Large-Scale Text Classification for Life Sciences
A large US research organisation needed industry text classified at volume with consistent labels. Clearlead designed LLM-based pipelines, benchmarked prompt strategies, and processing that could handle hundreds of thousands of documents reliably.
Hundreds of thousands
of text items classified across the programme
Multi-model
evaluation before production prompt patterns were chosen
Resumable pipelines
for high-throughput processing with automated quality checks
The client
- Large US research organisation
- Life sciences, Biotechnology
- United States
Overview
The client carries out research that depends on large volumes of unstructured text relating to biotechnology and pharmaceuticals. They needed those materials classified into structured categories at a scale manual review could not support, with consistent labels across diverse document types.
Clearlead designed and implemented LLM-based classification pipelines: prompt strategies tested across leading models, parallel processing for high volume, and evaluation frameworks to compare performance and reliability before production use.
The result was hundreds of thousands of classifications delivered with consistent treatment of varied inputs, plus reusable patterns for prompt engineering and pipeline design on similar programmes.
Methods
- Large-scale text classification
- LLMs
- Prompt engineering
- Multi-label classification
Engagement type
- Design and build
The situation
The client is a large research organisation in the United States working with textual data across the biotech and pharmaceutical domain. Analysts and researchers needed structured categories extracted from unstructured sources so downstream analysis, reporting, and comparison could proceed without every document being read by hand.
Volume was the constraint. The programme required classification across a corpus large enough that human coding would be slow, expensive, and hard to keep consistent. Inputs also varied in length, style, and subject matter, so a single rigid keyword approach would miss nuance and mis-label edge cases.
The classification task was not a simple single-label sort. Documents could belong to more than one category, and the labelling scheme had to stay stable as new batches arrived. The client needed a system that could run at throughput, recover from API or transient failures, and produce outputs an internal team could trust enough to build on.
The engagement
Clearlead led the technical design and implementation. The work centred on making modern large language models usable for production-scale classification: reliable prompts, comparable model behaviour, and pipelines that could process very large datasets without losing progress when something failed mid-run.
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Prompt engineering and model comparison across leading LLMs on identical small samples before any full-scale processing: this surfaced model-specific failure modes early, and prompt patterns were chosen based on consistency across the classification task rather than surface-level performance on a small demo set.
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High-volume parallel processing with resumable runs, progress tracking, and robust retry behaviour so large batches could complete without manual restarts after intermittent API issues.
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Evaluation frameworks to compare model outputs and measure agreement across diverse inputs, making gaps visible before prompt and pipeline choices were fixed for production.
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Multi-label classification design adaptable to the client's category structure, including automated checks and validation steps so obviously weak assignments could be flagged or rejected before results were handed over.
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Reusable pipeline patterns the client's team could apply when new document sets or related classification programmes were added, without rebuilding the core processing approach each time.
Outcomes
- Hundreds of thousands of text items classified, giving the client structured labels across a large corpus that would not have been practical to code manually at the same pace.
- Consistent classification behaviour across varied inputs, supported by benchmarked prompt strategies rather than ad hoc per-batch prompting.
- Scalable, high-throughput processing with error handling and resumable jobs suited to long-running document batches.
- Classification work from an earlier phase of the programme was structured as a reusable lookup: a meaningful share of a later dataset was resolved through direct matching rather than additional model calls, reducing cost and ensuring consistent treatment across both phases.
Similar applications
The core approach applies wherever a large body of text needs to be labelled consistently, reliably, and at a scale that rules out manual review.
Large-scale document classification
Archives, submission backlogs, or ongoing feeds that need to be sorted into stable categories for search, compliance, or downstream processing.
Model selection before committing to scale
Any large annotation or labelling task where testing several models on a small sample first identifies cost differences and failure modes before the full run is committed.
Regulatory and policy document triage
Consistent labels applied across thousands of regulatory, contractual, or policy files before specialist review.
Classification without a preset taxonomy
Domains where no standard classification scheme exists and the categories themselves must be designed and validated before any labelling can begin.
Reusing prior classification work
Programmes with multiple related datasets where earlier labelling resolves a meaningful share of subsequent items through direct matching rather than additional model calls.
Sensitive text in controlled environments
Medical, legal, educational, or other regulated material that must be processed with an auditable, reproducible methodology and appropriate data handling.
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