Network optimization, customer service automation, churn prediction.

Implementing a Due‑Diligence Copilot: Lessons from Fintech / Law Firm Use Cases

Intro summary – Client & outcome. A legal‑technology start‑up built a copilot platform to assist lawyers with document review, contract analysis and due diligence. By integrating Anthropic’s Claude models, the platform achieved high performance on internal benchmarks and improved lawyers’ ability to handle complex, long‑context documents. It also scaled to support deployment across multiple law firms within a month.

1 Context & Challenge

Law firms process large volumes of contracts, financial statements and regulatory documents when performing due diligence. Review cycles are time‑consuming and error‑prone, and lawyers often copy‑and‑paste from templates. Existing AI contract‑analysis tools struggled with long documents and domain‑specific language.

2 Goal / Success criteria

• Enhance lawyer productivity. Provide a copilot that drafts, summarises and reviews documents, reducing time spent on routine tasks.

• Handle long documents. Support context windows large enough to ingest hundreds of pages and understand complex clauses.

• Maintain accuracy and compliance. Ensure the AI’s outputs are reliable and preserve confidentiality.

3 Approach / Implementation

• Custom legal workflows. Domain experts and AI researchers collaborated to map common legal tasks—due diligence, contract drafting, litigation support—to model prompts and evaluation criteria.

• Large‑context models. The copilot integrated Anthropic’s Claude models, which support long‑context reasoning. Engineers fine‑tuned prompts to handle legal language and summarise sections effectively.

• Human‑in‑the‑loop. Lawyers interacted with the copilot via chat, reviewed the AI’s outputs and provided corrections. This feedback loop improved model performance over time.

• Secure deployment. The system was deployed on a secure platform with enterprise‑grade encryption and compliance with data‑protection regulations.

4 Outcomes & Metrics

• Rapid deployment. The platform integrated the Claude models and rolled them out to clients in under one month.

• High performance. On the start‑up’s proprietary “BigLaw Bench” evaluation, the copilot achieved top‑quartile scores across legal reasoning tasks.

• Productivity gains. Lawyers reported that the copilot dramatically reduced time spent on repetitive contract analysis and due‑diligence checklists. While the case study doesn’t quantify time savings, it emphasises improved outcomes and the ability to handle long documents.

5 Challenges & Lessons Learned

• Risk of hallucination. The model occasionally produced incorrect references; human review remained essential.

• Domain specificity. Fine‑tuning prompts for specific areas of law (e.g., banking vs. litigation) required input from subject‑matter experts.

• Ethical and regulatory considerations. Ensuring that AI outputs comply with professional‑conduct rules and confidentiality obligations is critical.

6 Next Steps / Extensions

The company planned to extend the copilot to support regulatory compliance tasks (e.g., KYC/AML checks) and to integrate additional data sources such as case law and market research.

Tools and Architectures Highlighted

The copilot combines large‑context language models, custom prompt engineering, domain‑specific workflows, human‑in‑the‑loop feedback, and a secure deployment platform. These elements enable legal professionals to perform due diligence more efficiently while maintaining accuracy and oversight.

Disclaimer: The case studies presented here are for illustrative purposes only and are based on publicly available information. They do not represent projects executed by Zynolabs and are intended solely to demonstrate the types of AI solutions and outcomes that could be achieved in comparable scenarios.