Presear builds production NLP pipelines — LLM fine-tuning, entity extraction, semantic search, summarisation, and conversational AI — at enterprise scale.
Technical Depth
From large language model fine-tuning to real-time dialogue systems — we match the right NLP approach to your data, domain, and deployment constraints.
Fine-tuning and deploying foundation language models — GPT-4, LLaMA, Mistral, Gemma — on proprietary enterprise data using full fine-tuning, LoRA, and QLoRA for domain adaptation. We also build retrieval-augmented generation (RAG) architectures that ground LLM outputs in your verified knowledge base, reducing hallucinations significantly.
Identifying and classifying entities — people, organisations, locations, dates, medical terms, legal clauses — and extracting relationships between them from unstructured text. We build span-based NER models, coreference resolvers, and knowledge graph population pipelines for domains with bespoke entity taxonomies.
Moving beyond keyword matching to meaning-aware retrieval — encoding documents and queries into dense vector spaces where semantic similarity enables accurate search across millions of documents in milliseconds. We build embedding pipelines with sentence transformers and deploy them on vector databases for enterprise-scale semantic retrieval.
Fine-grained opinion mining, aspect-level sentiment analysis, and multi-class intent detection for customer feedback, support tickets, and conversational inputs. We build models that go beyond positive/negative polarity to detect nuanced sentiments — frustration, urgency, satisfaction — at aspect and entity level for actionable business intelligence.
Abstractive and extractive summarisation of long-form documents — legal contracts, medical records, research papers, earnings calls — and controlled text generation for report drafting, product descriptions, and content automation. We fine-tune encoder-decoder models and instruction-tuned LLMs for domain-specific generation tasks with factual grounding.
End-to-end dialogue systems with natural language understanding (NLU), dialogue state tracking, policy management, and natural language generation (NLG) — deployed as voice or text chatbots across customer support, internal helpdesks, and transactional workflows. We build multi-turn, context-aware systems with fallback handling and human escalation.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
Text data is rarely clean — enterprise corpora contain encoding errors, boilerplate noise, duplicate content, and sensitive PII that must be removed before any model training. We build automated data ingestion and cleaning pipelines that handle diverse formats — PDFs, emails, HTML, databases — and produce normalised, deduplicated, privacy-safe training sets.
How text is represented determines what a model can learn. We select tokenisation strategies — BPE, WordPiece, SentencePiece — and embedding architectures appropriate to the domain vocabulary, language diversity, and downstream task, including domain-adaptive pretraining on your corpus when general-purpose tokenisers under-serve your vocabulary.
We select the most efficient training strategy for your data and compute budget: full fine-tuning for maximum accuracy, LoRA/QLoRA for parameter efficiency, or domain-adaptive pretraining for vocabulary-heavy domains. All experiments are tracked with version control, enabling comparison across training configurations before production commitment.
NLP models that perform well on benchmarks can still fail in production through hallucinations, biased outputs, or adversarial prompt exploitation. We run comprehensive evaluation batteries — task accuracy, hallucination rate, demographic bias audits, and red-teaming — before any model is approved for deployment.
NLP production requires low-latency, high-throughput inference at scale. We deploy models with vLLM or TGI for optimised transformer serving, apply quantisation (INT8/INT4) and speculative decoding for latency reduction, and containerise APIs behind autoscaling Kubernetes services — with monitoring for output drift, latency degradation, and token usage.
Real-World Impact
Production NLP deployments across industries — systems that extract value from language at scale, every day.
Core Challenge
Support teams face thousands of repetitive enquiries daily — order status, account changes, refund requests — that consume agent capacity without adding value. Traditional rule-based chatbots fail on paraphrased queries and escalate too frequently, frustrating customers while still requiring significant human oversight.
Who Benefits
E-commerce platforms, financial services firms, telecoms operators, and SaaS companies that handle high-volume, multilingual customer queries and need intelligent triage, automated resolution, and context-aware escalation that measurably reduces first-response time and agent load.
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Legal teams spend enormous hours reviewing contracts, identifying obligations, flagging risk clauses, and comparing versions — work that is repetitive, error-prone at scale, and blocks faster deal cycles. Manual review cannot keep pace with the volume of agreements in high-throughput legal and procurement workflows.
Who Benefits
Law firms, in-house legal departments, contract management platforms, and procurement teams that need automated clause extraction, risk scoring, obligation tracking, and redlining suggestions that accelerate review cycles without replacing legal judgment.
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Electronic health records contain vast amounts of unstructured clinical narrative — physician notes, discharge summaries, radiology reports — that cannot be queried or analysed at scale. Extracting structured clinical concepts, medications, diagnoses, and timelines from free text is essential for care quality analytics and research.
Who Benefits
Hospitals, health insurers, clinical research organisations, and digital health platforms that need structured clinical data extracted from unstructured EHR text for population health analytics, coding assistance, prior authorisation, and research cohort identification.
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Global media companies and publishers produce content across dozens of languages and must classify, tag, summarise, and make it searchable without per-language specialist teams. Multilingual NLP models enable consistent content intelligence across language boundaries at a fraction of the cost of manual processing.
Who Benefits
News agencies, OTT platforms, social media analytics firms, and global e-commerce companies that need multilingual content classification, cross-lingual search, automatic translation with domain preservation, and sentiment analytics across international markets.
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Foundation models, vector databases, serving frameworks, and orchestration tools — chosen for production reliability and enterprise scale.
Frequently Asked
Answers to the questions engineering leaders, product teams, and CTOs ask before starting an NLP engagement with Presear Softwares.
Ask Our NLP TeamPartner with Presear Softwares to build NLP systems that go beyond generic models — fine-tuned on your data, evaluated rigorously, and designed to deliver business value at production scale.