aqoHISTORY
A Question of History
From forgotten artefacts, we unlock hidden insights, use AI to uncover new connections, and tell the stories they reveal — differently.
Historic documents and wartime correspondence
Hosted on aqo.ai

aqoHISTORY Experiment

Automated Social Media Publishing

Testing how one researched historical story can be transformed into platform-specific media content, then developed into an automated publishing and analytics workflow.

Experiment ID: aqoh-exp2

Status: active test

Feature: multi-platform content generation and future AI media agent

From One Historical Story to Many Media Outputs

This experiment explores how the research, narrative, images and structured data behind a completed aqoHISTORY story can be reused to create a range of platform-specific social media posts.

The example uses AQOH-S001, the story of a commemorative cover posted in Brooklyn on 29 January 1944 to mark the launch of USS Missouri. The same source story has been adapted for X, Instagram, Facebook and LinkedIn, and developed into an Alastair-narrated podcast treatment.

Core principle: the approved aqoHISTORY story remains the canonical source. Social posts are derived publishing outputs rather than separate versions of the historical research.

The Proposed Publishing Flow

1

Research

Digitise the artefact and identify the people, objects, organisations, locations, dates and events it reveals.

2

Create the Story

Produce the canonical narrative and connect it to the structured historical data and approved source images.

3

Generate Outputs

Create copy, captions, hashtags, visual treatments, narration scripts and calls to action tailored to each publishing platform.

4

Publish and Learn

Approve, schedule and publish the content, then analyse its performance to improve future output.

Example Media Posts

Each example begins with the same historical evidence, but the emphasis, length and presentation change to suit the behaviour and audience of the platform.

Example aqoHISTORY X post promoting the USS Missouri story

X

A concise historical hook

The X treatment uses a short opening, a strong image and a direct invitation to discover the complete story.

Example aqoHISTORY Instagram post promoting the USS Missouri story

Instagram

Visual historical storytelling

Instagram places the designed story image at the centre, supported by a fuller caption and discovery-focused hashtags.

Example aqoHISTORY Facebook post promoting the USS Missouri story

Facebook

A more accessible narrative

Facebook allows more room to explain the object, the wider historical event and the purpose of aqoHISTORY.

Example aqoHISTORY LinkedIn post presenting the USS Missouri story and publishing method

LinkedIn

Method, data and innovation

LinkedIn presents the story as a demonstration of structured data, AI-assisted publishing and innovative digital history.

Example aqoHISTORY podcast treatment for the USS Missouri story, narrated by Alastair the AI Historian

Podcast

Alastair — the voice of aqoHISTORY

We have developed the persona of Alastair, “The AI Historian”, as the public voice of aqoHISTORY. Alastair is a Scottish historian who loves finding the hidden stories behind fascinating, forgotten documents from history.

The podcast version turns the researched story into an engaging narrated episode, giving aqoHISTORY a consistent and recognisable voice across audio channels.

Automating Content Creation and Publishing

The first practical stage would be a controlled publishing workflow. Once a story is approved, a service could read its content and structured data, select the approved images and generate draft posts using platform-specific templates.

Near-Term Workflow

AI creates the draft text, proposed image treatment, hashtags, narration script and link. A human editor reviews the output before it is scheduled or published.

Publishing could later be connected to supported platform APIs or to an external social media management service.

Human approval retained

Future AI Media Agent

A specialist agent could review impressions, reach, reactions, comments, shares, link clicks, audience retention and visits to the full aqoHISTORY story.

It could compare performance by platform, story theme, opening hook, image format, publication time and call to action.

Future capability

The AI Improvement Cycle

Create
Approve
Publish
Measure
Improve

Over time, the agent could recommend improved versions or controlled tests, while keeping the approved historical story and its evidence as the source of truth.

Editorial Control and Historical Accuracy

Automation should increase the reach and reuse of historical research without weakening its accuracy. Every generated output should remain traceable to the approved story, the original artefact and its supporting evidence.

Platform-specific content should not simply be shortened copies of the same post. Each output should be written and presented for the expectations of its intended audience while preserving the facts and meaning of the original story.

Experimental status: these images are design mock-ups rather than live posts. The content model, visual templates, publishing process and analytics framework will continue to be refined.