Overview
Symphony is an agentic financial layer that enables AI agents to autonomously execute trades across blockchains. Symphony required a best-in-class data backbone to fuel its trading algorithms.
They turned to Chakra.
In mid-2025, we partnered with Symphony to offer real-time, multi-platform web data for enhanced market insights.
We integrated Chakra’s open data platform into Symphony’s stack, emphasizing engineering depth, system architecture, and product use cases.
Symphony’s AI trading agents are empowered with instant access to relevant social and web data, all within secure, scalable infrastructure.
The ROI of the partnership has been clear: Symphony agents now process signals and execute trades in under 500 ms, boosting speed, accuracy, and profitability. Traders capture more alpha from volatile, news-driven moves, while early cohorts show higher engagement and retention as real-time data makes agents more autonomous and useful.
Onboarding & Integration
Kickoff & Snowflake Integration: We began by connecting Chakra’s data platform to Symphony’s Snowflake warehouse instance. Chakra’s toolkit is built for easy integration with external, pre-existing data warehouses (such as Snowflake and Databricks), which allows Symphony to rapidly ingest Chakra’s datasets into its environment. Within days, Symphony began querying fresh web datasets through SQL, thanks to a seamless onboarding process (<60 seconds from sign-up to first query). Early tests validated the data relevance, but also highlighted opportunities to tune performance as query volumes grew.
Performance Tuning & Warehouse Scaling: As Symphony ramped up usage, we collaborated on optimizing query performance. We shortened the data refresh window (from daily batches to near-hourly updates) so that new social data became available faster. We also advised Symphony on scaling their Snowflake warehouse size to handle larger queries and concurrency. By resizing the compute cluster and adjusting query patterns, we significantly reduced heavy query runtimes. For example, searching millions of social media records by keyword – a common operation for Symphony’s agents – became lightning-fast (under 50 ms per million-row scans). These improvements ensured that Symphony’s agents could obtain timely results without lag, even as data volumes increased. With technical validation in place, both teams proceeded confidently toward full deployment.
Contract Activation & Full Deployment: Following a successful pilot, Symphony officially brought Chakra’s platform into their production workflow. The partnership’s terms included committed data volumes and service SLAs, aligning incentives on both sides. With tight turnaround, Symphony’s AI agents were live with real Chakra-fed data streams, marking the start of real-time, data-driven trading workflows. The groundwork was now laid for Symphony to leverage Chakra not just in batch analytics, but in live decision-making.
From Batch Queries to Real-Time Streaming
One of the top developments during this integration was moving from periodic batch queries to real-time data usage. Initially, Symphony’s use of Chakra involved querying static snapshots (daily aggregates of tweets or posts). However, high-frequency trading agents require up-to-the-minute information. To meet this need, we introduced a real-time streaming pipeline into the architecture:
- Kafka Streams for Live Data: Chakra enabled a Kafka-based feed of incoming social data (especially Twitter/X posts) to Symphony. New tweets collected by Chakra’s Scout were published to Kafka topics that Symphony subscribed to, delivering updates within seconds of occurrence. This eliminated the latency of waiting for the next batch refresh.
- Rolling 3-Day Query Tables: To balance performance with data depth, we implemented rolling time-window tables. Symphony’s queries for agent decisions typically only needed the most recent 72 hours of activity. We created rolling tables that always contained the last 3 days of data (for example, tweets, comments, etc.), indexed and optimized for fast filtering. Older data was separately archived. This design kept query scans small and performant, while still allowing agents access to the recent past for trends.
- Real-Time Agent Workflows: With streaming data in place, Symphony developed real-time workflows where Chakra’s data triggers agent actions. For instance, if a sudden surge in mentions of a certain crypto asset occurs on Twitter, that event stream (via Kafka) notifies a trading agent, which then pulls the latest aggregated sentiment from Chakra’s warehouse and decides on a strategy (buy, sell, or alert a human). These autonomous workflows rely on Chakra’s ability to stream billions of rows instantly – no pipelines, no infra overhead. In practice, this meant Symphony’s agents could react to social signals almost as they happen, a critical edge in fast-moving markets.
- Keyword & Topic Filtering: To further refine the streaming setup, we worked with Symphony to define keyword and topic filters. Rather than firehose all tweets or posts, Chakra’s stream was filtered to the domains relevant to Symphony (finance-related keywords, specific stock or token mentions, relevant subreddits). This filtered keyword streaming reduced noise and data volume, focusing the agents on high-signal information. We configured dynamic filter rules so Symphony could tweak which topics the AI agents should listen to in real-time. As a result, the pipeline became both real-time and context-aware, streaming only the information that mattered for trading decisions.
This transition to real-time data was a significant technical milestone. By combining Snowflake’s warehousing (for historical analysis) with Kafka streaming (for live updates) and Chakra’s lightning-fast ingestion engine, Symphony’s platform evolved into a hybrid batch/stream system. The engineering collaboration here showcased Chakra’s flexibility: we seamlessly integrated into Symphony’s existing stack and upgraded it for live data without disrupting their core operations.
