Libraries - Off the shelf data models
In today’s post, see how our off the shelf data models are used for our library SEMA Financial Statements.
Our libraries:
SEMA FS Annual Financials
SEMA FS Interim Financials
SEMA SII Solvency II
SEMA MB Municipal Prospectus
SEMA CB Corporate Bonds
Why use our libraries?
Pre-trained, pre-built data models ready to go.
Normalisation & validation of row data attributes.
Language Agnostic, Cloud compatible.
Map to your taxonomy, no coding.
Standard mapping features.
Improve models with human feedback via Web UI.
Faster without compromising on accuracy.
Libraries – SEMA Financial Statements
We follow a three steps approach to parse documents to meet your exact extraction requirements:
Capture the data “raw” as reported in the doc, in specific fields or schedules,
Normalise, derive, enrich or validate the raw data in interim schedules and,
Create an output schedule mapped to your taxonomy (including any internal logic you need to apply).
About Semantic Evolution
We are a fast-growing technology firm with offices in London and Manhattan, dealing with great clients across the globe.
Our product uses artificial intelligence techniques to capture data from unstructured documents such as pdf's, spreadsheets and emails. Our parsing technology provides efficiencies to repetitive tasks which would normally require the time-consuming manual extraction of data.
Our unique scientific approach, industry leadership and total transparency bring intelligence to our client’s data.
