NLP DEVELOPMENT SERVICES

A large language model is one tool available within NLP, and often not the most suitable one. For repetitive high-volume extraction and classification, a smaller fine-tuned model is frequently more accurate, faster, and substantially cheaper. Large models earn their place on open-ended tasks and where labeled data does not exist. 

Usually because the text changed rather than the model. New terminology, altered document formats, different upstream systems, and shifts in who is writing all move the input away from what the system was trained on. Monitoring input distribution catches this earlier than waiting for complaints.

It is the rate at which independent reviewers assign the same label to the same text. It matters because it caps achievable accuracy: if two of your experts agree eighty-five percent of the time on what a category means, a model cannot reliably exceed that. Measuring it prevents a target being set above what is possible. 

Yes, and this is normally where the work goes. We adapt models using your own text so internal codes, abbreviations, product names, and conventions are learned rather than guessed. A general-purpose model applied to specialist text is where most accuracy is lost. 

We assess the input quality before modeling, because transcription and layout errors limit what any downstream accuracy can be. Where the source is weak we address extraction quality and layout handling first, then measure the language task on top of realistic input. 

We report per category rather than as one figure, split evaluation data by time period where possible, decide deliberately whether false positives or missed cases are more costly, and review actual errors with the people who own the work. A single headline number is not sufficient basis for deployment. 

Yes. We establish performance separately for each language rather than assuming it carries over from English, and we account for text that switches languages mid-document, which is common in customer and internal communication. 

Business text contains personal and confidential detail incidentally, often in fields nobody expected. We identify what is present in a sample, define what is redacted or de-identified before processing, and establish what is retained in training data and logs, according to your requirements.

Yes. We evaluate an existing system per category against held-out data, examine its error patterns, assess the labeled data and specification behind it, and recommend whether the gain is in better labels, a different method, or a rebuild.