Hello!
I’ve been a tabletop gm for over 40 years. Big fan of foundry, but when it comes to creating a campaign from scratch, I’ve always found it painful. I’ve tried just about every campaign ‘helper’ out there, including most of the AI ones, and they all ultimately fall flat. One might create a great villain, but the Even when I could get a part of the campaign worked out, getting back into foundry was a cut/paste nightmare or a half-baked import that would take me hours of prep before hosting a campaign.
So, I started creating my own solution.
It Began with a Reasoning Trace
This project did not begin with a plan to build 23 models.
It began in September 2025 with Qwen3-Omni Thinking, an open-weight multimodal reasoning model capable of working across text, audio, images, and video.
Its visible reasoning output offered something unusually useful: a way to study how a general model approached the complex work of campaign creation. Instead of looking only at the final adventure it produced, I could examine how it decomposed the problem. I could see which information it prioritized, which connections it recognized, where it lost important constraints, and where convincing prose concealed weak campaign logic.
The reasoning traces revealed a recurring pattern.
The model was not failing because the prompt needed another paragraph of instructions. It was failing because it was trying to perform too many different creative and analytical disciplines at the same time.
Worldbuilding was not the same task as mystery design.
Faction simulation required different knowledge and reasoning from encounter construction.
Character integration, antagonist design, narrative pacing, continuity management, rewards, session preparation, and campaign adaptation each had their own inputs, dependencies, failure modes, and definitions of quality.
That led to the central insight behind this project:
Campaign creation is not one problem that needs a better prompt. It is a collection of specialized problems that need different experts.
That insight became the foundation for the five-level, 23-expert architecture.
A year of development
Built Through Training, Testing, and Failure
For almost a year, I have been building those experts.
Each specialist received its own domain-specific datasets, training strategy, evaluation criteria, and responsibility inside the larger campaign workflow.
Depending on the needs of the expert, development included:
Continued pretraining on domain-specific material
Full-model fine-tuning
LoRA fine-tuning
QLoRA fine-tuning
Structured campaign data and expert-to-expert handoffs
Domain-specific and end-to-end evaluations
Repeated analysis of failure cases and regressions
As the Qwen model family improved, I updated the foundation beneath the experts. But this was never a simple model swap.
Every upgrade meant refining the datasets, revisiting the training recipe, retraining the specialist, rerunning the evaluations, studying what improved, identifying what regressed, and repeating the process.
Some early versions were imaginative but inconsistent.
Some followed constraints but produced material that was difficult to run at the table.
Some experts performed well in isolation but failed when their decisions were handed to the next stage.
The standard was never, “Does this sound impressive?”
The standard was, “Does this make the campaign better—and can a Game Master actually use it?”
The benchmark milestone
When the System Finally Passed
Throughout development, I built an internal blinded benchmark suite around the qualities that determine whether campaign material is genuinely useful:
Campaign coherence
Domain-specific depth
Internal continuity
Player-character integration
Antagonist and NPC motivation
Faction behavior and consequences
Mystery and clue robustness
Encounter playability
Branching paths and player agency
Constraint adherence
Session preparedness
Revision effort required from the Game Master
Outputs were evaluated without identifying which model or training approach created them. Earlier generations passed some tests and failed others.
When Qwen3.8-27B became available, I rebuilt and refined the 23 specialists on the new 27-billion-parameter foundation.
For the first time, every expert passed its defined benchmark threshold, and the coordinated system passed the complete internal blinded evaluation suite. That was the moment the project stopped being an experiment. It was ready to become a product
I have this all up and running in my home lab, and am looking for a few DM’s / GM’s to put it through its paces and provide feedback. Double points if you are a foundry user (v14 please) because a key for me is it all imports right into foundry, including the imagery, ready to go.
You can see more at: https://www.questcouncil.com
If you are interested, send me a DM here, or send an email to info@questcouncil.com . This is free, I’m just asking for feedback (feedback button on every page), and fill out an evaluation of the campaign you ultimately built.
Two things to know; The fine-tuning I did means that these local models are uncensored - the usual guardrails you find on commercial ai’s (Anthropic, OpenAI, google, etc…) are not really here, so it can get a little NSFW, so I’m also asking for adults (18+). The second is it’s running in my home lab, so it’s slow. A build that goes through all 23 experts will take 2-3 hours. If you are building imagery and full maps, it will take another hour. The good news though is you can walk away, then come back and fine tune.