Expanding Multi-Platform Data Coverage
Symphony’s strategy involves gleaning insights from across the web, not just one platform. A key reason they chose Chakra was our ability to provide structured datasets from multiple sources in one unified interface. Through Scout (SN0), Chakra has access to the largest collection of web assets on the market - one SQL query away.
By federating all these sources through Chakra, Symphony enjoys a single source of truth for external data. Instead of juggling multiple APIs or scrapers, they rely on Chakra’s unified interface to query Twitter, LinkedIn, Reddit, and more in one place. This multi-platform integration saved significant engineering effort. It demonstrates how Chakra turns the chaotic web into a structured, open database for inference, exactly what Symphony’s next-gen trading platform needed.
Ongoing Collaboration & Partnership
The Chakra–Symphony relationship has been far more than a typical vendor-client arrangement. From day one, we adopted a partnership mindset, working closely to ensure Symphony’s success. Some key collaboration efforts include:
- Dedicated Technical Support: Our engineering and data teams provided white-glove support to Symphony throughout onboarding and beyond. We set up a shared channel for real-time communication, troubleshooting queries, and pipeline issues as they arose. When Symphony’s developers encountered edge cases (for example, a particularly complex SQL query or a need to debug data anomalies), Chakra engineers were on call to assist in debugging and optimization. This high-touch support accelerated problem resolution and built trust between teams.
- API & Schema Evolution: Symphony’s use cases drove meaningful enhancements in Chakra’s product. As they expanded into new data types, they gave feedback on our API endpoints and data schema. We took this feedback to heart – for instance, adding new fields to the Twitter dataset (such as more granular timestamp metadata and sentiment scores) to support Symphony’s analytics. We also extended our REST API with endpoints for on-demand stream resets and custom filtering at Symphony’s request. Symphony became a design partner, helping us evolve the API and data models to be more powerful and flexible. These improvements now benefit all Chakra users, underscoring how collaborative iteration strengthened the platform.
Throughout these efforts, Chakra has acted not just as a data provider but as an extension of Symphony’s team. By aligning our engineering roadmaps and business goals, we’ve forged a partnership that accelerates innovation for both companies. This collaboration is a testament to Chakra’s commitment to customer success and our ability to scale with a client’s evolving needs.
Outcomes and ROI
The integration of Chakra’s real-time data streams into Symphony’s agentic financial layer produced clear outcomes in both agent performance and user experience. By shifting from static batch data to live Kafka feeds, AI agents have utilized this data to go from passive signal responders to proactive market participants.
Real-Time Market Awareness
Agents now have direct access to real-time social and macroeconomic signals. Instead of waiting for lagging updates, they ingest streaming datasets from Twitter/X and other sources. This enables them to detect market-moving events instantly. Whether a breaking news headline, a regulatory update, or a surge in mentions of a specific asset.
Proactive Agent Behavior
The deployment of real-time Kafka feeds allowed Symphony’s agents to act with greater autonomy. They no longer rely solely on human prompts or scheduled queries; instead, they can be configured to initiate trades or send user alerts the moment a high-confidence signal occurs. This proactive capability shifts agents from reactive tools into independent market actors, capable of defending positions, hedging risk, or seizing opportunities at machine speed.
User Empowerment Through Notifications
Symphony integrated real-time alerting into the user experience. Traders and builders can choose to receive instant notifications when a relevant signal is detected or delegate execution entirely to the agent. This flexibility empowers different trading profiles: discretionary traders benefit from actionable alerts, while algorithmic or hands-off traders can rely on fully autonomous execution.
Expanded Use Cases
The shift to streaming architecture dramatically broadened Symphony’s use cases. Beyond traditional sentiment analysis, agents now leverage live feeds for:
- Macro-event monitoring (e.g., Fed announcements, geopolitical developments).
- Social-driven volatility detection (e.g., trending tokens, coordinated influencer chatter).
- Cross-asset correlation tracking (e.g., equity, FX, and crypto news moving in sync).
This versatility positions Symphony as a platform not only for crypto-native strategies but also for global, multi-market macro plays.
ROI and Measurable Impact
The ROI has been evident in measurable trading outcomes:
- Speed Advantage: Agents now process signals and execute trades in seconds, far outperforming manual workflows or delayed data pipelines. Average time from signal detection to trade execution fell from minutes to sub-second latency (<500 ms).
- Higher Accuracy: Access to real-time social and news data improved the contextual awareness of agents, leading to better entry and exit points during volatile events.
- Profitability Gains: In practice, traders using Symphony agents have been able to capture more alpha from news-driven market swings, outperforming equivalent strategies running on delayed or batch datasets.
- Retention & Engagement: Early cohorts of Symphony users show increased engagement with agent workflows when empowered by real-time data, validating that the product-market fit strengthens as agents become more autonomous and useful.
In summary, the partnership transformed Symphony’s agents from fast followers into first movers. By combining Chakra’s real-time ingestion and Kafka streaming with Symphony’s cross-chain agentic execution rails, the collaboration delivered not just incremental improvements but a step-change in agent capability, leading to tangible ROI for traders and reinforcing Symphony’s vision of agentic finance.
