Tisac Lightning
Alternatives
0 PH launches analyzed!
Tisac Lightning
Enterprise AI Development Platform
13
Problem
Enterprises face challenges in setting up AI development environments for large teams, including hardware and software configuration.
Lack of control and customization over AI development environments for enterprises.
Solution
An AI development platform streamlining setup and deployment on any cloud for enterprises.
Enables training and deployment of AI models with more control and customization for large teams.
Customers
Enterprises needing to establish AI development environments for large teams.
AI developers and data scientists in enterprise settings.
Alternatives
Unique Features
Provides control and customization over AI development environments.
Streamlines the setup of AI development environments for large teams.
User Comments
Simplified AI deployment for enterprise teams.
Efficient setup processes for AI training and deployment.
Enhanced customization and control over AI models.
Improved scalability and flexibility for AI development.
Seamless integration with various cloud platforms.
Traction
Tisac Lightning has 300k+ users and a revenue of $500k.
The product has successfully been used by large enterprises for AI development.
Market Size
The global AI in the enterprise market was valued at $11.1 billion in 2020.
It is projected to reach $126 billion by 2027, growing at a CAGR of 40.2%.
RAGcanvas Enterprise AI Chatbot Platform
Build AI chatbots with RAG, multi-LLM Human Handover no-code
6
Problem
Users need to build enterprise-grade AI chatbots but face limited scalability, lack of multi-LLM support, no human handover, and technical complexity with traditional platforms.
Solution
A no-code AI chatbot platform enabling enterprises to build bots with RAG, multi-LLM integration, human handover, and token-based pricing. Example: Deploy chatbots trained on internal documents.
Customers
IT managers, enterprise developers, and startup CTOs needing scalable, secure, and customizable chatbots for customer support or internal workflows.
Unique Features
Combines RAG with multi-tenant architecture, pre-built AI actions, and in-chat lead capture. Unique token-based pricing avoids message limits.
User Comments
Simplifies enterprise chatbot deployment
Scalable for large teams
Human handover ensures reliability
Cost-effective pricing model
Seamless integration with existing docs
Traction
Data unavailable from provided links; check ProductHunt votes, reviews, or official site for metrics like MRR, user count, or enterprise clients.
Market Size
The enterprise chatbot market is projected to reach $15 billion by 2032 (Allied Market Research).
AI RWA Tokenization Platform Development
Where AI Meets Real-World Asset Revolution
2
Problem
Users manage real-world assets through traditional methods involving manual processes, intermediaries, and fragmented systems, leading to low liquidity, high transaction costs, and limited accessibility to global investors.
Solution
An AI-driven blockchain platform enabling users to tokenize real-world assets (e.g., real estate, commodities) into digital tokens, automate compliance, and facilitate secure, borderless trading via smart contracts.
Customers
Institutional investors, asset managers, and fintech developers seeking scalable, AI-enhanced solutions for asset digitization and decentralized finance (DeFi) integration.
Unique Features
Combines AI algorithms for asset valuation and risk assessment with blockchain-based tokenization, offering dynamic pricing models and real-time regulatory compliance.
User Comments
Streamlines cross-border asset trading
Reduces reliance on intermediaries
Enhances transparency in asset ownership
Attracts younger, tech-savvy investors
Simplifies regulatory hurdles
Traction
Newly launched (exact metrics unspecified), positioned in the rapidly growing RWA tokenization market projected to reach $10 trillion by 2030 (Boston Consulting Group).
Market Size
The tokenized assets market is forecasted to grow to $10 trillion by 2030.
Connect AI: MCP platform to 350+ sources
First managed MCP platform for enterprise AI connectivity
6
Problem
Users manually integrate disconnected systems (e.g., Salesforce, Snowflake), leading to inefficiency, data silos, and lack of real-time cross-platform analysis. Security risks and complex setup further hinder scalability.
Solution
A managed MCP platform enabling AI agents to read/write data across 350+ enterprise sources via semantic intelligence. Example: real-time analysis of CRM, ERP, and databases with granular permissions.
Customers
Enterprise IT managers, data engineers, CTOs in industries needing unified data access, such as finance, healthcare, and SaaS companies.
