Find Hidden Legal Risks in Contracts with Custom AI
Custom algorithms identify legal risks by comparing contract clauses against a firm's approved library. They flag non-standard terms, missing provisions, and ambiguous language automatically.
Syntora designs custom algorithms to identify legal risks in contracts. These systems compare contract clauses against a firm's approved library, flagging non-standard terms and ambiguous language for attorney review. Syntora's approach focuses on building tailored, human-in-the-loop solutions for legal document processing.
Syntora designs and builds custom systems tailored to your firm's specific legal playbook and risk tolerance for contract types like commercial leases, vendor agreements, or Master Service Agreements. These are production systems, not general-purpose SaaS tools.
The scope of such an engagement typically involves integrating with your existing document workflows and defining precise risk parameters with your legal team. We've developed document processing pipelines using Claude API for sensitive financial documents, and the underlying architectural patterns for clause extraction and semantic comparison are directly applicable to legal contracts.
What Problem Does This Solve?
Many firms start with contract management software like Ironclad or ContractWorks. These platforms are excellent for tracking key dates and organizing documents, but their AI features are often limited to keyword search. They can find an indemnity clause but cannot analyze its language to see if it deviates from your firm’s approved text. The logic is too rigid for nuanced legal review.
A paralegal at a growing business might try a Microsoft Word add-in that promises AI review. The problem is these tools use a generic model trained on public data. It might flag a clause as 'uncommon' but lacks the context of your business's specific risk profile. For a medical device company, a standard limitation of liability clause might be flagged as risky, creating false positives and wasting an attorney's time.
This forces a manual process. A new vendor agreement arrives. The add-in flags five clauses. A paralegal then has to open the firm's clause library in a separate document, manually compare each flagged clause line-by-line, and then search past contracts to see what variations were accepted before. The 'AI' tool added a step instead of removing one.
How Would Syntora Approach This?
Syntora approaches contract risk identification as a custom engineering engagement. The process would begin with a discovery phase to understand your firm's specific legal playbook, risk tolerance, and existing document workflows.
The first step in building the system would involve ingesting your firm's standard clause library and a representative set of historical contracts into a secure cloud storage, such as an AWS S3 bucket. A custom FastAPI service would be developed to handle Optical Character Recognition (OCR) for incoming PDFs. This service would also classify each document into relevant matter types, such as 'Vendor MSA' or 'Commercial Lease', to ensure the appropriate risk rules are applied.
For each new contract, the Claude API would be used to extract and categorize individual clauses. These extracted clauses would then be converted into vector embeddings and compared against your approved clauses, which would be stored in a Supabase vector database. This semantic comparison capability allows the system to identify conceptually similar clauses even when the wording differs. Clauses falling below a predefined similarity threshold would be flagged for review.
The delivered system would expose a simple review interface. An attorney would see the original flagged clause, a corresponding standard version from your library, and a clear indication of potential risk. This approach ensures a human-in-the-loop gate, where no contract is approved or rejected without an attorney's explicit sign-off. Every AI suggestion, confidence score, and final human decision would be logged in Supabase, establishing a permanent audit trail.
The system architecture would be designed for scalability and minimal operational overhead, often leveraging serverless technologies like AWS Lambda for processing and orchestration. Integration points, such as dedicated email inboxes for new contracts or notifications via Slack, would be tailored to your firm's operational needs. Importantly, all data processing and storage would occur within your firm's own cloud infrastructure, maintaining data security and compliance.
What Are the Key Benefits?
Review a 30-Page Lease in 90 Seconds
The system handles clause extraction and comparison instantly. Your team's time is spent on high-level legal strategy, not manual cross-referencing.
Built Once, Owned Forever
A single project fee replaces unpredictable per-document or per-seat SaaS costs. Monthly hosting on AWS is often under $50.
Your Clause Library, Your AI Model
You receive the complete Python source code in a private GitHub repository. The system is trained exclusively on your firm's documents and legal standards.
Audit Trails for Every AI Decision
Every flagged clause is logged with a confidence score and the attorney's final decision. This creates a feedback loop and provides a full audit history.
Works With Your Existing Email
Integrates with Office 365 or Google Workspace. New contracts are processed automatically from an inbox, eliminating manual uploads.
What Does the Process Look Like?
Week 1: Data and Playbook Ingestion
You provide your standard clause library and 50+ executed contracts. We set up the secure AWS S3 bucket and ingest the documents for processing.
Week 2: Core Algorithm Development
We build the Claude API pipeline for clause extraction and the Supabase vector database for comparison. You receive a demo of the flagging logic on 5 sample contracts.
Weeks 3-4: Review Interface and Integration
We build the human-in-the-loop review dashboard and connect the system to your email intake. You receive login credentials for user acceptance testing.
Post-Launch: Monitoring and Handoff
We monitor system performance for 30 days post-launch to tune thresholds. You receive the full source code, deployment scripts, and a maintenance runbook.
Frequently Asked Questions
- How much does a custom contract review system cost?
- Pricing depends on the number of contract types to be analyzed, the complexity of your legal playbook, and the quality of your historical documents. A project with two contract types and a well-defined clause library is a 4-week build. A more complex engagement with ten contract types may take longer. We provide a fixed-price proposal after a discovery call. Book a discovery call at cal.com/syntora/discover.
- What happens if the AI misinterprets a clause?
- The system is designed with a mandatory human-in-the-loop checkpoint. The AI only flags potential risks and suggests comparisons; an attorney always makes the final determination before any action is taken. This prevents automation errors. Every decision is logged, which helps us identify and correct any systematic misinterpretations by tuning the model over time.
- How is this different from using ChatGPT for contract review?
- Using public models like ChatGPT with client contracts raises serious attorney-client privilege and data privacy concerns. Our system uses the Claude API via a business associate agreement, ensuring your data is not used for training public models. More importantly, it is tuned on your firm's specific legal standards, not generic internet data, providing far more relevant and accurate risk analysis.
- Where is our confidential client data stored?
- All data, including contracts and extracted clauses, resides on your private cloud infrastructure, typically AWS. We use Supabase for the database and AWS S3 for document storage, all configured within your account. Syntora never stores your firm's privileged documents on its own systems. You maintain full ownership and control of all sensitive information.
- What kinds of contracts work best with this system?
- The system excels with standardized contracts like commercial leases, vendor agreements, MSAs, and employment offers where your firm has a defined playbook. It is less effective for highly bespoke documents like complex M&A agreements, which require more nuanced human interpretation from the start. We identify the best candidate documents during our initial discovery process.
- What if our legal standards or clauses change?
- The system is built for easy maintenance. Your firm's standard clauses are stored in a Supabase database. We provide a simple admin interface that allows your team to add, remove, or edit clauses in the library without writing any code. Changes are reflected in the risk analysis for the very next contract processed.
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