Synapse – Multi-model AI combining LLMs and humans for marketing output

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Synapse – Multi-model AI combining LLMs and humans for marketing output

Hey HN, 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!