GenAI · Media / Publishing

Custom RAG System & AI Tools for Esakal Newspaper Agency

For Esakal, a leading newspaper agency, we built a custom retrieval-augmented (RAG) system and a suite of AI tools — proofreading and correction, content summarisation and more — that cut manual effort and reduce errors in a high-volume newsroom.

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Custom RAG Proofreading & correction Summarisation Editorial workflow Self-hosted
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The challenge

What businesses struggle with

A high-volume newsroom like Esakal produces large amounts of copy under tight deadlines. Manual proofreading, fact-checking against archives and summarising long pieces is slow and error-prone, and editors spend time on repetitive corrections instead of judgement-heavy work.

Off-the-shelf AI tools weren’t grounded in Esakal’s own content and style, so they couldn’t be trusted for accuracy or consistency.

Our solution

How we solve it

We built a custom RAG system grounded in Esakal’s own archives and style guidelines, so AI assistance reflects their language, context and standards rather than generic output. On top of it we delivered targeted tools: AI proofreading and correction, content summarisation, and supporting editorial utilities.

These tools slot into the existing editorial workflow, helping writers and editors catch errors, shorten and repackage content, and retrieve relevant past coverage quickly — reducing manual effort and the issues that slip through under deadline pressure.

What you get

Key capabilities

Proofreading & correction

AI-assisted grammar, style and consistency checks aligned to the house style.

Summarisation

Condense long articles and source material into clean, usable summaries.

Grounded retrieval (RAG)

Answers and context pulled from the organisation’s own archives.

Workflow fit

Tools integrated into how the newsroom already writes and edits.

Private & self-hosted

Deployed so proprietary content stays within the organisation’s control.

Iterative tuning

Continuously refined against real editorial feedback and outputs.

Tech, hosting & deployment

Built and deployed properly

The RAG system and tools are deployed on infrastructure controlled by the organisation, keeping proprietary archives private. We combine a vector knowledge base over their content with task-specific AI tooling, accessible to editors through simple interfaces, with monitoring and ongoing accuracy tuning.

LLM (GenAI) RAG / vector DB Custom tooling Self-hosted deployment Editorial integrations

Outcomes

The difference it makes

  • Reduced manual proofreading and correction effort.
  • Fewer editorial errors slipping through under deadline.
  • Faster summarisation and reuse of long-form content.
  • AI assistance grounded in the organisation’s own material.

Getting started

How to start with us

1

Understand the newsroom

We map editorial workflows, content sources and quality standards.

2

Ground the RAG

We index archives and style guidance to ground the AI in real content.

3

Build the tools

We deliver proofreading, summarisation and retrieval tools editors can use.

4

Deploy & refine

We deploy privately and tune accuracy against editorial feedback.

FAQ

Questions people ask

What is a RAG system and why does it matter for publishing?
Retrieval-augmented generation grounds an AI model in your own content — archives, style guides, source material — so its answers and edits reflect your facts and voice instead of generic training data. For a newsroom, that means assistance you can actually trust for accuracy and consistency.
How much does a custom RAG and AI tooling project cost?
Pricing is custom-quoted because it depends on the size of your content corpus, the specific tools you need, integration with existing systems, and hosting. After a scoping discussion we propose a phased build with clear deliverables. Contact us for a tailored quote.
Will our proprietary content stay private?
Yes. We deploy the system so your archives and content remain within infrastructure you control, rather than being sent to a shared third-party service or used to train external models. Privacy of proprietary editorial material is a core design constraint.
Can the tools match our house style?
Yes. Because the system is grounded in your style guidance and past content, proofreading, correction and summarisation reflect your conventions, not a generic default — and we tune it against real editorial feedback over time.
Will it replace our editors?
No. These tools handle repetitive proofreading, summarisation and retrieval so editors spend more time on judgement, originality and verification. The human stays in control; the AI removes drudgery and catches errors.
Can you build similar tools for our organisation?
Yes. The same approach — a custom RAG grounded in your data plus task-specific AI tools — applies to any content-heavy organisation: media, legal, research, knowledge management and more. We scope it to your workflows.
How accurate is it, and how do you handle mistakes?
Grounding in your content greatly improves reliability, and we keep a human in the loop for final decisions. We monitor outputs, gather feedback, and iteratively tune the system to reduce errors over time. [CONFIRM any accuracy metrics you wish to publish].
Where do we start?
Start with a scoping session about your content, workflows and the tasks you most want to accelerate. We then index a sample corpus and build a grounded prototype so you can judge quality before a full rollout.

Ready to explore this for your business?

Tell us your goals and we’ll scope an approach, share a tailored quote, and show you how we’d build, host and support it.

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