Save Time and Manage Contract Risk
with AI

Get Simplified Generative AI-powered Insights for All Your Contracts in G-Drive and Sharepoint

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Reference Architecture

Pathway Q and A - answer flow

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db-schenker logotype
intel logotype
F1 logotype
la-poste logotype
transdev logotype
cma-cgm logotype
Mazars logotype
CLS logotype

How it helps

To understand obligations

Automatically scan contracts to extract due dates, renewal triggers, payment terms and enable workflow to ensure compliance and timely action. Alert appropriate parties as regulatory environment changes or key deadlines approach.

Highlighted important information
To assess your liabilities

Scan for risky clauses, provide risk scoring, and flag potential issues. Highlight areas that need review by legal or business teams.

Score and condition
To negotiate better

During renewal and contract negotiations, provide comparative data, flag unacceptable/risky terms and suggest alternative based on past deals and group norms.

Highlighted changes in doc

As a lawyer, I’m always reviewing contracts as part of audits and due diligence processes. Now I can instantly search our entire contract repository stored in Sharepoint and identify specific clauses, terms, and obligations. It saves me tons of time and allows me to focus on the most strategic or risky contracts

Corporate Lawyer, a Fortune 500 company

Proven Business Impact

*The Business Impact has been calculated on a company with a team of 30 counsels, that should be recruiting 5 additional counsels to handle the growing demand. The business impact is accounted for the number of business days in a year.

Productivity savings# of UnitsCost per UnitAnnual
Counsel FTEs5$95,000$475,000
Cost savings# of UnitsCost per UnitAnnual
Savings from other external SaaS services30$3,6000$108,000
Business Impact# of UnitsCost per UnitAnnual
Improved quality of service for non Legal users251$1,500$376,500
Faster answers to non Legal users251$20$5,020
Improved contract drafting workflow251$160$40,160
C-Suite dashboard overview of global contractual landscape20$8,000$160,000
Automatic evaluation of impact of new regulations (CPRA, GDPR, LGPD,...)10$5,000$50,000
Sub-total$631,680
Total annual impact & savings$1,214,680
Compute your ROI with us
Backed by Enterprise Security & Authentication
  • Host on your cloud or on-premise
  • Secure by Design
  • Granular Access Management
  • Compliance-Ready

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Interact with demo pipelines

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Pathway utilizes RAG (Retrieval-Augmented Generation) to power its document AI magic. Here's a breakdown of the pipeline:

1. Document Ingestion:
Connect your document sources like Google Drive, SharePoint, local storage, or S3.
Supported formats include text, PDF, DOCX, and HTML.
Documents are NOT duplicated and indexed for efficient retrieval.

2. Retrieval Stage:
When you ask a question, Pathway searches its document index using powerful algorithms.
Relevant passages are retrieved based on their potential to answer your query.

3. Generation Stage:
The retrieved passages are fed into a GPT model of choice, fine-tuned for document understanding.
GPT model synthesizes the information and generates a clear, concise, and informative answer to your question.

4. Summarization (Optional):
For multiple documents, Pathway can automatically summarize responses, presenting key points for quick review.

5. Continuous Learning:
Pathway constantly learns and adapts. As your documents change, the retrieval and generation models get updated through an always refreshed vector index, ensuring your answers are always based on the latest information.

Benefits:
Accurate & Insightful Answers: Get straight to the point with answers sourced directly from your documents.
Effortless Maintenance: No separate data preprocessing needed. No need for a separate vector database. Pathway automatically keeps itself up-to-date.
Highly Customizable: Adapt the pipeline to your specific needs, including prompts, search queries, and more.
Seamless Integration: Works with various document sources and integrates smoothly with your existing workflows.

See the source code