Enterprise AI Analysis
PalimpChat: Natural Language for Advanced AI Analytics
Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of AI operations to process large collections of unstructured data. Recent progress has seen the rise of declarative AI frameworks (e.g., PALIMPZEST, LOTUS, and DocETL) to build optimized and increasingly complex pipelines, but these systems often remain accessible only to expert programmers. In this demonstration, we present PALIMPCHAT, a chat-based interface to PALIMPZEST that bridges this gap by letting users create and run sophisticated AI pipelines through natural language alone. By integrating ARCHYTAS, a ReAct-based reasoning agent, and PALIMPZEST's suite of relational and LLM-based operators, PALIMPCHAT provides a practical illustration of how a chat interface can make declarative AI frameworks truly accessible to non-experts.
Transformative Impact on AI Workflow Efficiency
PalimpChat empowers non-experts and streamlines complex AI workflows, offering significant improvements in accessibility, cost-efficiency, and speed for enterprise data analysis.
0
Avg. Pipeline Cost
0
Avg. Pipeline Runtime
0
Increase in Accessibility
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
System Architecture
Demonstration Workflow
PALIMPCHAT System Overview
The PALIMPCHAT system integrates the declarative AI framework PALIMPZEST with ARCHYTAS, a reasoning agent framework. PALIMPZEST allows users to build and optimize AI pipelines for unstructured data, combining LLM-based and conventional operations through high-level logical specifications. ARCHYTAS, using a ReAct paradigm, enables LLM agents to interact with various tools, decomposing complex user requests into manageable steps.
This integration exposes PALIMPZEST's capabilities through natural language, allowing the ARCHYTAS agent to leverage templated code snippets (tools) to register datasets, generate schemas, filter records, and orchestrate entire transformation pipelines. This makes powerful AI analytics accessible even to non-expert users.
Scientific Discovery Use Case
In a real-world scenario, medical researchers can use PALIMPCHAT to perform literature surveys on colorectal cancer. The goal is to identify studies reporting on gene mutation and tumor cells, utilizing publicly available genomic datasets.
Users can upload a collection of scientific papers (e.g., PDFs), filter them based on relevance (e.g., "papers about colorectal cancer"), and then extract references to publicly available datasets with URLs. PALIMPCHAT, powered by PALIMPZEST, automates schema generation and data conversion, providing insights into pipeline execution statistics like cost and runtime. For instance, from 11 input papers, the system extracted 6 relevant datasets in about 240 seconds at a cost of $0.35.
Enterprise Process Flow
User Query
→
Agent Reasoning
→
Tool Invocation
→
PalimpZest Optimization
→
Pipeline Execution
→
Results & Stats
Cost Efficiency Highlight
$0.35 Total Cost for Scientific Discovery Pipeline| Feature | PalimpChat (Declarative + Chat) | Traditional Manual Coding |
|---|---|---|
| Accessibility |
|
|
| Optimization |
|
|
| Development Speed |
|
|
Case Study: Accelerating Biomedical Research
Medical researchers performing literature surveys on colorectal cancer often need to identify studies reporting on gene mutation, tumor cells, and publicly available genomic datasets. PalimpChat allows them to upload papers, filter by topic, and extract relevant datasets with URLs, all through natural language interaction. This significantly reduces the manual effort and accelerates the research process. The pipeline executed in ~240s with a cost of $0.35, extracting 6 relevant datasets from 11 papers.
Calculate Your Potential AI ROI
Estimate the time and cost savings your organization could achieve by implementing declarative AI solutions like PalimpChat.
Annual Savings
$0
Hours Reclaimed Annually
0
Your Path to Advanced AI Analytics
A structured approach ensures seamless integration and maximum impact with PalimpChat.
Phase 1: Discovery & Strategy
We begin with a deep dive into your current data workflows, identifying pain points and high-impact areas where PalimpChat can deliver the most value. This includes understanding your data sources, existing tools, and business objectives.
Phase 2: Pilot & Customization
A pilot program is initiated using a representative dataset to demonstrate PalimpChat's capabilities. We configure ARCHYTAS tools and PALIMPZEST schemas to align with your specific enterprise needs and data structures, ensuring initial success.
Phase 3: Integration & Training
PalimpChat is integrated into your existing environment. Comprehensive training is provided for your teams, from data scientists eager to iterate on generated code to business users leveraging natural language for analytics. Documentation is tailored to your operations.
Phase 4: Optimization & Scaling
Post-implementation, we focus on continuous optimization, leveraging PALIMPZEST's automated query optimizer to ensure peak performance and cost-efficiency as your AI pipelines scale. We monitor and refine, preparing for broader enterprise rollout.
Ready to Transform Your AI Workflows?
Unlock the full potential of your unstructured data with intelligent, declarative AI pipelines. Connect with our experts to discuss a tailored solution.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat