Every agreement, whether it’s a financing round, acquisition, or vendor contract, is defined by specific deal points: the economic and legal terms that together define risk, reward, and business context. Yet for most legal teams, that context remains locked inside PDFs and unstructured data.

Transactional lawyers spend hours sifting through thousands of documents to extract and compare these deal points—a slow, costly, and error-prone process. Despite billions invested in AI, most tools fail to deliver reliable results because they lack context. They can read language, but they don’t understand what that language means within the structure of a deal.

Why We Invested: Infrastructure for Structured Legal Intelligence

At Myriad Venture Partners, we back companies that solve systemic inefficiencies through novel infrastructure. We believe the future of enterprise AI must be:

• Domain-specific: Tools must understand the language and logic of the industries they serve.
• Data-secure: Confidentiality is non-negotiable, especially in sensitive sectors like transactional law.
• Deployment-friendly: Adoption must be seamless, especially in change-resistant environments.
• ROI-driven: Products must provide measurable value.

Syntracts embodies each of these principles.

The company has built an on-premise, API-first platform that integrates directly into law firms' document and knowledge management systems. Unlike cloud-based legal AI tools that require sending sensitive client data to third-party servers, Syntracts deploys entirely within a firm's infrastructure, processing and structuring deal data behind the firewall while making existing AI tools smarter and more reliable.

This architectural approach addresses two critical problems simultaneously: data security concerns that have blocked AI adoption at major firms, and the fundamental limitation of generic AI models that lack legal context. By training custom models on privacy-preserving representations of each firm's document corpus, Syntracts delivers accuracy tailored to real workflows without exposing confidential information.

By deploying self-hosted models on client infrastructure, Syntracts eliminates variable API costs and provides firms with direct control over computational resources, enabling more predictable total cost of ownership compared to cloud-dependent alternatives. This becomes especially valuable as firms scale their AI usage across thousands of contracts and multiple practice groups.

Early deployments demonstrate significant efficiency gains in contract profiling workflows, with users reporting multi-hour reviews compressed by more than 80% of their typical process.

Rather than chasing the entire legal market, Syntracts is intentionally focused on transactional practice groups, where accuracy, context, and confidentiality matter most. Transactional work represents approximately 45% of law firm revenue and is particularly well-suited to structured data approaches because deals follow recognizable patterns with definable terms. From that wedge, the company is positioned to expand into contract drafting, negotiation, and deal intelligence, ultimately serving not only law firms but also in-house legal teams, insurers, and financial institutions, among others - essentially any industry where deal points define core business value.

The Status Quo: Why Existing Solutions Have Failed

To understand why Syntracts matters, consider why existing contract analysis tools haven't solved this problem:

• Manual workflows persist: Despite a decade of investment in legal tech, transactional lawyers still spend multiple hours reviewing individual contracts. A practice group handling 200 contracts annually may spend more than three hours per contract on initial profiling and analysis, representing over 600 attorney hours of routine extraction work that could be structured systematically. First-generation AI tools failed to gain adoption because they treated contracts as unstructured text rather than structured data objects. These tools could identify clauses but couldn't understand the relationships between provisions, track how terms evolved across deal versions, or benchmark specific deal points against historical precedent. The result: outputs that still required extensive manual verification.
• Cloud-based solutions introduced a second barrier: data security concerns. For transactions involving confidential M&A negotiations, unreleased financing terms, or sensitive corporate restructurings, sending documents to third-party servers was simply non-viable for most AmLaw 100 firms and their clients.

Even AI tools designed for legal use often introduce new overhead, requiring heavy prompt calibration, hallucination checks, and manual verification.

What's changed? Two factors converge to make this the right timing: (1) advances in fine-tuning techniques that enable custom model training without massive data sets, and (2) growing enterprise demand for on-premise AI as firms recognize cloud-based tools won't meet their security requirements. Syntracts addresses both dynamics with an architectural approach built specifically for structured legal data extraction.

