Fluig is an AI drawing tool that can quickly convert text, documents or codes into a variety of professional diagrams such as mind maps, flow charts, fishbone diagrams, etc. It supports multi-format input and intelligent format conversion.

Fluig

Fluig is positioned as a zero-code AI workflow automation platform. Its core value is that it allows non-technical users to build complex automated processes that include AI capabilities through drag and drop. Unlike traditional automation platforms (such as Zapier, Make), Fluig directly encapsulates AI capabilities (text generation, image recognition, data classification, etc.) into draggable process nodes, and users do not need to write API call code or configure model parameters.

Core parameters and statistics

Parameter item	Value
Product positioning	Zero-code AI workflow automation platform
Delivery form	Web/SaaS
Online time	Q2 2026
Support Platform	Web
Language coverage	Chinese, English
Number of preset nodes	50+ (AI node + data connector + logical node)
AI node types	Text generation, image recognition, data classification, sentiment analysis, summary extraction, translation
Data connector	Google Sheets, Airtable, Notion, Feishu, DingTalk, Slack, Enterprise WeChat
Trigger method	Timing (cron), Webhook, manual trigger
Maximum number of nodes for a single workflow	30
Execution log retention	30 days
Failed retry	Supports configurable retry strategy (number of times + interval)

The core difference between Fluig and Zapier/Make lies in the "AI first" product design - the latter two are essentially application connectors, and AI capabilities need to be embedded in custom code or third-party API calls; Fluig treats AI capabilities as first-class citizen nodes, and users can drag out a "Text Generation" or "Image Classification" node from the panel to complete function access.

User and market recognition

After Fluig went online, the number of monthly active workflows increased from less than 1,000 in the first month to more than 8,000 in the third month. The growth curve shows that the product's spread effect among the target user group is taking shape. About 40% of natural user discovery channels come from community template sharing - new users make personalized modifications starting from verified templates, reducing the cost of learning from scratch.

User research (based on 200 active users) shows the distribution of usage scenarios: 55% data processing pipeline (regular extraction of data from Sheets/Airtable → AI classification and cleaning → writing to database), 30% content distribution automation (automatic synchronization to multiple platforms after articles/images are generated), and 15% approval and notification process. In the user NPS survey (sample size 80), 70% gave it a score of 8 or above (on a 10-point scale), and positive feedback focused on "You can build an end-to-end AI process without programming" and "The template market lowers the entry barrier." Negative feedback focused on "insufficient flexibility in advanced node configuration" and "limited data connector coverage."

Cost advantage

Cost Dimension	Description
Free version	$0/month, 500 executions per month, up to 3 active workflows, all 50+ nodes available
Professional Edition	$19/month, 5,000 monthly executions, unlimited workflows, advanced AI nodes, Webhook triggering, execution alarms
Enterprise Edition	$79/month, unlimited executions, private deployment options, dedicated support, SSO integration

Price comparison with competing products: Zapier Starter plan $19.99/month only supports 2,000 tasks/month, 5 active workflows, and AI capabilities need to be integrated by themselves through Webhook + OpenAI API - users need to pay additional AI model API fees. The Fluig Professional Edition provides 5,000 executions and built-in AI nodes at the same monthly fee (without paying additional model calling fees), giving it an even better TCO advantage in AI process-intensive scenarios.

Hidden cost: The underlying model call fee of the AI ​​node is included in the Fluig subscription fee - users do not need to be billed additionally by token or API call count, but the bring-your-own-model (BYOM) model is still on the roadmap.

Main functions

• Visual Workflow Editor: Drag-and-drop design interface, independent node configuration parameters and independent testing, built-in real-time syntax checking. Canvas supports infinite scaling and automatic layout.
• AI node library (50+ preset nodes): text generation, image recognition, data classification, sentiment analysis, summary extraction, translation, etc. Each AI node supports input mapping and output parsing. The cost of calling the AI ​​node model is included in the subscription fee.
• Data Connector: Two-way data reading and writing for Google Sheets, Airtable, Notion, Feishu, DingTalk, Slack, Enterprise WeChat, etc. OAuth authentication information is stored encrypted.
• Conditional branches and loops: Conditional branches dynamically select paths based on the output results of previous nodes; loop nodes support processing array elements one by one or continuous iteration based on conditions.
• Execution Monitoring and Alarm: View the execution path of each workflow instance in real time. If it fails, it will automatically retry according to the configured retry strategy. If the retry fails, the specified channel will be notified. Logs are retained for 30 days.
• Version Management and Rollback: Automatically generate a version snapshot every time you edit and save, supporting rollback and difference comparison.

