Fulkrum
Fulkrum provides expert-crafted training and evaluation data for AI models and agents operating in judgment-heavy business domains such as consulting, investment banking, private equity, accounting, legal, and finance. The company leverages a network of practitioners from top-tier firms to create custom prompt-rubric datasets, agent trajectory annotations, and RLHF preference pairs, with all data generated synthetically to avoid IP and confidentiality concerns.
- Artificial Intelligence
- AI Agents
- Financial Technology
- Software Only
Funding
Founders
Founder details are not available yet.
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.