C3 AI Feature Trails

Feature trailActive

Agentic Development Stack

C3 AI is operationalizing its Agentic Development Stack, a comprehensive platform that fuses advanced coding agents with enterprise-grade infrastructure. This stack enables the rapid and reliable development of secure, durable, and fully deployable enterprise AI applications. Key components include high-value application templates, a domain-expert LLM trained for enterprise software and the C3 AI ecosystem, and the C3 AI Model Context Protocol (MCP) Server for seamless integration of external co-pilots and tools. This post details the practical application of this stack in building an asset management application, showcasing iterative development, data source integration, backend API generation, automated testing, access control management, and enabling data science workflows.

2 posts · 2026

Timeline

Feature trailActive

Autonomous Coding Agents

C3 AI is operationalizing Autonomous Coding Agents (ACAs) as core cloud infrastructure for enterprise software, extending their role beyond developer productivity. This involves embedding remotely programmable ACAs directly into production systems for data integration, workflow generation, and decision support. These agents operate as distributed composable services that reason over enterprise data, collaborate with one another, and adapt continuously as business needs evolve. This post details the application of these agents to the problem of documenting and modernizing legacy codebases, including parsing code into structured representations, generating documentation drafts, and translating code into modern languages using a multi-agent framework for improved accuracy and consistency.

11 posts · 2025–2026

Timeline

20252026

Feature trailActive

C3 AI Inventory Optimization

C3 AI is operationalizing C3 AI Inventory Optimization, an AI-powered software solution designed to address the complexities of inventory management. It leverages simulation, advanced optimization techniques, and dynamic reorder recommendations to enable businesses to right-size inventory across products, locations, and suppliers. The system models inventory evolution, incorporates demand fluctuations, supplier reliability, and planning rules, and uses Monte Carlo simulations to test numerous reorder parameter combinations to minimize total costs while achieving service-level goals. This post details the simulation-driven approach, including modeling inventory evolution, applying optimization techniques, and using Monte Carlo simulations to account for uncertainty. It also presents a customer case study illustrating how the system reduces excess costs and service risks by analyzing historical inventory, forecasts, and supplier performance, and demonstrating how optimized strategies perform under real-world conditions.

2 posts · 2025–2026

Timeline

20252026

Feature trailActive

C3 AI Document IQ

C3 AI is operationalizing C3 AI Document IQ, an application that ingests, organizes, and applies document-based information within valuation workflows. It combines OCR, NLP, and LLMs to extract, interpret, and validate information from unstructured documents, transforming them into structured, decision-ready data with traceability. This enables automated checklist creation, content extraction from scanned documents, and validation against rules and policies, with a human-in-the-loop approach for review and refinement. The system has demonstrated significant time savings and accuracy improvements in real-world applications, such as processing condominium declarations and rule books for property appraisal.

3 posts · 2026

Timeline

Feature trailActive

Retrieval-Augmented Explainability (RAE) and Expert Companion (EC)

C3 AI is operationalizing Retrieval-Augmented Explainability (RAE) and the Expert Companion (EC) as core AI architectures for enterprise software, particularly in high-stakes operational environments like oil and gas. RAE embeds domain knowledge and situational awareness by retrieving historically similar time periods, enriched with expert annotations and operational context, to explain anomalies and provide actionable insights. The Expert Companion (EC) codifies field expertise at scale by capturing expert annotations and linking them with time series patterns and corrective actions to refine the system over time. This post details the technical implementation of RAE, including embedding time series data into vector representations using foundation models, dual-vector store retrieval (general time series and expert-annotated), uncertainty quantification and re-ranking based on annotation probability and pattern similarity, and LLM-enhanced verbalized explanations and decision recommendations. The EC's role as a feedback loop for continuous improvement is also highlighted.

1 post · 2025

Timeline

20252026