CHIFFON FRAMEWORKS builds a context-management layer that connects enterprise AI tools like OpenAI, Anthropic, Copilot, and Gemini to a firm's proprietary taxonomy, client definitions, and institutional knowledge. The company deploys version-controlled frameworks that make AI outputs specific to a firm's data and workflows, with a focus on maintaining human decision authority and full audit trails. Their approach includes a four-step deployment process—from workflow scoping to training—and has been applied to use cases like tax lien auction intelligence across 52+ jurisdictions.
Funding
Funding not disclosed
Founders
Product
Problem
Small and mid-sized firms using enterprise AI tools often receive generic, off-the-shelf outputs because the models lack context about the firm's specific data, terminology, and workflows. This forces teams to spend time on formatting and manual intervention rather than analysis, and creates a gap between the institutional knowledge held by senior leadership and what AI systems can access.
Solution
CHIFFON FRAMEWORKS provides a context-management layer that sits on top of existing enterprise AI tools, connecting them to a firm's taxonomy, client definitions, and institutional knowledge. The company follows a four-step process—understanding the workflow, collecting and normalizing data, building a prototype, and deploying with training—to create version-controlled frameworks that integrate with a firm's existing toolchain. These frameworks enable AI to reason with a firm's data rather than around it, with human operators maintaining decision authority and full audit trails. The goal is to shift teams from aggregation to analysis, with AI handling the structuring and humans making the decisions.
Target Audience
Primary customers are small to mid-sized firms in professional services, finance, and real estate, typically engaging operations, marketing, or RevOps teams that are burdened by unstructured data and manual reporting workflows.
Features
- Context management layer compatible with OpenAI, Anthropic, Copilot, and Gemini
- Version-controlled frameworks that integrate with existing enterprise toolchains
- Four-step deployment process: workflow scoping, data collection and normalization, prototype building, and deployment with training
- Automation of recurring data pulls, model refreshes, comp tables, and briefing documents
- Feedback loops that allow systems to test their own output, learn from manual interventions, and graduate from assisted to autonomous operation
- Capability to map data pipeline dependencies across Excel templates, SQL, Snowflake, and CRM fields