Show HN: Synapse – Multi-model AI combining LLMs and humans for marketing output
I’m Zack, CEO at Averi AI, and we just released Synapse, a modular AI architecture we built to solve a problem we kept running into within the marketing ecosystem:
“How do you get domain-specific intelligence without trying to recreate GPT-4 from scratch?”
The Problem
Most domain-specific AI tools (marketing, legal, ops, etc.) tend to fall into one of three camps: Use GPT-4/Claude as-is and rely on prompt engineering
Train a small model from scratch but lose general reasoning
Go full frontier model… and burn millions trying
We’ve considered all three. None hit the mark.
Our Approach: Multi-Model + Human Routing
Synapse is our attempt at something better: A routing architecture that matches tasks with the best resource whether that’s an LLM, a smaller domain model, or a vetted human expert
A way to balance specialization and scale, instead of choosing one
It powers our own domain-specific foundation model (AGM-2), and integrates GPT-4, Claude, and others alongside it. Tasks get routed based on complexity and type.
For example: A quick product description → routed to AGM-2
A cross-channel campaign brief → goes through Strategic Cortex + GPT-4
A nuanced brand tone rewrite → routed to a human expert
Under the Hood
Architecture: Synapse is structured around 5 specialized cognitive modules (we call them cortices): Brief Cortex: Disambiguates messy requests
Strategic Cortex: Maps business goals to tactical plans
Creative Cortex: Writes content tuned to brand voice
Performance Cortex: Weighs historical campaign data
Human Cortex: Escalates to our expert network when needed
Routing Logic:
Dual-track complexity scoring: LLM + heuristic analysis
Tasks run in one of 3 “modes”: Express (quick), Standard, or Deep (multi-stage, may call a human)
Results fed back to improve future routing decisions
Training Data:
AGM-2 was trained on over ~2M marketing artifacts (positioning docs, campaigns, A/B test data, etc.) We licensed real performance data and layered in structured messaging frameworks. It’s not the biggest model, but it’s trained with domain-native intent.
What Makes This Different
Rather than trying to force one model to do everything, Synapse behaves more like a strategist. It knows when to go fast, when to go deep, and when to ask for help.
We’ve been running it in production for 3+ months.
It’s shown strong gains in:
Brand tone consistency vs. GPT-4-only setups
Time-to-launch on full campaigns
Quality of briefs when humans are looped in
Try It + Read More
Demo (mention you're from HN and we'll get you right in): https://www.averi.ai/demo-sign-up
Technical overview: https://www.averi.ai/blog/averi-launches-synapse-a-new-ai-sy...
Open Questions We’re Exploring
Specialist vs. generalist tradeoffs — When does our domain-trained AGM-2 outperform GPT-4? When doesn’t it?
Human-in-the-loop scaling — How do you decide when to escalate to a human? We use ML for this but would love to hear other approaches.
Training data — What’s the right mix of public vs. proprietary when building domain-specific datasets?
Would love feedback from anyone building domain AI systems, orchestration layers, or multi-agent workflows. AMA on routing logic, model behavior, or anything else.
Thanks!