Nex

Accepts documents in multiple formats (PDFs, images, potentially Word/Excel) and converts them into a unified internal representation for downstream processing. Uses format-specific parsers (likely PDF libraries for text extraction, OCR engines for image-based documents) that normalize content into a standardized token stream or document tree, enabling consistent analysis across heterogeneous input types without requiring users to pre-convert formats.

Implements a retrieval-augmented generation (RAG) pipeline where user questions are embedded into a vector space, matched against document chunks using semantic similarity, and then passed to an LLM with retrieved context to generate grounded answers. The system likely chunks documents into overlapping segments, embeds them during ingestion, stores embeddings in a vector database, and at query time retrieves top-k relevant chunks before feeding them to a language model with a prompt template that enforces citation or grounding in source material.

Enables export of documents, extracted data, and analysis results in multiple formats (PDF, CSV, JSON, API) and integration with external systems (CRM, contract management platforms, data warehouses). Implements export pipelines that transform internal representations into target formats, with optional data mapping and transformation rules. Supports both one-time exports and continuous synchronization via APIs or webhooks, enabling downstream systems to consume Nex insights without manual data transfer.

Enables users to annotate documents with comments, highlights, and tags, and supports collaborative review workflows where multiple users can comment on the same document and track changes. Implements a comment threading system with user attribution, timestamps, and optional resolution tracking. Annotations are stored separately from the document, enabling non-destructive markup and version tracking. Supports role-based access control (read-only, comment, edit) to manage review workflows.

Processes multiple documents in parallel through an analysis pipeline that extracts structured insights (key entities, relationships, summaries, risk flags) without requiring explicit user queries. Uses a combination of named entity recognition (NER), relationship extraction, and summarization models applied to document chunks, likely with configurable extraction templates or schemas that define which insights to extract. Results are aggregated across documents to enable comparative analysis and trend detection.

Implements a stateful chat interface where user questions and system responses are maintained in a conversation history, enabling follow-up questions that reference prior context without requiring re-specification of the document or prior answers. The system likely maintains a session state (conversation ID, document context, embedding cache) that persists across turns, allowing the LLM to understand pronouns, implicit references, and cumulative context. Each turn retrieves relevant document chunks based on the current question and conversation history, then generates responses that can reference both the document and prior exchanges.

Generates abstractive summaries of documents at multiple granularity levels (executive summary, section-level summaries, key points) using a hierarchical summarization approach. The system likely chunks documents into sections, generates summaries at each level, then synthesizes section summaries into a document-level summary. Users can configure summary length, focus areas (e.g., 'risks only', 'financial metrics'), and output format (bullet points, prose, structured outline). The implementation likely uses prompt engineering or fine-tuned summarization models to enforce consistency and relevance.

Compares two or more documents to identify differences, similarities, and changes across versions or related documents. Uses a combination of text alignment algorithms (likely sequence matching or diff-based approaches) and semantic similarity to detect substantive changes (clause modifications, term variations) versus formatting differences. Results highlight additions, deletions, and modifications with context, enabling users to quickly identify what changed between contract versions or how similar agreements differ in key terms.

Automatically categorizes documents into predefined classes (e.g., 'NDA', 'Service Agreement', 'Purchase Order') and applies tags based on detected content, metadata, or user-defined rules. Uses a combination of text classification models (likely fine-tuned on domain-specific corpora) and rule-based heuristics (keyword matching, structural patterns) to assign categories with confidence scores. Results enable filtering, organization, and routing of documents without manual categorization.

Automatically extracts structured metadata from documents (dates, parties, amounts, effective periods, renewal terms) and normalizes it into a queryable schema. Uses a combination of named entity recognition (NER) for entity detection, relation extraction to link entities (e.g., 'Party A is XYZ Corp'), and domain-specific pattern matching (regex for dates, amounts) to populate structured fields. Results are stored in a database or knowledge graph, enabling filtering, sorting, and aggregation across documents.

Enables full-text and semantic search across a document corpus, returning results ranked by relevance to the query. Implements a hybrid search approach combining keyword matching (BM25 or TF-IDF) with semantic similarity (embedding-based retrieval) to balance lexical and semantic relevance. Users can search across document titles, content, extracted metadata, and tags. Results include snippets with query terms highlighted and relevance scores, enabling rapid document discovery without manual browsing.

Automatically scans documents against compliance rules, regulatory requirements, or risk criteria to identify potential issues. Uses a combination of pattern matching (regex for prohibited terms), rule-based logic (if-then conditions), and ML-based risk detection (trained on labeled examples of risky clauses) to flag problematic content. Results highlight specific clauses or sections with risk severity levels and remediation suggestions, enabling compliance teams to prioritize review efforts.