Building Synapse: A Multi-Model Architecture for Domain-Specific AI Systems

How we built a production system that orchestrates specialized models, frontier LLMs, and human experts for marketing workflows

The Problem with One-Size-Fits-All AI

When we started building Averi, we ran into the same wall every AI application team hits: generic foundation models are incredible at broad reasoning but struggle with domain-specific consistency and judgment.

In marketing specifically, we needed systems that could:

• Maintain brand voice across hundreds of assets
• Know when a task needs 30 seconds of generation vs. 30 minutes of strategic thinking
• Understand when human expertise is actually required (and route to it seamlessly)

After months of over-prompting GPT-4 and building increasingly complex RAG systems, we realized we needed a fundamentally different approach: a multi-model orchestration system that treats different AI capabilities—and humans—as specialized components in a larger cognitive architecture.

That system is Synapse.

Architecture Overview: Cortices as Cognitive Modules

Rather than building one massive model, we designed Synapse around five specialized "cortices"—each handling a distinct cognitive function:

User Input → Brief Cortex → Strategic Cortex → Creative Cortex → Performance Cortex
                                    ↓
                            Human Cortex (when needed)

Brief Cortex: Parses ambiguous natural language into structured marketing intent Strategic Cortex: Maps objectives to campaign frameworks using domain knowledge Creative Cortex: Generates brand-aligned content across formats and channels Performance Cortex: Injects historical data and optimization insights Human Cortex: Routes to vetted experts for high-stakes or nuanced work

Each cortex can operate independently or in sequence, with Synapse's router determining the optimal path based on task complexity and requirements.

AGM-2: Our Marketing-Specialized Foundation Model

At the core of the system is AGM-2, a 13B parameter model trained exclusively on marketing data:

Training Corpus: ~2M documents including:

• Brand positioning frameworks and messaging architectures
• Conversion-tested ad copy across channels (Meta, Google, LinkedIn)
• Email sequences with performance data
• Landing page copy with A/B test results
• Content calendars and campaign planning documents

Key Technical Decisions:

• Medium scale by design: 13B parameters for faster inference while maintaining quality
• Domain-specific tokenization: Custom vocabulary for marketing terminology
• Performance-weighted training: Higher weight on content with proven conversion data
• Multi-format training: Native understanding of emails, ads, social posts, long-form content

Unlike general models that treat all text equally, AGM-2 understands the functional role of marketing content—it knows the difference between awareness-stage blog content and bottom-funnel sales copy.

Adaptive Reasoning: Dynamic Cognitive Depth

One of our biggest breakthroughs was solving the "cognitive effort" problem. Most AI systems apply the same processing power whether you're asking for a headline rewrite or a complete GTM strategy.

Synapse's Adaptive Reasoning evaluates every input across two dimensions:

1. Complexity Assessment Engine

• LLM-based classifier: Trained to recognize task complexity patterns
• Heuristic scoring: Keyword analysis, input length, ambiguity markers
• Historical similarity: Comparison against previous successful task patterns

2. Reasoning Depth Stratification

Based on complexity assessment, tasks route to one of three processing tiers:

Express Mode (< 2 seconds):

• Template-based responses
• Simple rewrites and formatting
• Factual queries with clear answers

Standard Mode (5-15 seconds):

• Structured content generation
• Campaign planning with templates
• Performance optimization suggestions

Deep Mode (30+ seconds):

• Multi-step strategic reasoning
• Cross-channel campaign orchestration
• Human expert activation when needed

This approach reduces computational waste on simple tasks while ensuring complex work gets appropriate cognitive resources.

The Human Cortex: Programmatic Expert Integration

Perhaps the most controversial aspect of Synapse is the Human Cortex—our belief that production AI systems need seamless human integration, not just human oversight.

Expert Matching Algorithm:

def route_to_human(task_complexity, domain_expertise_required, urgency, budget):
    if task_complexity > DEEP_THRESHOLD:
        expert_pool = filter_experts(domain=domain_expertise_required)
        return match_expert(expert_pool, availability=urgency, cost=budget)
    return None

When Human Routing Activates:

• Brand messaging for new product launches
• Crisis communication planning
• Complex multi-channel attribution analysis
• Creative concepts requiring cultural nuance

The system tracks collaboration patterns to improve future routing—learning which expert types work best for specific task categories and user preferences.

Production Performance & Learnings

Metrics After 6 Months:

• Response quality: 23% improvement in brand consistency scores vs. GPT-4 baseline
• Speed: 72-hour average for complex multi-asset campaigns (vs. 2-3 weeks traditional)
• Cost efficiency: 67% reduction in human expert hours through smart routing
• User satisfaction: 4.8/5 average rating for multi-cortex outputs

Key Technical Learnings:

1. Domain-specific training data quality matters more than quantity

   • 2M high-quality marketing docs outperformed 10M+ general web content
   • Performance-annotated training data significantly improved output relevance

2. Multi-model routing reduces single points of failure

   • When AGM-2 struggles with creative tasks, GPT-4 handles them well
   • When frontier models hallucinate marketing metrics, AGM-2's domain knowledge corrects

3. Adaptive reasoning prevents over-engineering

   • 73% of tasks complete successfully in Express mode
   • Deep mode usage correlates strongly with user satisfaction on complex tasks

4. Human integration requires architectural planning from day one

   • Bolt-on human review doesn't work—integration must be native to the system
   • Expert feedback loops significantly improve model performance over time

Technical Challenges & Solutions

Memory Architecture

Challenge: Context windows limit long-term brand consistency Solution: Hierarchical memory system (short-term/long-term/archival) that functions like OS memory management

Model Coordination

Challenge: Preventing conflicts between different models in the pipeline Solution: Structured handoff protocols with explicit context passing between cortices

Quality Assurance

Challenge: Maintaining output quality across multiple models and humans Solution: Automated quality scoring + expert review triggers for high-stakes content

Latency Optimization

Challenge: Multi-model routing increases response time Solution: Parallel processing where possible + intelligent caching of common patterns

Open Questions & Future Directions

For the Research Community:

1. Optimal architectures for domain-specific orchestration: Is our cortex-based approach generalizable?
2. Human-AI collaboration at scale: How do you maintain quality with 100+ experts in the loop?
3. Multi-model evaluation: Standard benchmarks don't capture orchestration quality—what should we measure?

Technical Roadmap:

• Cortex specialization: Training domain-specific routing models for each cortex
• Federated learning: Enabling AGM-2 to learn from expert feedback without compromising privacy
• Real-time adaptation: Dynamic model weights based on user success patterns

Resources

While Synapse itself is proprietary, we're open-sourcing some components:

• Adaptive Reasoning classifier: Training scripts and model weights
• Multi-model routing framework: Basic orchestration patterns
• Marketing evaluation benchmarks: Domain-specific test suites

Learn More: [https://www.averi.ai/blog/introducing-synapse-from-averi]

Try Synapse: [https://www.averi.ai/demo-sign-up]

Conclusion

Synapse represents our thesis that the future of AI applications isn't about building bigger foundation models—it's about building smarter orchestration systems that combine specialized models, frontier capabilities, and human expertise.

For domain-specific applications, we believe hybrid architectures that treat humans as first-class cognitive components will outperform pure AI systems for the foreseeable future.

The question isn't whether AI will replace human expertise, but how we can build systems that amplify the best of both.

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What are your thoughts on multi-model orchestration? Are you working on similar domain-specific architectures? We'd love to connect with other teams pushing the boundaries of production AI systems.

- Zach Chmael, Head of Marketing @ Averi