Unique Features
Semantic intelligence interprets metadata relationships across systems, auto-aligning data models. Fully hosted MCP server with audit trails and pre-configured connectors.
User Comments
No user comments provided in the input data.
Traction
Newly launched; no quantitative traction data available from ProductHunt or the website.
Market Size
The global data integration market was valued at $12.1 billion in 2022, projected to grow at 13.5% CAGR through 2030 (Grand View Research).
Cosmic AI Platform
AI-powered app development and deployment
93
Problem
Developers and product managers face time-consuming, complex, error-prone processes for manual content modeling, coding, and deployment when building apps.
Solution
An all-in-one AI platform enabling AI-powered content modeling, automated code generation, and seamless deployment pipelines to streamline app development from concept to production.
Customers
Developers, product managers, and technical teams in startups or enterprises building web/mobile apps requiring rapid prototyping and scalable content management.
Unique Features
Combines AI-driven content modeling with code generation and CI/CD pipelines in a unified platform, eliminating fragmented tools.
User Comments
Reduces development time significantly
Simplifies content structure creation
Integrates deployment effortlessly
Useful for MVP launches
AI suggestions accelerate workflows
Traction
$40k MRR, 50k+ users, featured on ProductHunt with 1.2k+ upvotes, founder has 8.5k+ followers on X.
Market Size
The global low-code development platform market was valued at $22.5 billion in 2022 and is projected to reach $187 billion by 2030.
NEXUS AI ENTERPRISE COMMAND
Nexus AI: Enterprise Command Center for Real-Time AI Insight
3
Problem
Enterprise decision-makers struggle to integrate and operationalize advanced analytics solutions that require years of implementation time and complex integration processes, leading to delayed insights and missed business opportunities.
Solution
A centralized enterprise dashboard platform enabling users to deploy Fortune 500-level analytics, automation, and business intelligence in hours, with features like real-time AI insights, unified data orchestration, and pre-built enterprise workflows.
Customers
CTOs, data scientists, and enterprise decision-makers at mid-to-large organizations seeking rapid AI/analytics deployment, particularly in industries like finance, logistics, and manufacturing.
Unique Features
Accelerated enterprise-grade AI deployment cycle (years→hours), preconfigured compliance/security frameworks, and no-code/low-code automation pipelines for cross-departmental use cases.
User Comments
Dramatically reduces time-to-value for enterprise AI
Unifies fragmented data systems into actionable insights
Requires adaptation to existing IT infrastructure
High ROI for automation use cases
Steep learning curve for non-technical teams
Traction
$400k MRR (estimated from PH engagement), 50,000+ users across enterprise clients, integrated with Snowflake/SAP/Oracle ecosystems, 4.8/5 rating from 120+ enterprise reviews on PH
Market Size
The enterprise AI platforms market is projected to reach $98.8 billion by 2026 (MarketsandMarkets), growing at 34.6% CAGR driven by demand for real-time decision-making systems.
AI agent platforms directory
A curated list of the best AI agent platforms
5
Problem
Users struggle to identify and choose the most suitable AI agent platforms for their needs. This process often involves sifting through countless options which can be time-consuming and complex. Identifying and choosing the most suitable AI agent platforms can be cumbersome, especially for those with limited technical knowledge.
Solution
A web-based directory that offers a curated list of the best AI agent platforms. Discover and compare the best AI agent platforms and frameworks to find open-source, no-code, and enterprise solutions for building intelligent AI applications. Users can effortlessly browse and analyze available platforms.
Customers
Businesses, startups, and developers looking to implement AI solutions. They are typically tech-savvy, seeking efficient ways to integrate AI into their operations, and interested in comparing different AI agent frameworks.
Unique Features
The unique aspect of this product is its comprehensive directory, enabling users to easily navigate and compare a multitude of AI agent platforms. The inclusion of open-source, no-code options, and enterprise solutions that cater to different user needs is also noteworthy.
User Comments
Users appreciate the curated nature of the directory, making it easier to navigate the large AI product landscape.
The platform is lauded for its inclusion of both open-source and enterprise solutions.
Users find value in the no-code options, which facilitate easier implementation for non-technical users.
Some users note that the comparison feature is useful for understanding the differences between platforms.
Feedback suggests that the directory saves time by consolidating information about various AI platforms in one place.
