Adaline provides an end‑to‑end platform for product and engineering teams to build, test, andmonitor AI agents. It centralizes prompt management across multiple LLM providers, supports multi‑modal inputs and dynamic variables, and offers automatic versioning, performance analytics, and scalable deployment infrastructure. The service is monetized through usage‑based API credits and enterprise subscriptions, enabling teams to iterate quickly while maintaining security and reliability at scale.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and maintaining large language model (LLM) applications requires rigorous prompt engineering, testing, and monitoring to ensure reliability and minimize costs. Teams often lack a centralized platform to collaboratively iterate on prompts, evaluate performance across diverse datasets, and track changes over time. This can lead to inefficiencies, increased risk of errors, and difficulty in optimizing AI application performance.
Solution
Adaline provides a collaborative platform designed to streamline the entire LLM application lifecycle, from prompt engineering to production monitoring. The platform offers a centralized workspace for teams to iterate on prompts, evaluate performance using intelligent evaluations, and manage versions effectively. Adaline automates prompt testing across thousands of data rows, providing real-time performance analytics and continuous testing capabilities. By offering tools for prompt management, performance tracking, and issue debugging, Adaline enables teams to optimize their AI applications, reduce costs, and ensure consistent performance in real-world scenarios. The platform supports major LLM providers and models, allowing users to switch between them and fine-tune parameters for optimal results.
Target Audience
Adaline is designed for product and engineering teams building AI-powered applications, including those in enterprises and fast-scaling startups.
Features
- Collaborative playground for prompt engineering with support for chat threads and multiple LLM providers (OpenAI, Anthropic, Google Gemini)
- Variable support for incorporating context from Retrieval-Augmented Generation (RAG) pipelines or user questions
- Automated version history for prompts, enabling easy restoration and change tracking
- Intelligent evaluations, including context recall and LLM-powered rubrics, to assess model output quality
- Heuristic-based evaluations for response latency and content filtering
- Debugging tools for identifying and addressing issues, with filtering capabilities for failing tests
- Production logging to evaluate completions against established criteria
- Analytics dashboard with insights into inference counts, evaluation scores, cost metrics, and token usage
- Datasets module to build datasets from real data using Logs, upload CSVs, or collaboratively build and edit within the Adaline workspace
- Multi-environment deployments to manage the entire lifecycle from development to production with environment-specific configurations