One platform for affiliate, influencer, and referral programs. Plug in your billing, define how commissions work, and be live before the day ends.
We connect directly to Stripe, Paddle, Chargebee, Recurly, and Braintree, so every billing event reaches your program as it happens. Each upgrade, downgrade, renewal, refund, and cancellation recalculates the commission behind it. Payouts never drift from revenue.
For paying promoters, choose fully managed payouts or bulk transfers through PayPal and Wise. We collect W-9/W-8BEN forms before funds move and attach an invoice to every payout, so the paperwork side of the program largely runs itself.
Promoters track links, coupon codes, and earnings from a dashboard under your brand. Behind it, you hold 18-point reporting, commission structures from flat fee to multi-tier, fraud detection, and broadcast email across your promoter base.
Try it free for 14 days. No credit card required.
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Extole is the enterprise offer management platform powering personalized incentive programs at scale for B2C brands. Highly configurable and AI-accessible, Extole is structured to fit seamlessly into every team’s agentic workflow.
Marketers build intelligent offer programs that motivate action throughout the customer lifecycle; product teams embed them into digital experiences to maximize app growth; and developers extend them via APIs, SDKs, and partner integrations to enable customizability.
Behind every offer, Extole combines real-time audience and event data with enterprise-grade infrastructure that ensures every reward is delivered securely, accurately, and reliably.
Leading brands across retail, financial services, telecommunications, and home services trust Extole to power referral, loyalty, promotional, lifecycle, and reward-for-action programs. Build your next offer program with Extole.
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YoRefer
YoRefer is a web platform designed to assist businesses in the UK in boosting their word-of-mouth referrals. By transforming your most devoted customers into enthusiastic advocates, it offers them rewards such as digital gift certificates or other personalized incentives for each successful referral they make. You can set up your initial referral program in under five minutes, eliminating the need for any technical skills or developer assistance.
With this application, you can connect with new leads, monitor their journey throughout the referral process, and manage payouts conveniently from your dashboard. Incorporating this system into your customer experience can create a viral loop effect that continues to grow and enhance your referral rates over time. This approach not only encourages loyalty but also fosters a community of brand ambassadors who contribute to your business's success.
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Muse Spark 1.2
Muse Spark 1.2 is a coding-focused AI model from Meta designed to support advanced software engineering tasks through Muse Code and the Meta Model API. The model builds on Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan repository changes, write code, validate outputs, and work across large codebases. Muse Code uses persistent async background agents that stay active throughout a session to reduce redundant information gathering and support difficult multi-step work. The runtime uses a local event log where model calls, tool runs, approvals, and edits are appended, making sessions replay-exact and restart-safe. Muse Spark 1.2 was co-trained with Muse Code so the model can take advantage of its toolset, harness workflows, goals, compaction, and subagent architecture. Meta significantly scaled training compute on coding tasks and expanded training environment diversity to improve the model’s engineering capabilities. The model was also trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, auto-research, and extended iterative work. Its training approach uses planning, goal conditioning, context compaction, rejection-sampled harness trajectories, and self-improvement data generated with Muse Spark 1.1. Meta also tested Muse Spark 1.2 on long-running GPU kernel optimization workflows where the model wrote, compiled, profiled, and improved Triton kernels over many tool calls. By combining coding-focused training, agentic runtime integration, persistent subagents, long-horizon reasoning, replay-safe execution, and API availability, Muse Spark 1.2 helps developers and AI agents complete complex software engineering work with less intervention.
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