Model and version evolution

Version	Release Date	Major Changes
v0.9 Internal Beta	2026-06	Basic visual editor, 20 nodes, only timed triggering, single workflow 15 node limit
v1.0 public beta	2026-07	Node library expanded to 50+ (including 20+ AI nodes), conditional branches and loops, version management, Webhook triggering, execution alarms, 30 node limit
v1.5 (Roadmap)	2026-Q3	Template market, cross-workspace collaboration, custom AI nodes (access to self-selected models)
v2.0 (Roadmap)	2026-Q4	Mobile management application, automatic error repair suggestions, parallel execution optimization

Technical advantages

• DAG Scheduling Engine: Adopts the directed acyclic graph (DAG) scheduling model, supports node-level concurrent execution and dependency management, and single-node sandbox isolation ensures that failures do not affect other nodes.
• Built-in AI inference optimization: The bottom layer of the AI ​​node calls an optimized inference interface, supporting request-level timeout control and automatic retry. Batch data scenarios are automatically fragmented and processed in parallel.
• OAuth Credentials Encrypted Storage: All third-party connector authentication credentials are stored using AES-256 encryption, so users do not need to see the original credentials when editing workflows.
• Declarative configuration interface: Nodes use declarative form configuration instead of scripted configuration, which eliminates the possibility of syntax errors and makes workflow maintenance and handover easier.

How to use

Entrance	Applicable scenarios	Function description
Web console	Workflow design and configuration management	Full-featured access: editor, monitoring, logs, version management
Template Market (under planning)	Quick Start	Customize after importing from community shared templates
Mobile App (under planning)	Viewing and alarming	Execution status viewing and alarm notification reception

Typical operation process: Register and log in → Enter the workflow editor → Build in the order of "Trigger → Data Input → Processing Node → AI Node → Data Output → Notification" → Configure parameters → Save and publish → Monitor execution.

Product Pricing

Package	Price	Core Benefits
Free version	$0/month	500 executions per month, up to 3 active workflows, all nodes available, basic monitoring
Professional Edition	$19/month	5,000 monthly executions, unlimited active workflows, advanced AI nodes, Webhook triggering, execution alarms
Enterprise Edition	$79/month	Unlimited executions, private deployment options, dedicated support, SSO integration, custom AI nodes

Pay annually and enjoy approximately 15% discount. Newly registered users during the public beta period will automatically receive the first month of professional version experience.

Application scenarios

• Scheduled data collection and AI processing pipeline: Read sales data from Google Sheets at scheduled times every day → AI classification tags customers → Write back to Airtable → Send summary reports to the enterprise WeChat group. Verification method: Compare the data processing time and error rate before and after automation.
• Multi-platform content synchronization distribution: Webhook trigger → Extract articles from Notion → Translation node generates multi-lingual versions → Synchronize to multiple platforms → Notify the person in charge. Verification method: Compare the coverage timeliness and consistency of manual distribution.
• AI-assisted work order classification and distribution: Feishu feedback incoming → sentiment analysis + keyword matching classification → assigned to the corresponding processing team → high-priority expedited notification. Verification method: work order classification accuracy and distribution timeliness.
• Regular report generation and push: Pull indicators from GA, database and advertising platform → AI summary generates analysis text → Render report images → Push to management. Verification method: Report data accuracy and format consistency.

Applicable people

• Internet Operations and Product Team: There are a large number of repetitive data processing and content distribution tasks on a daily basis. Operations personnel are required to have the ability to sort out SOPs.
• Small and medium-sized enterprises lacking development resources: For enterprises with 10-50 people, operation or administrative staff can independently complete the construction of common scenarios after 3-5 hours of training.
• Business personnel in digital transformation: Build and modify processes independently without relying on IT department scheduling.
• Technology Enthusiasts and Independent Developers: Quickly verify the effect of combining AI with business and build MVP-level automation prototypes.

Unsuitable scenarios: require deeply customized AI behavior (fine-tuned models in specific areas, customized reasoning pipelines) - the current preset nodes are not flexible enough; complex integration scenarios spanning more than 5 different systems - connector coverage may be insufficient.

Summary and Outlook

Fluig uses a zero-code visual editor + a deeply integrated AI node library to lower the entry barrier for automation from "being able to program" to "being able to sort out logic." Taking the typical process of data collection + AI classification + multi-channel distribution as an example, Fluig compresses the human investment in each execution cycle from 30-60 minutes to zero (only the first configuration is required).

Current limitations: (1) The underlying model of the AI ​​node is not optional - users can only use the platform's pre-set AI capabilities, and BYOM functionality is still on the roadmap; (2) Enterprise-level system (SAP, Oracle, Salesforce) connectors are not yet built-in; (3) Parallel execution capabilities may cause node queuing delays under high load conditions; (4) The product has not yet disclosed SOC2, GDPR and other compliance certification status. Purchasing/Adoption Suggestions: New users start with the free version, first select the most urgent repetitive manual process to build a prototype, and run it for 1-2 weeks to verify stability. Test the professional version with monthly payment for 2-3 months before paying annually. Enterprise customers need to confirm the data storage location and AI node model data training strategy.

Related tools: CrewAI, langchain

Version Info

• Public beta version ：Fluig, function updates are subject to official website announcements. （2026-07-14）
• earlier version ：Initial version, core functions are subject to release notes. （2026-07-14）