The Syntracts Approach

Syntracts automatically extracts, tags, and structures deal points from unstructured data and contracts, creating a searchable, queryable data layer that provides:

• Instant access to key clauses, terms, and obligations across a firm's entire document archive.
• Contract benchmarking across matters and clients, enabling data-driven negotiation positions.
• Historical deal analytics that surface how specific terms vary by counterparty, industry, or transaction type.

By understanding how deal points vary from contract to contract, as well as capturing the contextual relationships between provisions, Syntracts provides not just visibility into documents, but insight into the evolving relationships and risks those documents represent.

Three technical differentiators enable this approach:

1. Custom-Trained Models Using Privacy-Preserving Data: Rather than relying on generic models trained on web data, Syntracts creates custom models trained on privacy-preserving representations of each firm's document corpus. This approach delivers accuracy tuned to a firm's specific practice areas, deal types, and drafting conventions, without exposing actual contract content during the training process.

2. 100% On-Premise Deployment: Syntracts runs entirely within a firm's infrastructure. Documents never leave the firewall, eliminating third-party dependencies and providing firms with full control over their AI infrastructure while maintaining compliance with client confidentiality requirements.

3. API-First Integration: The platform integrates directly with existing document management systems (DMS), contract lifecycle management (CLM) tools, and workflow platforms via API. This enables firms to enhance their current technology stack without rip-and-replace implementations, reducing deployment friction in an industry resistant to workflow disruption.

Market Traction and Validation

A top twenty-five AmLaw firm has deployed Syntracts for a multi-year, full-firm implementation, an uncommon commitment in enterprise legal technology. This rollout followed a six-month pilot and competitive vendor evaluation, making it one of the first fully on-prem, large-scale AI implementations across multiple practice groups at a major law firm.

Syntracts was also selected for A&O Shearman’s Fuse Incubator, a highly competitive accelerator that admits only a handful of companies each year. Out of hundreds of applicants, Syntracts was one of just two selected for its ability to amplify A&O Shearman’s impact and enhance its global offerings.

These deployments signal growing recognition among sophisticated legal buyers that structured, on-premise AI infrastructure—not cloud-based generative models—will define the next phase of transactional legal technology adoption.

The Founding Team: Deep Tech Meets Deep Law

Lifelong friends Doug Bemis and Chris Martin built Syntracts on decades of trust and a shared mission to bring structure, precision, and security to legal data.

Doug Bemis brings deep technical leadership in machine learning, natural language processing, and secure, scalable infrastructure. With a PhD in Computational Neurolinguistics, he understands the subtlety of language, the same nuance that underpins Syntracts’ ability to interpret complex legal text. He previously co-founded Geometric Intelligence (acquired by Uber to form Uber AI Labs), led ML-driven operations at Optoro, and built zero-to-scale AI systems at Redesign Health.

Chris Martin bridges the legal domain, having served as a corporate associate at Akin Gump and Cooley, and later leading Latham & Watkins’ Emerging Tech Team. His firsthand experience exposed the gap between AI potential and legal practicality—a gap Syntracts is closing.

Together, they combine deep technical fluency with practical legal insight, an uncommon pairing that’s critical for building infrastructure-grade legal AI.

Partnering with the Future of Legal Tech

At Myriad, we see AI’s greatest impact in fields where trust, accuracy, and security matter most, and law is one of the clearest examples.

Syntracts gives legal teams a structured, secure foundation to make faster, smarter data-driven decisions, scale institutional knowledge across practice groups and offices, and maintain complete data sovereignty without sacrificing AI capability.

The company is building critical infrastructure for transactional intelligence—structured data that powers faster negotiation, better risk assessment, and institutional knowledge preservation. Validated by top-tier legal clients, Syntracts is proving that structured, secure AI isn’t just the future of law, it’s the infrastructure that will power it.

We’re proud to partner with Syntracts as they build this new infrastructure layer for the legal industry.