Gamer Knowledge Assistant

For

Competitive players and esports teams

Solves

Game knowledge is scattered across many sites and finding specific details takes longer than the gameplay itself.

Delivers

Instant answers with citations, source links, outdated information warnings

Built in

10 days of creation time, MVP in 2 days

Investment

$5,000 for the MVP, $15,000 for the full product

Run it

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

For competitive players and esports teams, turn game documentation and patch notes into verified answers and source citations.

1. Ingest game wikis and patch notes.
2. Index text and images.
3. Answer questions with citations.
4. Detect outdated information.
5. Support voice input.
6. Flag bad answers for re-indexing.

What goes in, what comes out

The workflow

1. In
   Start with

   Game wikis, official patch notes, community guides, item drop tables

2. 1

   Upload game documentation

3. 2

   Index the corpus

4. 3

   Ingest updates

5. 4

   Answer queries

6. 5

   Verify citations

7. 6

   Refine search parameters

8. Out
   Finish with

   Instant answers with citations, source links, outdated information warnings

AI does the heavy lifting, people stay in charge

Use language models to read entire game wikis and patch notes. Understand context and citations. Detect outdated mechanics. A human verifies the source links and accuracy before the final answer is presented.

What your team sees

Key screens: Query interface, source library, citation viewer. The main screen is a search bar where the user types a question and receives a direct answer with source links.

Accounts and administration

User permissions, query history, source versioning, audit trail of citations

Integrations and data access

Game launcher APIs, wiki hosting platforms, Discord

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

   5 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 competitive players and esports teams use it to solve "game knowledge is scattered across many sites and finding specific details takes longer than the gameplay itself"?
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. Prove it by indexing a popular game and measuring query time vs.
4. Measure, then decide. Track average answer time and citation accuracy rate. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. First cut for one popular live service game. Build a web app for text queries. Manual review of answers. No voice yet.

After the MVP. Automate voice input, image upload for item identification, and white-label version for game studios

What the build depends on. Large language models with long context windows

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

Test pricing in USD. Hypothesis: 50 dollars per month per team

Message to test

Get instant answers to game mechanics with verified citations

Where to find buyers

Discord communities, gaming forums, esports Discord servers

Lead magnet

Free demonstration for one popular live service game

The first 30 days

1. Week 1: Index the top five most requested live service games.
2. Week 2: Build a simple web app where a user types a query and gets a plain text answer with source links.
3. Week 3: Let a few Discord communities test it.
4. Week 4: Ship within 30 days.

Paid pilot

Prove it by indexing a popular game and measuring query time vs. manual search. Baseline: 5 minutes to find info. Decision: If average answer time is under 10 seconds, proceed.

Success metrics

Average answer time, citation accuracy rate

Retention and expansion

Earn by adding voice input, image upload, and personal libraries

Why clients would pick it

Data licensing is the honest hard part. Some wiki owners will want compensation. Staying current across dozens of live games requires reliable ingestion pipelines and a way to detect when a source quietly updates.

Alternatives and positioning

Scrolling through wikis, watching YouTube videos, reading Reddit threads

Main delivery costs

Data ingestion, server costs for inference, maintenance of pipelines

Limits on daily questions, flagging inappropriate content, preventing spoilers for story modes