AI models are getting more capable by the month. But knowing what to actually do with them inside a large company? That’s a different problem entirely. Anthropic has decided that solving that problem is a business worth building, and now that business has a name.
Ode with Anthropic is a $1.5 billion AI implementation company that TechCrunch reports was launched in May as a joint venture between the AI lab and a group of investors including Blackstone, Hellman & Friedman, and Goldman Sachs. The company’s job is straightforward in theory: send elite AI engineers into enterprise customers’ offices and help them actually use the technology. In practice, it’s one of the harder problems in tech right now.
The launch follows a similar move from OpenAI, which stood up its own version called The Deployment Company. Both efforts reflect a growing belief among frontier AI labs that selling a model is only half the battle. The other half is making sure it gets used, and used well.
The idea for Ode came from Blackstone, not Anthropic. The private equity firm had been bringing in large consulting firms and smaller AI services boutiques to roll out AI across its portfolio companies. One of those smaller shops, an AI engineering startup called Fractional AI, stood out. Blackstone structured the joint venture around acquiring Fractional, which had previously been in an 11-month partnership with OpenAI before the acquisition ended that arrangement. Fractional’s co-founders, Chris Taylor and Eddie Siegel, now run Ode as CEO and chief technologist respectively.
Taylor does not understate the ambition behind the venture. “It’s pretty easy to imagine this as a trillion-dollar company someday if we execute well,” he told TechCrunch. “The key challenge of the business is how do you go through that phase of hyper growth without losing the emphasis on quality?”
Right now, Ode has 100 engineers on staff. The company works closely with Anthropic’s applied AI team to identify where the technology can have a real impact and then builds systems tailored to each organization. Anthropic’s internal team will continue handling strategic, mission-aligned deployments separately, according to a company spokesperson.
Ode will operate under a “Claude-first” principle, meaning it defaults to Anthropic’s tools, including features like Claude integration in Slack, wherever possible. But it is not locked in. If a client’s needs call for a rival product, Ode can use it. Siegel describes model selection as just one ingredient in a larger system that has to be carefully built.
“I think model selection matters, but it’s not where the majority of calories are spent,” Siegel said. “It’s like the choice of programming language when you build a piece of software. I would not define an enterprise transformation in terms of whether they choose Python or Java.”
The type of work Ode targets is deliberate. Taylor says the ideal client is one where the CEO is personally bought in, and where the AI project ranks among the company’s top one or two priorities. These are not proof-of-concept pilots. They are rewrites of core business processes or major product features that companies plan to ship in the next two years.
Taylor’s founding belief is that traditional, non-AI companies have a real shot at coming out ahead in the current AI moment, but only if they adopt the technology the right way. The problem is that doing it well requires rare talent.
“That requires top-caliber applied AI talent, which is not something most companies have,” Taylor said.
The team Ode has assembled is described by its executives as elite generalist engineers, more than half of whom are former founders. These are people who can handle a hard technical problem while also owning the outcome end-to-end. One Blackstone executive put it bluntly: a team of “grown-up” engineers, more like special forces than a large army of forward-deployed staff.
That positioning is also the company’s main challenge. Ode is trying to scale internationally while maintaining a boutique quality standard, which means constantly measuring the actual business impact of every implementation. In a market where top engineering talent is already scarce, growing that team fast enough to meet demand is a real problem. And Ode is not competing in a quiet corner of the market. It is going up against:
- OpenAI’s own Deployment Company, which is pursuing the same enterprise customers
- Accenture and Deloitte, both of which have built out their own forward-deployed AI teams
- Smaller specialist boutiques that were doing this work before the big labs got involved
Siegel is not particularly worried about finding enough people with the right profile. His argument is that it has never been easier to become an entrepreneur, and that the experience of trying to build something, find product-market fit, and move the needle on a real business is exactly what creates the skill set Ode needs. The pool of people with that background is growing, not shrinking.
Whether that turns out to be true at the scale Ode needs is still an open question. But the broader bet the company and its backers are making is clear: the next major AI race is not about who builds the best model. It is about who can put those models to work inside the world’s largest companies, reliably and at scale.
