Automated Game Lore Wiki

For

Product leads at independent game studios

Solves

Official game documentation is often incomplete and outdated while community knowledge is scattered across multiple platforms.

Delivers

A structured, searchable knowledge base with source-linked pages and a public changelog

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,500 for the full product

Run it

Inside your business, or as part of your offer to clients

For product leads at independent game studios, turn patch notes, developer streams and game data exports into a structured, searchable knowledge base.

1. Ingest official communication feeds.
2. Extract structured entities like items and quests.
3. Generate narrative pages with cross-references.
4. Detect contradictions between versions.
5. Flag conflicts for human review.
6. Publish nightly updates with source attribution.

What goes in, what comes out

The workflow

1. In
   Start with

   Patch note URLs, developer stream links, game data exports, approved community guides

2. 1

   Connect data sources

3. 2

   Ingest historical data

4. 3

   Run entity extraction

5. 4

   Generate initial pages

6. 5

   Run fact-checking

7. 6

   Review flagged conflicts

8. 7

   Publish site

9. Out
   Finish with

   A structured, searchable knowledge base with source-linked pages and a public changelog

AI does the heavy lifting, people stay in charge

Use language models to parse unstructured text and extract entities. Use retrieval-augmented generation to write pages based on official sources. A human reviewer must verify lore accuracy and resolve contradictions before publication.

What your team sees

Key screens: Entity dashboard, source feed monitor, fact-check queue, changelog view. Use a dashboard to list all game entities, a feed monitor to show incoming official updates, a queue for human review of conflicts, and a changelog to track edits. The main screen is the entity dashboard, followed by the source feed monitor and fact-check queue.

Accounts and administration

User roles for studio staff, version control for page edits, approval workflows for content, audit logs for source attribution, and access controls for public facing site

Integrations and data access

Steam patch notes, YouTube API, game data exports (JSON/XML)

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product leads at independent game studios use it to solve "official game documentation is often incomplete and outdated while community knowledge is scattered across multiple platforms"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Provide the site to the studio and measure the reduction in support tickets related to game mechanics and lore
4. Measure, then decide. Track number of questions answered without Discord search and accuracy rate of generated pages. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Build for one indie studio with a single game. Ingest three months of patch notes and five blog posts. Generate pages for 200 core entities. Manual review of all generated content

After the MVP. Automated daily monitoring of official channels, real-time updates to the site, and integration with community support tools

What the build depends on. NLP models, vector databases for entity linking, static site generator

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

$500 per month per game

Message to test

Turn your patch notes into a living wiki that updates itself

Where to find buyers

Indie game dev forums, Steam community hubs, Discord servers

Lead magnet

A free audit of your current community documentation

The first 30 days

1. Week 1: Set up ingestion pipelines for patch notes and streams.
2. Week 2: Run entity extraction on historical data and generate initial pages.
3. Week 3: Implement fact-checking logic and set up review queue.
4. Week 4: Launch site and gather feedback on accuracy

Paid pilot

Provide the site to the studio and measure the reduction in support tickets related to game mechanics and lore

Success metrics

Number of questions answered without Discord search, accuracy rate of generated pages

Retention and expansion

Monthly subscription for continuous updates and new game support

Why clients would pick it

Accumulated data on game lore and version history creates a unique dataset that improves accuracy over time, making it harder for competitors to replicate

Alternatives and positioning

Manual wiki editing, community-maintained wikis like Fandom, and static documentation sites

Main delivery costs

AI API costs for text processing and storage

Strict source linking to prevent hallucinations, manual approval gates for lore-heavy content, and version rollback capabilities