Chronicle is an enterprise document intelligence system. It ingests any format, enriches every file with structured AI analysis, and answers questions across your entire corpus in plain English — with sources, with precision, and with the reliability that regulated environments require.
Most of what your organization knows is written down somewhere no one can reach.
Contracts, policies, research, correspondence, reports, recordings — organizations generate knowledge continuously and lose access to most of it almost immediately. It lives in repositories no one can search effectively, in formats that defeat keyword search, across languages that fragment the corpus, in documents that were never meant to be found years later by someone who wasn't there when they were written.
Keyword search was not built for this. Neither was a vector database bolted onto an LLM. Chronicle is.
Four phases. Every document passes through all of them before it becomes searchable or answerable. By the time a query arrives, the work is already done.
Four phases. Every document passes through all of them before it becomes searchable or answerable. By the time a query arrives, the work is already done.
Every incoming file is classified by its actual content, not its extension. Archives are unpacked. Each document is routed correctly before any processing begins.
Text is extracted from everything — native documents directly, scanned files through OCR, audio and video through transcription. Non-English content is detected and translated. Every document emerges as clean, chunked, processable text, regardless of how it arrived.
Each document passes through specialist models in sequence. Named entities are extracted, topics classified against a configurable taxonomy, summaries generated, images captioned. Every enrichment output becomes a searchable dimension — a filter, a facet, an indexed field.
Every text chunk becomes a dense vector encoding its meaning; every image becomes a vector in the same space as text queries. Two documents discussing the same concept in different words end up geometrically close — which is what makes semantic retrieval, and generation, possible.
When a query arrives, Chronicle does not run a search. It runs a pipeline — understanding the question, retrieving on two axes at once, and synthesizing an answer grounded in what it found.
When a query arrives, Chronicle does not run a search. It runs a pipeline — understanding the question, retrieving on two axes at once, and synthesizing an answer grounded in what it found.
When a user submits a query, Chronicle does not run a search. It runs a pipeline. A local language model interprets the query, extracts structured filters expressed in plain English, and rewrites it for semantic retrieval — before the index is touched, and without any data leaving the environment.
Two retrieval legs then run simultaneously. The first applies the extracted filters as hard constraints and matches on structured metadata and keywords. The second embeds the query as a vector and retrieves by meaning, surfacing results that share no literal terms with the query but are semantically aligned. Reciprocal Rank Fusion combines both legs into a single ranking that reflects structural precision and semantic relevance together.
The retrieved passages are handed to a generation model. Chronicle synthesizes a narrative answer grounded in those passages, with every claim tied to a specific document and location.
Nothing is invented. Every statement is verifiable.
This is the precision bar regulated environments require — and the reason Chronicle is built on RAG, not on a model that answers from memory.
Every model runs where your data lives. Every output is traceable to a version and a configuration. Every parameter is tunable without a redeployment.
Every model runs where your data lives. Every output is traceable to a version and a configuration. Every parameter is tunable without a redeployment.
No external API calls. No data leaves your environment. Every model — enrichment, embedding, query understanding, generation — runs on your own infrastructure. Built for environments where data sovereignty is a legal requirement, not a preference.
Every enrichment output is a traceable result of a documented model and configuration. Topic classifications, entity extractions, and generated answers are structured outputs tied to specific model versions and runtime parameters — reviewable, reproducible, and defensible.
Prompts, confidence thresholds, topic taxonomies, and model parameters are tunable at runtime through a configuration interface. When your domain or regulatory environment changes, Chronicle adapts without an engineering engagement.
Three numbers that signal completeness and sovereignty — independent of any single client or deployment.
Three numbers that signal completeness and sovereignty — independent of any single client or deployment.
“Your organization has been generating the answer for years. Chronicle is the system that can finally reason through it.”
Every engagement that draws on Chronicle starts with the pipeline already running, the enrichment layer already configured, and the generation architecture already production-tested on Kubernetes. What changes is the document taxonomy, the entity types that matter in your domain, and the precision thresholds your environment requires. That is the work worth doing together.
Tell us about it — its scale, its formats, its languages, and where your current search breaks down. We'll tell you what Chronicle can do for it.