OpenAI is betting that winning the legal industry will require more than putting powerful AI models in the hands of lawyers. The company is increasingly looking to the people who can help law firms and corporate legal departments integrate those models into the way they actually work.
That approach is taking shape through a new partnership with Telon, a London-based startup that embeds former lawyers inside legal teams to help customers get more value from the artificial intelligence tools they already have.
Telon said Tuesday that OpenAI has named it a “select partner,” giving the startup a role in working with shared customers to configure models, develop prompts, build AI agents and train lawyers to use the technology.
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The arrangement provides an early glimpse into how OpenAI could expand its presence in professional services: by building an ecosystem of specialists around its models rather than relying solely on direct software sales.
Telon was founded in June by Lewis Bretts, a former trial attorney who previously built PwC’s legal practice. The startup employs so-called “legal engineers,” primarily former lawyers who work directly with law firms and corporate legal departments to help them deploy and use AI.
The model resembles forward-deployed engineering, but with legal expertise at its center. Instead of simply handing a customer access to an AI platform and leaving its lawyers to figure out how to use it, Telon works inside the organization to adapt the technology to specific workflows.
The idea is being embraced because adoption, rather than access, is increasingly becoming one of the biggest challenges in enterprise AI.
OpenAI launched its Partner Network in June as part of an effort to make it easier for consultants and other professional services companies to deploy its models and software inside organizations. The company has said it wants to certify 300,000 consultants by the end of 2026.
The expansion of that ecosystem reflects a broader shift in the enterprise AI market. The first phase was largely about getting companies to buy licenses, run pilots, and experiment with general-purpose models. The next phase is likely to be determined by whether employees use those systems consistently and whether companies can translate that usage into measurable productivity gains.
For law firms, that challenge is particularly pronounced.
Legal work is highly specialized, heavily dependent on existing processes and subject to professional and confidentiality requirements. A lawyer may have access to a sophisticated AI model but still use it only for relatively simple tasks if the system is not integrated into research, drafting, document review, knowledge management and other established workflows.
Bretts said the biggest challenge in selling AI to law firms is not closing the initial deal, but getting lawyers to actually use the technology.
That has created an opening for companies such as Telon.
Traditional professional services firms have spent decades helping enterprises implement major software systems. A company might hire McKinsey, Deloitte, or another consulting firm to introduce a Salesforce database or overhaul a business process. Those projects could take months or years and eventually reach an endpoint.
AI adoption is less static.
New models are released frequently, and each improvement can expand what applications built on top of them can accomplish. A company that successfully completes an AI pilot can therefore find itself with a rapidly changing technology stack only months later. That creates a recurring implementation problem. Organizations may have paid for AI licenses and demonstrated that the technology works, yet employees may continue using only a small portion of its capabilities.
Telon is betting that law firms will pay for specialists who can continuously close that gap.
The startup has already grown to 30 employees since its June launch and has raised capital from Zach Posner’s LegalTech Fund. Bretts declined to identify Telon’s customers or disclose the financial terms of its agreement with OpenAI.
OpenAI’s Legal Ambition Meets a Specialized Software Market
Legal technology presents OpenAI with a difficult competitive question. If the company provides increasingly capable models, why should a law firm buy directly from OpenAI rather than from a specialized legal-technology provider that has built an entire product around the way lawyers work?
Specialized legal software companies can package AI around specific tasks, proprietary workflows, and industry-specific interfaces. Their advantage is not necessarily the underlying model. It is the layer between the model and the professional using it.
Telon offers OpenAI another way to address that problem.
Rather than trying to replicate every piece of legal expertise and workflow software itself, OpenAI can work through partners that understand the customer environment and can translate the capabilities of its models into practical applications. The approach could become increasingly important as the AI market moves away from a simple competition over model benchmarks.
The value of a frontier model is ultimately constrained if customers cannot integrate it into their operations. For OpenAI, therefore, distribution now means more than putting ChatGPT or an API in front of a customer. It also means creating an implementation network capable of adapting the technology to specific industries.
The legal sector is a useful test case because it combines high-value knowledge work with unusually demanding requirements around accuracy, confidentiality, and professional judgment. It also illustrates why AI adoption may create a new layer of professional services around the technology itself. As models become more capable, companies may need specialists not simply to install AI, but to continuously redesign workflows around capabilities that did not exist when the original implementation began.
That is a different consulting model from traditional software deployment.
It also gives OpenAI a potential answer to one of the major issues facing model companies as competition intensifies: how to capture more of the economic value created by AI without having to build every application themselves. If the underlying models become increasingly commoditized, the companies that control distribution, workflow integration, and customer relationships could capture a larger share of enterprise spending.
Telon is still a very young company, so its partnership with OpenAI is not evidence that the model has been proven at scale. But the relationship points to a broader direction in enterprise AI. The next competitive battleground may not be simply who has the most capable model. It may be who can get those models embedded deeply enough into professional work that customers cannot easily operate without them.
The larger opportunity lies in the gap between buying AI and actually using it. OpenAI is increasingly building an ecosystem designed to make sure that gap does not remain someone else’s business.