01 Research content systems

Research publication management: the content system that makes your work findable

Research publication management runs on a content system: the taxonomy, publication library, author pages, search and the workflow that turns PDFs into web pages. Tailwebs plans it with your team before any design, then builds it, including AI-assisted extraction and tagging like the system we built for Vidhi's Crime and Punishment database.

Start with an ecosystem auditSee how a PDF becomes a record
Illustrative · content types
PublicationEach report exists once
  • AuthorTheir profile lists it
  • TopicIts hub features it
  • ProgrammeIts page shows it
  • EventWhere it was presented
  • DatasetThe data behind it

02 From PDF to record

How does a PDF become a structured record?

In the workflow we plan, the document is ingested, AI extracts the fields, entities and citations your content model defines, a person reviews them in a queue, and only approved records are published and searchable.

  1. 01System

    Ingest

    Reports, briefs, laws, policies or judgments come in from uploads, old sites or a scraper.

  2. 02AI

    AI extraction

    The fields, entities and citations your content model defines are pulled out of each document.

  3. 03A person

    Review queue

    An editor checks each record, corrects it and approves it.

  4. 04Library

    Publish

    Only approved records go live, searchable in the library, on author pages and in topic hubs.

It fits flagship reports that need web versions with chapters and key findings, and large document sets such as laws, policies and judgments that have to become searchable data. Datasets rather than documents? See open data portal development.

Shipped: the extraction and tagging

Vidhi Centre for Legal Policy

Crime and Punishment

A scraping engine plus AI extracts punishment data and tags law sections across 7,305 crimes and 370 central laws, each turned into plain language. Tailwebs built it and maintains it. It cut manual effort by 90%, with higher quality, and states have adopted it.

crimeandpunishment.in
The Crime and Punishment database home page, built for Vidhi Centre for Legal Policy

Excel models, calculators and forms rather than reports? See tool digitalisation. The back end of a document database can be built with our Ruby on Rails development team.

Who checks what the AI extracts?

A person does: AI suggests, an editor approves, and nothing is published until they have.

The review step stays in the workflow after launch, not only during the build, so quality holds as the volume grows.

Every record keeps a trail of what was extracted and who approved it.

Review · before anything is published
  1. AI suggests the fields and tags.
  2. An editor checks and corrects them in the review queue.
  3. Only approved records are published.
  4. Each record keeps its trail: what was extracted, and who approved it.

Accuracy is tested against a sample your researchers tagged by hand before the workflow goes live.

04 Taxonomy clean-up

Content taxonomy for research organisations, with or without a rebuild

It starts with a taxonomy workshop with your team, and it can run inside your current system, before or instead of a rebuild, so a later migration is clean.

  1. Taxonomy workshop

    Your team agrees the topics, programmes, regions and formats, and what each one means.

  2. Merge

    Overlapping tags, often hundreds of them, become one agreed set.

  3. Re-tag

    Your content is re-tagged, AI-assisted and approved by a person.

  4. Keep in sync

    Author pages stay in step with publications, media and events.

When the rebuild comes, the clean model carries straight into a research website. The planning method behind it is on policy and research.

05 The editor's day

What does running it look like for editors?

A routine a non-technical team can keep: upload, check, approve, and the work appears everywhere it belongs.

  1. Upload

    An editor uploads the new report.

  2. Check

    The extracted fields wait in the review queue for a person to check.

  3. Approve

    The right people sign off, through roles and approvals.

  4. Appear

    The report shows in the library, on its authors' pages and in its topic hubs.

  5. Digest

    Optional: a digest or newsletter pulls new work by topic.

Want to score your current workflow first? Take the research website checklist.

06 Where to start

Where it fits, and where to start

Usually as part of a research website build, or as a clean-up before one. Either way, it starts with an ecosystem audit.

  • Start

    Discovery Sprint

    An ecosystem audit of your content and sites, with a written build estimate.

    ₹1.5–3 lakh

    $2,000–4,000

    2–3 weeks

  • Build

    Research Website

    The content system built into the site: content model, library, author pages and search.

    ₹6–20 lakh

    $8,000–25,000

    8–16 weeks

What moves the price: the content model, migration volume, search and integrations.

Citation metadata on your site (schema.org Report and ScholarlyArticle, and a DOI where you use one) and persistent URLs come as part of the build. The sprint is on policy and research; the build on research websites.

07 FAQ

Questions about research content systems.

What is a taxonomy and why does a think tank need one?
A taxonomy is the agreed list of topics, programmes and formats used to tag every piece of work; without one, related work never links up and search filters stop working.
Can you clean up our existing tags without a full rebuild?
Yes, we can audit and merge your existing tags and re-tag content in your current system, before a rebuild or without one.
Can AI tag our publications automatically?
Yes, AI can suggest tags and extract key data, as it does in Vidhi's Crime and Punishment database.
How do you check AI extraction is accurate?
We test it against a sample your researchers have tagged by hand, and keep a review step in the workflow.
Will an AI search make up answers?
It is built not to: it answers only from your own publications and cites the source for each answer, the way the NFTR Assistant on the Frontier Tech Repository portal does, and it reports questions it could not answer.
PDF to web: should every report become a web page?
No, flagship reports and briefs benefit most; the rest can stay as PDFs with good metadata and full-text search.
Can a content system power a research repository website?
Yes, the repository holds each publication once, linked to its authors, topics, datasets and events, with its PDFs searchable, and it can run inside your current site or feed a new one. We built the NITI Aayog Frontier Tech Repository portal, with the NFTR Assistant built in.