Building World Class Agentic Frameworks and Observability Tools

Conductr is an advanced Observability and Productivity platform designed to accelerate software and AI development by removing friction across the engineering and deployment lifecycle.

Originally built to optimize developer velocity, track builds, and monitor deployments, Conductr has evolved into a unified platform that now manages both developer workflows and AI agent operations—delivering real-time insight into every stage of human and machine collaboration.

With Conductr, teams can track and optimize AI agent performance by monitoring deployments, runtime behavior, reasoning paths, and token utilization to maintain cost control and transparency. The platform enhances build and release efficiency by streamlining continuous delivery pipelines with intelligent automation, ensuring faster and more reliable releases. It also helps detect and resolve issues early, identifying performance bottlenecks, bugs, and anomalies across both code and agent executions before they impact production. By keeping developers and agents in flow through proactive observability, automated insights, and tools that minimize context switching and downtime, Conductr empowers teams to operate at peak efficiency.

Native Langgraph tools often lead to unnecessary complexity and rigidity. Facing these challenges, we created a new framework: Request Completion (RC). 

RC simplifies the creation of dynamic, input-driven workflows with a node-based architecture tailored for flexibility and scalability. In RC, nodes are defined with clear input and output parameters, each capable of invoking others to build modular, interconnected workflows. What sets RC apart is its approach to decision-making: each node determines its subsequent actions internally, eliminating the need for an external control flow layer. Beyond its architectural innovations, RC includes robust features to streamline development: integrated debugging tools, comprehensive error handling, real-time streaming of intermediate outputs, and automatic parallelization to handle complex workflows efficiently. RC is a direct substitute for Langgraph, designed to natively support dynamic, evolving workflows without the friction. Explore its features to see how RC can improve your engineering process.

Connect external data to LLMs, no matter the source using Railtown’s universal retrieval engine for LLMs, to access unstructured data from any source. 

Build RAG Applications on your user data. Railtown’s universal retrieval engine has pre-built connectors to ingest unstructured data from any source and load it into any destination.

Railtown ingestion pipeline allows devs to send data by event or in batch format, and allows the team to run multiple cleaning and transformation steps to the data before it is saved in a unified database structure that will allow  the team to interact and create RAG applications / automation on top of the data.

AI Partnerships Corp. was formed in 2020 with the goal of establishing a world-wide affiliate network of AI-as-a-Service based companies that are focused on providing AI-based solutions in a select number of sectors.

These sectors include healthcare, manufacturing, supply chain and fintech, as well as sector agnostic AI development tools and applications. AIP has established a network of over 180 SaaS-based AI affiliates (each an “AIP Affiliate”), primarily headquartered in Canada and the US, who have offices in 13 countries world-wide.