Fulkrum
Fulkrum provides expert-crafted training and evaluation datasets for AI models and agents operating in judgment-heavy business domains such as consulting, investment banking, private equity, accounting, legal, and finance. Its data is created by practitioners with real deal and client experience from firms like McKinsey, Goldman Sachs, and the Big Four, and includes custom prompt-rubric pairs, RLHF preference data, and multi-step agent trajectory annotations.
- Artificial Intelligence
- AI Agents
- Financial Technology
- Software Only
Funding
Founders
Product
Problem
AI models and agents often struggle in judgment-heavy business domains like consulting, investment banking, and accounting because generic training data lacks the nuance, context, and professional rigor required for accurate reasoning. Off-the-shelf datasets fail to capture the complexity of real-world workflows, leading to models that underperform on tasks requiring deep domain expertise.
Solution
Fulkrum creates expert-crafted training and evaluation data specifically for AI labs and agents in consulting, investment banking, private equity, accounting, legal, and finance. The company leverages a network of practitioners from top-tier firms like McKinsey, Goldman Sachs, and the Big Four to produce custom prompt-rubric datasets, agent trajectory annotations, and RLHF preference pairs. Every task is governed by explicit rubrics and a multi-step QA process, ensuring quality and consistency. Fulkrum also develops business RL environments—multi-day practitioner simulations that generate artifact-rich scenarios for realistic next-step reasoning training.
Target Audience
AI labs and AI product teams building copilots or agents for consulting, investment banking, private equity, accounting, legal, and finance applications.
Features
- Custom dataset design with proprietary prompt-rubric pairs tailored to specific models and domains, avoiding repurposed open data.
- End-to-end agent evaluation with multi-step trajectory annotations for tool-calling agents in complex business workflows.
- Business RL environments built from multi-day practitioner simulations, including emails, notes, and deliverables.
- RLHF and preference data with expert-ranked response pairs for reward model training and DPO/RLAIF.
- Multi-step QA process involving audit trails, gold-standard checks, and AI-assisted review.
- All data generated synthetically by domain experts to avoid IP, copyright, or confidentiality concerns.