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TNS AI Ecosystem

TNS AI Documentation

Comprehensive user guides and resources to help faculty, researchers, and graduate students exploit the AI-powered econometrics and research tools of TNS AI.

Application Ecosystem5 Core Apps
  1. EcoDataExtract international macro statistics (World Bank, IMF, FRED) and micro-level survey databases (VHLSS, PCI).
  2. EcoLabConduct quantitative analysis with over 30 econometric models integrated with AI RAG and Neo4j academic graphs.
  3. EcoLitAutomate academic literature reviews mapping metadata across OpenAlex, Crossref, and ORCID registries.
  4. PDFHUBParse and structure tabular data from financial report PDFs using LiteParse and Prompt Caching.
  5. KEYWORDsAnalyze keyword frequencies in corporate annual reports using optimized local OCR engines.
Ready for international publication

Why EcoData?

An economic and financial data platform standardized for research and teaching.

Clean, publication-ready data

All data is cleaned and standardized into panel/time-series formats, ready for research and international publication.

Rich and up to date

A multi-domain, multi-source repository, regularly updated from official agencies and organizations.

Complete international macro indicators

Coverage of macro indicators from trusted sources: World Bank, IMF, ADB, UN, FRED, OECD, ILO...

Official Vietnamese data

GSO (Statistical Yearbook, socio-economic reports), Customs (import/export by commodity), surveys (PAPI, PCI, PAR, SIPAS, ICT) and VHLSS, VARHS, VES microdata.

Documentation Directory

Select an application below to view detailed operational guides, feature breakdowns, and deployment instructions.

Integrated tool

Econometric Analysis, built in

Assess the practicality and feasibility of your research right away, through an end-to-end workflow from data preparation to exporting reproducible code.

01

Data preparation

Filter the data scope, select indicators, compute variables, clean the data and review descriptive statistics.

02

Analysis models

Build a proposed model and alternatives, estimate and test them, then run robustness checks and model selection.

03

Reporting

Export results in the citation style you need: APA 7th, Chicago 8th, Harvard 2008, IEEE 2008 and MLA 7th.

04

Reproducible code

Generate the complete code from indicator selection to report export, ready to run on Stata, R and Python.

Citation styles
APA 7thChicago 8thHarvard 2008IEEE 2008MLA 7th
Code export
StataRPython