By Austin Sun, co-founder of Clausey.ai
The Starting Point: Why Contracts Need Conversation
Contracts sit at the heart of every business. They govern relationships with vendors, outline obligations withpartners, and define the risks and opportunities an organization faces. Yet, for most companies, contracts are difficult to access, time-consuming toanalyze, and nearly impossible to query without dedicated legal teams.
At Clausey, we set out to change that. Our vision has always been to make contracts as searchable and interactive as a database — something employee scan interrogate as easily as they ask a question to a colleague. Imagine being able to say:
“Show me every vendor contract that isset to expire in the next 90 days.”
or
“List the agreements that contain penalties greater than $50,000 for late delivery.”
The promise of AI should be to make these questions routine, but in reality, they require a deep technical bridge: one that connects human language to structured contract data with precision, speed,and reliability.
That bridge, for us, turned out to be Chat2Data.
The Search for a Scalable Solution
In the early stages of building Clausey, our engineering team faced a classic startup tradeoff: do we attempt to build anatural language query system from scratch, or do we adopt and adapt existing frameworks? On one hand, rolling our own solution meant full control but at thecost of months — maybe years — of experimentation with parsing, query generation, schema management, and access control. On the other, finding theright framework could allow us to leapfrog straight into value creation for ourcustomers.
The difficulty wasn’t simply turning text into SQL. It was making the translation reliable, secure, and context-aware for the legal domain. Legal queries carry nuance: renewal clauses, exceptions, cross-references, and conditional obligations allmatter. A single misinterpretation can shift the meaning of a contract query from accurate to dangerously misleading.
We knew we needed a framework that couldhandle complexity at enterprise scale while still being flexible enough for a fast-moving startup. That’s when we came across DataBathing and Chat2Data,a system created by AI/ML and data-engineering expert Jiazhen Zhu.
Enter DataBathing and Chat2Data
DataBathing and Chat2Data was built to doexactly what we needed: connect natural language with structured enterprisedata in a way that is trustworthy and production-ready. It doesn’t just translate text into queries. It introspects schemas, optimizes for efficiency,validates outputs against known structures, and ensures queries respect accessand security policies.
For Clausey, this meant we could immediately provide our users with a conversational layer over contracts without having to reinvent critical infrastructure. When a user asks about renewal clauses or penalty triggers, DataBathing and Chat2Data examines our underlying schema, generates the appropriate structured queries, and ensuresthe results align with contract metadata and document context.
Instead of worrying about whether a query would break or return irrelevant results, our team could focus on refining theuser experience and domain models specific to contracts.
The Integration Experience
Bringing DataBathing and Chat2Data intoour stack was less about bolting on a third-party library and more about incorporating an architectural philosophy. The framework’s modular design allowed us to integrate it alongside our existing contract ingestion and AIparsing pipelines.
During the first weeks, we connected DataBathing and Chat2Data to our metadata store and ran a series of internal benchmarks. We measured not just correctness of query translation but alsolatency, scalability under concurrent load, and resilience to ambiguous phrasing. The results were immediately promising: queries that previously required custom engineering logic resolved correctly and efficiently.
We then piloted the system with a closedgroup of users. These were individuals who had historically avoided advanced filters because they were too technical or cumbersome. With DataBathing andChat2Data, they were able to simply type questions in plain English and receive precise, validated answers. The enthusiasm was immediate. Users who once saw contracts as static PDFs now experienced them as a living knowledge base.
Impact on Engineering and Product Development
For our team, the biggest benefit was speed. Development cycles shortened dramatically. What once required weeks of backend engineering effort could nowbe delivered in a matter of days. Instead of writing complex SQL queries orbuilding custom APIs for each new question type, our developers could rely on DataBathing and Chat2Data’s natural language understanding and schema awareness.
This speed translated into directbusiness impact. We were able to accelerate our roadmap, delivering feature smonths ahead of schedule. We rolled out natural language contract queries as acore product capability, which quickly became one of the most adopted features among our customers.
Equally important, the reliability of DataBathing and Chat2Data improved trust. Because the framework includes validation and caching mechanisms, errors and hallucinated queries dropped significantly. Customers who tested the system reported higher confidence in AI-driven answers, which in turn drove adoption and retention.
Transforming the User Experience
For end users, the change was profound. Instead of struggling with dropdowns, filters, or static search fields, they could now treat their contracts like a conversation partner. Lawyers, procurement officers, and finance teams alike could ask questions in their own words and receive precise answers in seconds.
One user described it as “like having ajunior lawyer on demand — except faster and always available.” Another remarked that it shifted how they thought about contract management altogether, turning what used to be a reactive process into a proactive one.
This kind of transformation only happens when the underlying technology is both powerful and invisible. DataBathing and Chat2Data gave us that foundation: a layer of intelligence that feels seamless to the user but is powered by deep technical innovation.
Why DataBathing and Chat2Data Matters Beyond Clausey
While Clausey is among the early adopters, the implications of DataBathing and Chat2Data extend far beyond ourcompany. The framework represents a new way of thinking about how largelanguage models interact with structured enterprise data.
Too often, AI systems are either powerful but unreliable, or reliable but inflexible. Chat2Data demonstrates that withthe right design — schema awareness, validation, and enterprise-grade controls— it is possible to achieve both. This has ramifications not only for legal technology but for any industry where professionals need to query structured data using natural language: healthcare, finance, supply chain, and more.
For Clausey, adopting Chat2Data was adecision about speed and scalability. But in the bigger picture, it underscore show foundational frameworks like this can reshape the AI ecosystem by enabling startups to innovate on top of trusted infrastructure.
Looking Ahead
As we continue to scale Clausey, DataBathing and Chat2Data remains a central part of our architecture. We areexploring deeper integrations, including cross-document queries, multi-language support, and richer contract analytics. Each of these builds on the same principle: that contracts should be as easy to query as they are to read.
We see Chat2Data not just as a tool but as a partner in this vision. The framework gave us a head start, saved uscountless engineering hours, and provided the reliability we needed to earn user trust. In doing so, it helped turn our vision into a reality much faster than we thought possible.
Conclusion
Every startup faces a moment when technology choices define its trajectory. For Clausey, integrating DataBathing and Chat2Data was one of those moments. It allowed us to move faster, deliver more value, and raise the standard for what contract intelligence can achieve.
More than that, it proved that the right AI frameworks don’t just solve technical problems — they unlock new possibilities. With DataBathing and Chat2Data, contracts are no longer static documents hidden in folders. They are living, conversational assets that businesses can interact with in real time.
And that is the future we believe in.
