We have been applying Machine Learning techniques for a long time, long before Large Language Models and generative AI hit the mainstream.
Turning messy, inconsistent source data into clean datasets fit for ML models, business applications, and reporting. Validation, limit checks, and reconciliation built in from the start, not bolted on afterwards.
Automatically categorising documents, tickets, and customer messages into meaningful buckets. Built in Python and Rust, integrating with the systems you already run.
Scoring the tone of customer communications, reviews, and free-text feedback. Transformer-based models fine-tuned for the specific language your business uses, not a generic one-size-fits-all.
Turning unstructured text into structured, queryable information. Tokenisation, entity extraction, and domain-aware language handling across multiple languages.
Search that retrieves by meaning, not just keyword match. Vector databases, hybrid retrieval, and domain-specific ranking built to fit the shape of your data.
Connecting LLMs to your business data, workflows, and systems. Retrieval augmentation, tool use, permission-aware access, and honest handling of the model's uncertainty.
Connecting an LLM to private business data requires more than a standard retrieval pipeline. Project Lodestone is our R&D project to build permission-aware, auditable AI retrieval.
Running large language models in production means managing the trade-off between model quality and the cost of inference. A series covering the business case, the mechanism, bit packing, and a full Rust implementation.