Natural Language Processing
Natural language processing consulting
Structure out of the text nobody has time to read.
Classification, entity extraction, and sentiment analysis over documents, tickets, and transcripts too high-volume for manual review - built as a distinct discipline from generative AI, with evaluation against labelled ground truth rather than a subjective read of the output.
What this covers
- Document and ticket classification tuned to your taxonomy
- Entity and relationship extraction from unstructured text
- Sentiment and intent analysis at a volume no team can read manually
- Evaluation against labelled ground truth, not a subjective spot-check
How we engage
- Define the taxonomy and label a representative sample
- Train and evaluate against precision/recall targets you set
- Deploy into the pipeline that currently does this by hand
More in AI & Data Intelligence
Most engagements draw on more than one capability, built as a single stack rather than separate practices.
- AI & Data ScienceFrom a question nobody can answer to a model in production answering it.
- Machine Learning EngineeringModel development that survives contact with real data.
- Computer VisionTeaching a camera to catch what a person would miss on the tenth pass.
- Generative AILLM systems that answer from your data, not the model's guess.
- Agentic AIAutonomous agents for the decisions that are routine, not the ones that aren't.
- Recommendation SystemsThe right item in front of the right person, measured, not guessed.
- Data Wrangling & Feature EngineeringThe unglamorous 80% of a model's performance nobody budgets for.
- Data Engineering & Systems IntegrationTrustworthy data, and systems that were never designed to talk actually talking.
- MLOps & AI Platform EngineeringThe unglamorous layer that decides whether your models survive contact with production.
Common questions about Natural Language Processing
Can't find what you're looking for? Ask us
Do we need a data platform in place before we can do AI?
Not a finished one, but you need the feeds the model depends on to be reliable. Data engineering and MLOps are capabilities inside this pillar precisely because most stalled AI programmes are actually stalled data programmes - we build the two together rather than waiting on one.
Ready to talk through your next move?
Book a 30-minute strategy session with a ECLACTRA™ lead - no sales deck, just a straight conversation about where AI, geospatial, engineering, or fractional leadership could actually move the needle.