Why Use Flowent
The Problem: Fragmentation Fatigue
Consider a developer building an AI agent to analyze Ethereum’s market trends. Without Flowent, their workflow might look like this:
Fetch ETH/USD price from DexScreener’s API.
Pull large transactions (>$1M) from Etherscan.
Scrape Twitter for sentiment using the Twitter API.
Manually align timestamps and resolve conflicts (e.g., a price spike on DexScreener that doesn’t match Coinbase’s data).
Aggregate results into a usable format for the AI model.
This process isn’t just time-consuming—it’s fraught with pain points:
Inconsistent Schemas: DexScreener returns prices as
{ pair: "ETH/USD", price: 3400 }, while Coinbase uses{ symbol: "ETH-USD", last: "3400.50" }.Rate Limits: Twitter’s API allows only 500 tweets/month on free tiers, while Etherscan caps at 5 requests/sec.
Hidden Costs: Each API call adds up. For example, a trading bot making 10,000 daily requests across 3 APIs could cost $500+/month.
Flowent’s Solution: One Integration to Rule Them All
Flowent replaces this chaos with a single, unified API that abstracts away the complexity of 50+ data sources. Instead of wrestling with multiple integrations, developers send a natural language query like “ETH price with whale transactions and social sentiment” and receive a structured, AI-summarized response in seconds.
Cost Efficiency, Quantified Let’s break down the savings, below is the estimate cost:
ETH price + sentiment
$0.12 (3 APIs)
$0.04
NFT floor price analysis
$0.08 (2 APIs)
$0.03
DeFi liquidity check
$0.15 (4 APIs)
$0.05
How? Flowent’s AI optimizes queries across sources—for example, fetching prices from the most cost-effective exchange (e.g., Binance instead of Coinbase Pro) and caching frequently accessed data.
Key Features, Explained
Context-Aware Routing
For traders: Prioritize low-latency sources like Binance for prices and CoinMetrics for real-time on-chain data.
For researchers: Blend Nansen’s institutional insights with Messari’s reports.
Example: A query for “institutional ETH accumulation” automatically prioritizes Nansen and Glassnode over retail-focused platforms.
Semantic Caching with Deepseek
Flowent uses Deepseek’s embeddings to detect similar queries. For instance:
Query 1: “What’s Bitcoin’s price?” → Embedding vector
[0.76, -0.12, ...]Query 2: “BTC current value?” → Embedding vector
[0.74, -0.11, ...]
If the cosine similarity exceeds 95%, Flowent serves the cached response, slashing costs and latency.
Credit-Based Billing
1 credit = 1 data point (e.g., a price, a transaction hash, a sentiment score).
Staking $FLWT increases your tier:
Free Tier: 1,000 credits/day (500 $FLWT staked).
Pro Tier: 50,000 credits/day (5,000 $FLWT staked) + priority support.
Why Developers Love Flowent
Speed: Reduce integration time from weeks to hours.
Simplicity: No more parsing conflicting JSON schemas.
Scalability: Handle 10x more queries without hitting rate limits.
Real-World Impact
We internally tested this tool for an indices summary dashboard development and manage to improve development time by 68% by leveraging unified social + on-chain data.
Last updated