River AI is the intelligence engine behind our services. Designed specifically for legal workflows, it merges traditional logic with live data pulled from verified sources—including case law, public filings, and social data networks.

River AI is built on a carefully selected hybrid of open-source and proprietary technologies, optimized for legal application and real-time data parsing.

  • Llama 4 (Maverick & Scout) developed by Meta, designed to understand and generate text, images, and video data, and often available as open source for researchers and developers to use.

  • Super Grok (Enterprise), providing foundational language modeling tuned for regulatory and compliance-based contexts.

  • Pinecone for vector-based memory and retrieval—ensuring continuity across sessions and fast access to past queries or legal references.

  • GitHub for version control and collaborative legal document workflows.

  • Eliza, powerful web3 framework designed to create, deploy, and manage autonomous AI agents performing blockchain metric tasks, tracing, and engagement with audiences on connecting social web2 & web3 platforms.

  • Meta MusicGen, optionally available for generative sound design in legal content—ideal for accessibility tools, training modules, or immersive case presentations.

River, a unique AI model, is meticulously crafted using human data directly sourced from real-world user engagement. It is publicly sorted in a natural order and meticulously filtered and compounded from vast data lakes into highly specialized, ready-to-use AI formats.

River’s Legal AI is trained on a vast dataset of:

  • 36 district court dockets

  • 9 legal company dockets

  • APIs from Fed-PACER

  • Regulations.gov

  • Court Listener

  • FOIA, EUR-Lex, & SEC-Edgar

Crystal Clear
Push for open source and decentralization.

There’s excitement about 11River’s potential to become an AI Layer 1 that is open-sourced and decentralized. But there’s also recognition that regulatory scrutiny requires careful consideration.

Position River as a Layer 1 for AI in the justice system. Implement open-source development with decentralized node incentives to publicly display every non-sealed action.


Open Source and Decentralized Example; When River modifies a legal notice social media targeting custom audience, these adjustments will be visible to ensure transparent actions.

class River11Agent:
    def __init__(self, fb_ad_manager):
        self.fb_ad_manager = fb_ad_manager  # Facebook Ads API connection
        self.agent_id = "river_ad_001"     # Decentralized agent identifier        

    async def optimize_ad_targeting(self, ad_set_id):
        # Get current ad set configuration
        ad_set = await self.fb_ad_manager.get_ad_set(ad_set_id)        

        # Define new targeting parameters
        updated_targeting = {
            'age_min': 37,              # Narrowed from 35
            'age_max': 42,              # Narrowed from 50
            'genders': [2],             # 2 = Female only (Facebook API value)
            'geo_locations': ad_set['geo_locations'],  # Keep existing locations
            'custom_audiences': await self._build_priority_audience()
        }        

        # Update ad set with new targeting
        await self.fb_ad_manager.update_ad_set(
            ad_set_id=ad_set_id,
            targeting=updated_targeting
        )        
        # Log update in decentralized network
        await self._broadcast_update(ad_set_id, updated_targeting)        

  async def _build_priority_audience(self):
        # Fetch audience data from Facebook API
        audience_data = await self.fb_ad_manager.get_audience_insights(
            filters={
                'age_range': {'min': 37, 'max': 42},
                'gender': 'female'
            }
        )

        # Sort audience by follower/friend count
        prioritized_audience = sorted(
            audience_data['users'],
            key=lambda x: x.get('follower_count', 0) + x.get('friend_count', 0),
            reverse=True  # Highest first
        )
       
        # Create custom audience segments based on priority
        audience_segments = {
            'high_priority': prioritized_audience[:10000],  # Top 10k users
            'medium_priority': prioritized_audience[10000:50000],
            'low_priority': prioritized_audience[50000:]
        }

        # Generate custom audience IDs for each segment
        custom_audience_ids = {}
        for priority, users in audience_segments.items():
            custom_audience_ids[priority] = await self.fb_ad_manager.create_custom_audience(
                name=f"river_priority_{priority}_{self.agent_id}",
                users=users,
                priority_weight=self._get_priority_weight(priority)
            )  

        return custom_audience_ids

    def _get_priority_weight(self, priority_level):
        """Assign weights for ad serving priority"""
        weights = {
            'high_priority': 0.6,
            'medium_priority': 0.3,
            'low_priority': 0.1
        }
        return weights.get(priority_level, 0.1)

    async def _broadcast_update(self, ad_set_id, targeting):
        """Broadcast update to decentralized ElizaOS network"""
        update_message = {
            'agent_id': self.agent_id,
            'ad_set_id': ad_set_id,
            'targeting_update': targeting,
            'timestamp': datetime.utcnow().isoformat()
        }
        # Assuming a decentralized broadcast method exists
        await self.network.broadcast(update_message)

# Usage example
async def main():
    fb_manager = FacebookAdManager(api_credentials)
    eliza_agent = River11Agent(fb_manager)
    await river_agent.optimize_ad_targeting("ad_set_12345")

RDE

Request to join the River Developer Experience program to gain hands-on experience with River’s legal AI services and embark on your journey of developing legal AI applications with social data integrations. Receive support from our experts, access River sandbox playgrounds, architecture guides, APIs, developer documentation, and reference implementations.

Ethic

Leverage 11River’s human experts, serving as strategic guardians, monitor AI actions to ensure compliance with brand standards, network rules, and local government regulations. Innovative guardrails define the actions LLMs and AI can automatically take and the areas where human approval is necessary. Daily, step-by-step audit reporting is conducted on every network flag, target adjustment, demographic update, and engagement sentiment score.

Secure

11River provides secure and accountable infrastructure to maximize the utility of public or provided data. processed in accordance with the client standard, local regulation, and industry privacy norms that govern the data in use.

Lite Operation

River AI seamlessly integrates into your existing operations, whether you’re a law firm, an in-house legal team, or a compliance unit. Our system is accessible through any secure web browser, eliminating the need for installations or complex setups. Access legal research, declaration tools, and real-time insights directly from your team’s custom portal, ensuring speed, privacy, and clarity at scale.

“Generative AI is significantly enhancing our careers by at least one to two years, making it an appealing choice for new junior members. Firms and organizations that fail to leverage AI will be at a disadvantage in the upcoming years, losing out on pricing wars.”

Chief Legal Officer, Christina Durvis