Turn messy enterprise files into retrieval-ready intelligence
The Data Engine parses raw company data, extracts structure and meaning, applies redaction and enrichment, and emits a retrieval-ready package the Search Engine can serve.
Parse
Any file type in. Structured output out.
Raw files in. Retrieval-ready package out.
Enterprise retrieval does not fail on models first. It fails on raw data.
PDFs, scans, spreadsheets, images, emails, and fragmented internal content are not retrieval-ready. Generic RAG stacks treat them like input blobs. The Data Engine prepares them for search before retrieval ever begins.
Raw files are not structured enough for reliable retrieval
The Data Engine converts enterprise files into structured, inspectable output with layout, content, and provenance.
Messy corpora need more than chunking
The Data Engine extracts entities, relations, metadata, and optional graph signals before they reach search.
Security and governance cannot be bolted on later
The Data Engine applies redaction and controlled processing inside the same system.
One engine for preparing enterprise data for retrieval
Not another parser. Not another point tool. One preparation layer for enterprise retrieval.
One deterministic preparation lane turns messy enterprise inputs into structured, policy-aware, retrieval-ready output without splitting the work across fragile tools.
Bring in raw enterprise inputs without flattening them into text first.
Preserve structure, layout, tables, and document boundaries.
Pull out entities, relations, and metadata before retrieval begins.
Apply policy and masking inside the same deterministic workflow.
Attach provenance, graph signals, and cross-document linking.
Emit one portable handoff the Search Engine can actually serve.
From file to package
Parse any enterprise file
PDFs, spreadsheets, slides, images, emails, and more become structured output instead of raw text blobs.
Extract structure and meaning
The engine identifies entities, relations, metadata, and document structure in a way downstream retrieval can use.
Apply redaction and enrichment
Sensitive content can be redacted, while additional enrichment prepares the data for graph and higher-quality retrieval.
Emit a retrieval-ready package
The output is a portable artifact with chunks, provenance, metadata, and enrichment signals.
Portable retrieval handoff with inspectable state.
The output is not text. It is a retrieval package.
This is the handoff between raw enterprise data and high-performance retrieval.
One portable artifact for search, provenance, and downstream intelligence.
Customer must provide renewal notice 90 days prior to expiration. Linked entities, source page, and policy state stay attached.
Better than generic RAG preprocessing
Fully unsupervised structuring yields entities, labels, relations, ontologies, and a disambiguated connected graph that augments both graph search and BM25 hybrid retrieval.
Parse. Chunk. Recover meaning later. The system stays text-first, so structure, ontology, and graph augmentation arrive too late or not at all.
Text first, structure later
Parsing collapses layout, labels, provenance, and ontology context into raw text too early.
Chunking becomes the recovery mechanism
Meaning, entities, and relations are guessed after chunking instead of being extracted before retrieval.
Graph and lexical retrieval stay disconnected
There is no shared graph, no stable IDs, and no metadata layer to enrich hybrid BM25 search.
Manual cleanup creeps into production
Labels, ontology mapping, and disambiguation turn into brittle downstream work instead of core pipeline output.
The Data Engine automatically emits structured output, entities, labels, relations, ontologies, and stable IDs that stay attached to the retrieval package.
Entities resolve to stable IDs and ontology classes, so graph retrieval and lexical retrieval can share the same augmentation layer.
The graph can expand a query through disambiguated entities, relation edges, and ontology classes at retrieval time.
Lexical retrieval gets enriched fields, labels, and ontology-backed metadata instead of depending on raw text alone.
One inspectable package can drive graph retrieval, metadata enrichment, and hybrid BM25 search without manual ontology maintenance.
Already real.
Now being packaged into the next enterprise release.
Latence is not starting from slides. The core components already exist today — across the current cloud product, SDK, and open-source retrieval infrastructure.
Use the existing product to process documents, extract structure, and build intelligence workflows today.
Develop against the current pipeline and Data Package model now.
Open-source proof of a real local retrieval engine built for multimodal, multi-vector search.
Open-source proof of real encoder serving for modern retrieval models.
The waitlist is for the next unified enterprise release — not for a concept that does not exist yet.
Bring your own LLM.
Deploy where your data lives.
Start quickly in any environment. Test. Iterate. And move it behind your walls.
Managed pilot
Fastest path to validation with the same product architecture.
Customer cloud / private VPC
Controlled deployment for teams not ready for full self-hosting.
Self-hosted / air-gapped
Built for local GPU environments and high-control enterprise deployments.
If retrieval quality matters, start with the data layer.
The next Latence release packages the Data Engine into a cleaner, local-first enterprise product. If that is what you need, join the early-access list.
The technology exists today. The waitlist is for the next unified release.