Contextual AI Review

Supplies a generation component designed to produce responses grounded in retrieved enterprise knowledge rather than relying on frozen, general-purpose LLM outputs.

Offers a reranking component that reorders retrieved chunks based on natural-language instructions to improve relevance of results fed to the generator.

Jointly optimizes retriever and generator components in a unified pipeline, rather than stitching together frozen models and vector databases, to deliver higher accuracy and groundedness.

Provides evaluation tooling to create natural-language unit tests and check whether generated responses match gold answers and are grounded in retrieved knowledge.

Automatically generates synthetic training data under the hood when customers lack their own fine-tuning datasets, steerable using defined end-user personas.

Provides a document understanding pipeline that parses complex enterprise documents, preserving reading order and table structure for downstream retrieval.

Enables enterprises to build specialized retrieval-augmented generation (RAG) agents capable of active retrieval and test-time reasoning over large volumes of structured and unstructured enterprise data.

Allows adjustment of system prompts and retrieval parameters, such as the weighting between lexical and semantic search and the number of chunks retrieved and reranked.

Lets teams jointly fine-tune multiple components of a RAG agent via a Python API to boost performance on domain-specific tasks and workflows.

Uses a custom document understanding pipeline that chunks and embeds data before indexing it into an Elasticsearch cluster, supporting millions of documents per deployment.