tune NLP models for Australian English, including Australian place names, organisation names, industry terminology, regulatory language (ASIC, APRA, ATO, TGA), and Australian slang where relevant. Generic English NLP models trained predominantly on US text miss these important regional nuances.
We expose NLP capabilities as RESTful APIs or event-driven microservices that integrate with SharePoint, Salesforce, MYOB, Xero, and custom DMS platforms. Document processing pipelines can be triggered by file upload, email receipt, or scheduled batch runs — fitting into your existing operational workflow without disruption.
NLP pipelines processing sensitive documents are deployed in your own cloud environment or on-premise — no document content leaves your infrastructure. We implement document-level access controls, audit logging of all processing events, and PII detection and redaction for documents that must be anonymised before analysis.
OpenMalo's NLP engineering team has been building natural language processing systems since the pre-transformer era — from statistical NLP with spaCy and NLTK through to today's fine-tuned BERT variants and LLM-powered document intelligence systems. Our Melbourne team has delivered 40+ production NLP systems for Australian law firms, banks, insurance companies, health funds, and government agencies — processing over 10 million documents per month in production environments. Our NLP models are specifically tuned for Australian English, regulatory language, and industry-specific terminology.
We take a pragmatic approach to NLP model selection: fine-tuned transformer models (BERT, RoBERTa, DeBERTa) for classification and extraction tasks where training data is available; LLM-powered approaches (GPT-4o, Claude 3.5) for summarisation and generation tasks where flexibility is more important than latency. Every NLP system is delivered with an accuracy benchmark report, a data drift monitoring configuration, and a model update plan. Post-launch support and retraining starts from AUD $1,500/month.
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Custom NER models extracting structured entities (organisations, persons, dates, monetary amounts, product codes, contract clauses) from unstructured Australian legal, financial, and operational documents at scale.
Learn More →Aspect-based sentiment analysis of customer reviews, NPS responses, support transcripts, and social media — identifying sentiment at the level of specific product features, service attributes, and brand topics.
Learn More →Fine-tuned transformer classifiers that automatically route incoming documents (contracts, invoices, complaints, support tickets) to the correct category, team, or workflow — eliminating manual triage at scale.
Learn More →Extractive and abstractive summarisation pipelines for long-form documents — contracts, regulatory submissions, research reports, and clinical notes — producing structured summaries with key obligations and action items highlighted.
Learn More →Hybrid BM25 and vector search systems using sentence-transformer embeddings — enabling intelligent document retrieval across large knowledge bases, legal repositories, and product catalogues that understands meaning, not just keywords.
Learn More →End-to-end NLP pipeline development and integration — from document ingestion and pre-processing through model inference to structured output delivery via REST APIs, Webhooks, or event-driven architectures.
Learn More →Industry-leading tools and frameworks chosen for performance, scalability, and long-term maintainability.
We classify your NLP task (extraction, classification, summarisation, search), conduct a data audit assessing volume and labelling quality, and define accuracy, latency, and throughput success criteria before selecting the modelling approach.
We design a data annotation scheme, select or build a labelling tool (Prodigy, Label Studio, or custom), and benchmark candidate models (fine-tuned transformers vs. LLM-prompting) on your sample data before committing to a training approach.
Fine-tuning on your labelled corpus with rigorous evaluation on hold-out test data. NER models are evaluated by entity type F1-score; classifiers by precision, recall, and confusion matrix analysis. Integration API is developed and load-tested in parallel.
NLP model is deployed as a containerised API with autoscaling. Accuracy monitoring tracks per-class F1 and data drift in production. Quarterly retraining cycles incorporate new labelled examples from production edge cases.
Consistently rated as a top technology company in Australia — backed by verified client reviews on Clutch and GoodFirms.
We sign an NDA before any discussion. All IP belongs to you — no shared code, no reuse on completion.
Access to project management tools, weekly progress reports, and live sprint demos throughout delivery.
Melbourne and Sydney offices with real people you can meet in your time zone. Invoiced in AUD.
Agile delivery with fortnightly demos so you see progress, give feedback, and stay in control at every sprint.
We don't just deliver and disappear. Structured post-launch support and ongoing development partnerships available.
"OpenMalo delivered exactly what we needed — on time, on budget, and with a level of quality that exceeded our expectations. The team communicated brilliantly throughout."
"The technical quality and attention to detail from the OpenMalo team is outstanding. Our users love the end product and our business metrics improved significantly post-launch."
"Fast, reliable, and professional. OpenMalo understood our requirements immediately and delivered a solution that has genuinely transformed how we operate. Highly recommended."
LLM-powered document intelligence, summarisation, and content generation at enterprise scale.
Explore →Custom ML models for prediction, classification, and recommendation beyond natural language use cases.
Explore →Conversational AI chatbots powered by NLP and LLMs for customer support and workflow automation.
Explore →Image and video AI for document digitisation, OCR, and visual quality control alongside NLP pipelines.
Explore →AI readiness assessment and NLP use case prioritisation before committing to development.
Explore →Robust REST APIs to expose your NLP capabilities to downstream applications and integrations.
Explore →Get a free NLP consultation from our Melbourne team — we respond within 24 hours with a problem framing approach and sample data assessment.
Local teams across Australia backed by a global delivery centre — giving you the best of both worlds.
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