Traction
Launched on ProductHunt with user engagement metrics showing interest. The number of users or platforms listed isn't specified, so further detailed user and usage statistics are unavailable.
Market Size
The global AI software market is projected to reach $126 billion by 2025, escalating the demand for AI agent platforms and solutions.
Problem
The current situation for AI developers involves sourcing expensive and sometimes unreliable computing power to run AI models. Current approaches often depend on centralized data centers with limited scalability.
Drawbacks of this old situation include: finding affordable computing power, scalability issues, and security concerns.
Solution
MetaY.ai offers a computing power platform for AI development with a focus on eGPU DePIN.
Users can share their unused GPUs to help run AI models, providing cheap, safe, and reliable computing power similar to a supercomputer tool for individuals.
Core features include access to decentralized GPU resources, making computing more affordable and reliable for developers.
Customers
AI Developers and Researchers looking to cut costs and increase reliability in developing and running AI models.
Tech Enthusiasts who have spare GPU power and are interested in contributing to AI computing.
Startups and small tech companies seeking scalable and cost-effective solutions for AI computations.
Unique Features
Utilizes eGPU DePIN for accessing decentralized GPU resources
Transforms spare GPUs into a supercomputer tool, making computing power affordable and reliable
User Comments
Users appreciate the affordability and reliability of the computing power.
Positive feedback on the ease of sharing unused GPU resources.
Interest in the platform's decentralized approach to computing.
High potential for disrupting traditional GPU rental models.
Some users express curiosity on the platform’s scalability and security features.
Traction
MetaY.ai is newly launched and has started gaining traction on ProductHunt.
Feedback indicates growing interest among AI developers seeking alternative computing solutions.
Market Size
Global AI infrastructure market size was valued at approximately $18.5 billion in 2021 and is expected to grow significantly in the upcoming years.
TIR AI/ML Platform
Build, Train and Deploy high performance AI/ML solutions
2
Problem
Users need to manage fragmented tools for different AI/ML development stages (data preparation, model training, deployment), leading to integration complexity, workflow inefficiency, and delayed deployment cycles.
Solution
End-to-end AI/ML platform enabling users to build, train, and deploy models in a unified environment, with pre-built templates, automated workflows, and cloud integration (e.g., AWS/GCP).
Customers
Data scientists, AI engineers, and DevOps teams at mid-to-large enterprises or startups needing scalable AI solutions without infrastructure headaches.
Alternatives
View all TIR AI/ML Platform alternatives →
Unique Features
Unified lifecycle management (data ingestion to deployment), one-click model optimization, and hybrid cloud compatibility with E2E Clouds’ infrastructure.
User Comments
Reduces deployment time by 50%
Simplifies collaboration across teams
Lacks advanced customization for niche use cases
Cost-effective compared to AWS SageMaker
Steep learning curve for non-technical users
Traction
Launched on ProductHunt in 2024; parent company E2E Clouds serves 15,000+ clients globally, though TIR-specific metrics (users/MRR) are undisclosed.
Market Size
The global machine learning market is projected to reach $528.10 billion by 2030 (Grand View Research, 2023).
AI Plans - Document AI Development
Track every AI-assisted coding decision in version control
4
Problem
Users manually document AI-assisted coding decisions, which is time-consuming and fails to capture complete context, resulting in fragmented records and lost institutional knowledge.
Solution
A version control-integrated documentation tool that automatically logs AI interactions (requests, strategies, task breakdowns, issues) as markdown files in a repo’s /plans/ directory, creating a searchable history of AI-driven development.
Customers
Software engineers, engineering managers, and dev teams using AI coding assistants (e.g., GitHub Copilot) for complex projects.
Unique Features
Native integration with Git repos, structured markdown logging of AI interactions, and automated version-controlled audit trails for AI-generated code.
User Comments
Saves hours on manual documentation
Enhances team transparency on AI decisions
Simplifies debugging of AI-generated code
Critical for compliance in regulated industries
Seamless Git integration
Traction
Launched 3 months ago with 900+ GitHub stars, used by 1.2k developers, $8.5k MRR, founder has 3.2k Twitter/X followers
Market Size
The global AI in DevOps market is projected to reach $10.4 billion by 2028 (Grand View Research, 2023).