Moda
Customer.io
Loops.so
Mailmodo
MailerLite
SENDER
ActiveCampaign
HubSpot
Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
PromptLayer
While eCommerce businesses are looking to grow with acquisition channels like paid and influencer marketing, retention marketing through personalized experiences (like Emails & SMS) is becoming critical for businesses to stay profitable. Personalize at scale - level up your retention game with Moda.
Moda is a modern customer data & marketing platform that helps eCommerce brands understand their customers better via segments, send automated email and SMS marketing campaigns and personalize experiences for each customer based on their behaviors.
Stay ahead of your competitors by reaching out to your customers in real time by automating marketing channels such as Emails, SMS, WhatsApp and more. Engage with them by sending high-converting personalized messages based on their activities without any efforts.
Stop looking at each individual customer, group them into similar Profiles & engage with them with Moda's prebuilt segments. You can also set up different communication flows based on their behaviors such as targeting new customers, products viewed, cart abandoned, post-purchase, recommendations, and more.
While so many activities are happening in your store, you might lose track of whatโs working for your brand or what isnโt. But with Moda, you will get all your customer's data into a single view from site interactions to behaviors across your support, review, subscriptions and shipping apps.
Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
Moda LangfuseBased on our record, Langfuse seems to be a lot more popular than Moda. While we know about 28 links to Langfuse, we've tracked only 1 mention of Moda. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Email marketing: Self plug here: getmoda.io . It can help you understand your customers to the tee and craft personalized emails and SMS at scale using AI. Source: over 3 years ago
In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / 1 day ago
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / 20 days ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / about 1 month ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / about 1 month ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / about 1 month ago
Customer.io - We make it easy to send emails triggered by user behavior. Build, measure and improve your emails to activate and retain users
Helicone AI - Open-source LLM Observability for Developers
Loops.so - We bought a billboard in Times Square and we're letting you advertise your startup on it!It's free.
LangSmith - Build and deploy LLM applications with confidence
Mailmodo - Helping marketers build interactive emails and get better conversions from email marketing
LangChain - Framework for building applications with LLMs through composability