Turn raw data into decisions you can defend.
Daytta is the data-heavy analytics platform. It composes three engines — Extract for documents, Graph for memory and knowledge, and Time for forecasting — on the governed Llamox runtime, turning unstructured source material into structured, auditable data products.
Financial markets is our first example. The platform is built to be customized to any data-heavy domain.
Your data is trapped in documents, notebooks, and dashboards.
Every data-heavy team rebuilds the same brittle plumbing: a parser for the PDFs, a notebook for the model, a vector store standing in for memory, and a spreadsheet to glue it together. Then someone asks where a number came from, and nobody has a clean answer.
Daytta is built on the opposite assumption: useful analytics has to be observable, repeatable, governed, and reviewable from the start — so the work survives scrutiny long after it ships.
Three engines, one data platform.
Each engine is a serious product in its own right. Daytta composes them — and each is available directly to teams that want just one.
Documents
Kautious Extract
Turn messy PDFs, filings, web pages, tables, and transcripts into quality-scored, source-grounded JSON.
Explore Kautious ExtractMemory & knowledge
Kautious Graph
A temporal knowledge graph: entities, relationships, facts, and decisions that know what was true and when.
Explore Kautious GraphForecasting
Kautious Time
Run time-series through 30+ models and ship the one that earns it, with backtested metrics and intervals.
Explore Kautious TimeGoverned by Llamox, end to end.
Daytta runs on the Llamox control plane, so every pipeline, model run, and data product comes with the controls a serious team needs.
Approval workflows
High-impact steps pause for human review before they reach production.
Sandboxed execution
Generated code and analysis run in isolated providers, never against raw infrastructure.
Audit trails
Inputs, transformations, decisions, and lineage are recorded as the work moves.
Versioning & rollback
Promote data products through staged deployment with health checks and rollback.
Our first example
Financial markets, end to end.
We proved the platform where wrong answers get expensive. The same engines, pipelines, and governance generalize to any data-heavy domain.
Extract the filings
Pull XBRL facts, sections, and entities out of SEC filings, transcripts, and corporate actions with source grounding.
Remember the context
Keep entities, relationships, decisions, and timelines in a knowledge graph your workflows can query.
Forecast the numbers
Run demand, revenue, and market series through a model tournament and ship the forecast with receipts.
Beyond finance, the same building blocks fit legal, compliance, healthcare, operations, and any team drowning in documents and time-series. Domain packs and custom pipelines tailor Daytta to your data.
Built to be customized.
Domain packs
Start with templates, schemas, validators, and connectors for your field — or generate a pack from a plain-English description of how your team works.
Composable pipelines
Wire Extract, Graph, and Time together with transformations, lineage, quality checks, and schedules into the workflow you actually need.
Your stack, connected
REST, SDK, and MCP interfaces, signed webhooks, and warehouse-ready outputs plug Daytta into the tools you already run.
Get your data out of the prototype phase.
Tell us the data you are fighting with. If it is a fit, we will help you stand up a governed, customizable pipeline this week.