How Pigment Built an AI-Powered Business Planning Platform with LangGraph

LangChain · Intermediate ·🤖 AI Agents & Automation ·11mo ago
Discover how Pigment revolutionized enterprise planning and performance management by replacing Excel and legacy systems with AI. In this video, see how they built conversational AI and autonomous agents using LangGraph's multi-agent framework, enabling natural language interactions with business metrics and automated scenario planning. This allowed them to reduce time-to-insight from hours to minutes. Key highlights: - Building a Multi-agent supervisor with Data Analyst, Reporter, Planner, and Modeler - Making natural language business metric queries - Automated report and scenario generation Discover more agent engineer stories: langchain.com/customers
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1 Chat With Your Documents Using LangChain + JavaScript
Chat With Your Documents Using LangChain + JavaScript
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 2 LangChain SQL Webinar
LangChain SQL Webinar
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 3 LangChain "OpenAI functions" Webinar
LangChain "OpenAI functions" Webinar
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 4 LangSmith Launch
LangSmith Launch
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 5 LangChain x Pinecone: Supercharging Llama-2 with RAG
LangChain x Pinecone: Supercharging Llama-2 with RAG
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 6 LangChain Expression Language
LangChain Expression Language
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 7 Building LLM applications with LangChain with Lance
Building LLM applications with LangChain with Lance
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 8 Benchmarking Question/Answering Over CSV Data
Benchmarking Question/Answering Over CSV Data
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 9 LangChain "RAG Evaluation" Webinar
LangChain "RAG Evaluation" Webinar
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 10 Fine-tuning in Your Voice Webinar
Fine-tuning in Your Voice Webinar
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 11 Tabular Data Retrieval
Tabular Data Retrieval
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 12 Building an LLM Application with Audio by AssemblyAI
Building an LLM Application with Audio by AssemblyAI
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 13 Superagent Deepdive Webinar
Superagent Deepdive Webinar
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 14 Lessons from Deploying LLMs with LangSmith
Lessons from Deploying LLMs with LangSmith
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 15 Shortwave Assistant Deepdive Webinar
Shortwave Assistant Deepdive Webinar
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 16 Cognitive Architectures for Language Agents
Cognitive Architectures for Language Agents
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 17 Effectively Building with LLMs in the Browser with Jacob
Effectively Building with LLMs in the Browser with Jacob
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 18 Data Privacy for LLMs
Data Privacy for LLMs
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 19 "Theory of Mind" Webinar with Plastic Labs
"Theory of Mind" Webinar with Plastic Labs
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 20 LangChain Templates
LangChain Templates
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 21 Using Natural Language to Query Postgres with Jacob
Using Natural Language to Query Postgres with Jacob
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 22 Building a Research Assistant from Scratch
Building a Research Assistant from Scratch
LangChain
 23 Benchmarking RAG over LangChain Docs
Benchmarking RAG over LangChain Docs
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 24 Skeleton-of-Thought: Building a New Template from Scratch
Skeleton-of-Thought: Building a New Template from Scratch
LangChain
 25 Benchmarking Methods for Semi-Structured RAG
Benchmarking Methods for Semi-Structured RAG
LangChain
 26 LangSmith Highlights: Getting Started
LangSmith Highlights: Getting Started
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 27 LangSmith Highlights: Debugging
LangSmith Highlights: Debugging
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 28 LangSmith Highlights: Datasets
LangSmith Highlights: Datasets
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 29 LangSmith Highlights: Evaluation
LangSmith Highlights: Evaluation
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 30 LangSmith Highlights: Human Annotation
LangSmith Highlights: Human Annotation
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 31 LangSmith Highlights: Monitoring
LangSmith Highlights: Monitoring
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 32 LangSmith Highlights: Hub
LangSmith Highlights: Hub
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 33 SQL Research Assistant
SQL Research Assistant
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 34 Getting Started with Multi-Modal LLMs
Getting Started with Multi-Modal LLMs
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 35 Build a Full Stack RAG App With TypeScript
Build a Full Stack RAG App With TypeScript
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 36 Auto-Prompt Builder (with Hosted LangServe)
Auto-Prompt Builder (with Hosted LangServe)
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 37 LangChain v0.1.0 Launch: Introduction
LangChain v0.1.0 Launch: Introduction
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 38 LangChain v0.1.0 Launch: Observability
LangChain v0.1.0 Launch: Observability
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 39 LangChain v0.1.0 Launch: Integrations
LangChain v0.1.0 Launch: Integrations
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 40 LangChain v0.1.0 Launch: Composability
LangChain v0.1.0 Launch: Composability
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 41 LangChain v0.1.0 Launch: Streaming
LangChain v0.1.0 Launch: Streaming
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 42 LangChain v0.1.0 Launch: Output Parsing
LangChain v0.1.0 Launch: Output Parsing
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 43 LangChain v0.1.0 Launch: Retrieval
LangChain v0.1.0 Launch: Retrieval
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 44 LangChain v0.1.0 Launch: Agents
LangChain v0.1.0 Launch: Agents
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 45 Build and Deploy a RAG app with Pinecone Serverless
Build and Deploy a RAG app with Pinecone Serverless
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 46 Hosted LangServe + LangChain Templates
Hosted LangServe + LangChain Templates
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 47 LangGraph: Intro
LangGraph: Intro
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 48 LangGraph: Agent Executor
LangGraph: Agent Executor
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 49 LangGraph: Chat Agent Executor
LangGraph: Chat Agent Executor
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 50 LangGraph: Human-in-the-Loop
LangGraph: Human-in-the-Loop
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 51 LangGraph: Dynamically Returning a Tool Output Directly
LangGraph: Dynamically Returning a Tool Output Directly
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 52 LangGraph: Respond in a Specific Format
LangGraph: Respond in a Specific Format
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 53 LangGraph: Managing Agent Steps
LangGraph: Managing Agent Steps
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 54 LangGraph: Force-Calling a Tool
LangGraph: Force-Calling a Tool
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 55 LangGraph: Multi-Agent Workflows
LangGraph: Multi-Agent Workflows
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 56 Streaming Events: Introducing a new `stream_events` method
Streaming Events: Introducing a new `stream_events` method
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 57 Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
LangChain
 58 OpenGPTs
OpenGPTs
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 59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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 60 LangGraph: Persistence
LangGraph: Persistence
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