Show HN: Synapse – Multi-model AI combining LLMs and humans for marketing output
by zchmael | 2025-07-31 16:12:49+00:00
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!