Editorial Research Platform

Citation-grounded newsroom research in 3–5 minutes.

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Inspiration

Professional newsroom research is often slow because relevant information is scattered across internal archives, previous coverage, wire services, trusted publications, television programmes, and official documents.

A researcher may spend 30–60 minutes finding the right material, checking whether it is current, tracing quotations, comparing evidence, and preparing something another editor can verify.

Generic AI chatbots can produce quick answers, but newsroom research requires more than speed. It requires source provenance, publication dates, document status, evidence relationships, citations, and editorial accountability.

I started building the Editorial Research Platform around one principle:

AI prepares. Editors decide.

The goal is to help newsrooms prepare citation-grounded research in approximately 3–5 minutes while keeping final editorial judgment with editors.

The 3–5 minute target is still to be validated through real newsroom tasks.

What the current development build does

Editorial Research Platform is an active development project designed primarily for Hindi newsrooms.

The current build demonstrates a controlled research workflow in which an editor:

  • enters a research question
  • selects the Research Type
  • selects the Research Sources
  • starts the research process
  • reviews the retrieved evidence, citations, and Hindi Research Brief

The current interface includes Research Types such as:

  • Latest Status
  • Timeline
  • Quote Search
  • Comparison
  • Background
  • Other

It also includes source-scope controls for:

  • All Sources
  • Web
  • Wire
  • TV / Programmes
  • Official Documents

The selected source scope is passed into the research request as an actual source filter, so it affects which parts of the controlled knowledge base are searched.

The demonstrated workflow is:

Research question → controlled retrieval → evidence checks → cited Research Brief → editor review

The product is not intended to act as an autonomous newsroom agent or make final editorial decisions.

RAG and the Editorial Intelligence Layer

RAG can retrieve potentially relevant material, but semantic relevance alone is not enough for newsroom use.

A historical report may be related to a query but may not support the latest status. A secondary article may provide useful background but should not automatically be treated as equivalent to an official document.

For this reason, I am developing an Editorial Intelligence Layer alongside retrieval.

It is being designed to evaluate factors such as:

  • research type
  • selected source scope
  • source authority
  • publication date and recency
  • document status
  • latest-status eligibility
  • evidence relevance and sufficiency
  • citation support
  • contradictions
  • missing information

Retrieval finds potentially useful material.

The Editorial Intelligence Layer is intended to help determine how that material should be ranked, qualified, or surfaced for editorial review.

It does not make the final editorial decision.

How I am building it

I am a newsroom-domain founder, not a conventional software engineer, so Codex has played an important role throughout the development process.

I have used Codex to:

  • inspect the repository
  • implement features
  • create tests
  • debug failures
  • review code
  • validate architecture decisions

GPT-5.6 currently supports bounded, citation-grounded generation of the Hindi Research Brief.

It helps organize evidence into a structured research output while the product remains under active development.

The platform is being built as a production-oriented TypeScript monorepo with separate web, API, shared-contract, database, and evaluation packages.

The architecture is automation-first but editor-governed.

Automated services are being developed to handle structured and repetitive work such as:

  • source handling
  • approved-source fetching
  • ingestion
  • normalization
  • document and version creation
  • chunking
  • evidence extraction
  • retrieval
  • citation mapping
  • Research Brief preparation

Editors are intended to retain control over:

  • source trust
  • research intent
  • exceptions
  • review
  • final judgment

The current development build includes working foundations and selected implemented components for:

  • controlled source registration
  • approved-source workflows
  • article fetching and ingestion
  • asynchronous ingestion jobs
  • document, version, and chunk persistence
  • structured evidence extraction
  • citation handling
  • research-quality validation
  • latest-status evidence and claim validation
  • Research Type selection
  • Research Source Scope selection
  • cited research-result presentation
  • automated tests for validation, retries, persistence, and concurrency

Some of these components are already implemented and tested. Others are still being expanded and integrated into the broader workflow.

Deterministic services remain responsible for:

  • validation
  • identity
  • persistence
  • concurrency
  • retries
  • source governance
  • safety
  • evidence eligibility
  • citation integrity

AI assists with extracting, organizing, and preparing research.

It does not replace editorial judgment.

Challenges

The hardest problem has not been generating fluent text.

The real challenge is designing a system that professional newsrooms can verify and trust.

The architecture has to address risks such as:

  • unsupported claims
  • outdated evidence presented as current
  • citation mismatches
  • loss of source provenance
  • duplicate or superseded material
  • weak evidence supporting strong claims
  • contradictory evidence being ignored
  • autonomous editorial conclusions

Another challenge has been designing around real newsroom behaviour rather than a generic chat interface.

Researchers and editors need to specify both what kind of research they are doing and which source categories should be searched.

The system therefore has to support editorial control before, during, and after retrieval.

What I learned

I learned that useful newsroom AI is not mainly about producing fast answers.

It is about:

  • retrieving appropriate evidence
  • preserving where it came from
  • distinguishing relevant evidence from eligible evidence
  • exposing uncertainty and contradiction
  • allowing editors to verify important claims

I also learned that retrieval and Editorial Intelligence should remain separate layers.

Retrieval finds related material.

Editorial Intelligence evaluates how that material relates to:

  • authority
  • recency
  • document status
  • evidence sufficiency
  • claim support

Codex has also shown me how a domain-led founder can direct and validate the development of a substantial technical product without giving up control of the product vision.

Current status

This is an active development build, not a finished production product.

The current version demonstrates:

  • the Research workspace
  • Research Type control
  • Research Source Scope control
  • source filtering
  • research-request processing
  • Hindi cited results
  • inline citations
  • cited evidence review
  • selected ingestion foundations
  • selected evidence-processing services
  • automated validation and testing foundations

The broader Editorial Intelligence Layer, stronger retrieval, contradiction handling, missing-information handling, and the complete end-to-end newsroom workflow are still under development.

The current demo uses a controlled research scenario and controlled source evidence.

The 3–5 minute research goal is a product target and still needs to be validated through real newsroom tasks.

What’s next

The next stage is to:

  • strengthen hybrid retrieval
  • add more granular newsroom filters
  • complete entity, topic, claim, and evidence relationships
  • improve contradiction and missing-information handling
  • improve Hindi newsroom-ready Research Briefs
  • validate the 3–5 minute target through real newsroom research tasks
  • test the workflow with newsroom editors and researchers
  • improve source coverage while preserving source control and citation integrity

The long-term goal is not to build an autonomous newsroom agent.

It is to build a reliable editorial research assistant that reduces repetitive work, improves evidence visibility, and helps editors make faster and better-informed decisions.

AI prepares. Editors decide.

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