Your company's knowledge. Finally connected.
SIGNAL is an enterprise retrieval-augmented generation (RAG) platform that unifies internal documentation, SOPs, engineering runbooks, and policy archives into a single conversational interface. Every answer is grounded in retrieved passages with explicit source citations and enforced access controls.
A cobalt-blue satellite dish with rotating antennas.
WHY LEGACY WORKFLOWS BREAK DOWN.
Critical institutional knowledge is fragmented across handbook PDFs, wikis, ticket threads, and shared drives. Teams waste hours hunting for canonical answers or act on outdated documents.
Keyword search fails when employees ask natural-language questions or don't know exact internal terminology.
Standard LLM chat interfaces hallucinate policy details when detached from verified source documents.
Flat document indexes risk exposing sensitive HR, legal, or project files across permission boundaries.
HOW SIGNAL SOLVES IT.
SIGNAL ingests heterogeneous document stores through a layout-aware chunking pipeline, indexes hybrid dense + lexical vectors with document-level ACL tags, and synthesizes strictly cited responses.
Hybrid semantic + BM25 retrieval with cross-encoder reranking for high precision on technical terminology.
Inline paragraph-level citations that open the exact source section side-by-side.
Role- and group-scoped retrieval filters evaluated before context ever reaches the language model.
BUILT FOR PRODUCTION REALITY.
5 CORE MODULES // SIGNAL
Document Ingestion & Preprocessing
Parses PDFs, Markdown, Office files, and wiki pages while preserving tables, section hierarchy, and metadata.
Semantic Search & Vector Retrieval
Combines dense embeddings with sparse lexical matching so both conceptual queries and exact part/policy codes resolve accurately.
Retrieval-Augmented Generation
Synthesizes concise answers constrained to retrieved context windows, abstaining cleanly when evidence is absent.
Context-Aware Responses with Source Citations
Attaches clickable footnote markers linking every claim to its source document, page number, and last-modified timestamp.
Permission-Aware Enterprise Search
Enforces identity-provider group memberships at query time so users only retrieve knowledge they are authorized to read.
FROM INPUT TRIGGER TO VERIFIED OUTPUT.
Ingest & Normalize
Extracts sections, tables, and access permissions from handbooks and technical docs.
Embed & Tag
Computes embeddings and stores chunks alongside role/group ACL attributes.
Query & Rerank
Rewrites conversational queries, filters by user identity, and reranks candidate passages.
Synthesize & Cite
Generates a grounded response with inline citation badges linked to original pages.
DETERMINISTIC GUARDRAILS + APPLIED INTELLIGENCE.
Retrieval quality determines generation quality. We engineer the ingestion and ranking layers with the same rigor as application code.
Structure-Preserving Chunking
Instead of naive fixed-character slicing, documents are segmented by heading hierarchy and table boundaries so policies and tables stay intact.
Pre-Filter Access Control
Security principals are attached to every chunk record and enforced as mandatory metadata predicates inside the vector query.
Grounded Abstention
System prompts and structured output checks require explicit passage references, instructing the assistant to state when knowledge is missing rather than guess.
SYSTEM ARCHITECTURE & DATA FLOW.
Two-plane architecture separating asynchronous document ingestion/indexing from the low-latency permission-filtered query and synthesis pipeline.
Watches source repositories for document updates, extracting raw text, tables, structural headings, and ACL principals.