Customer success, defined. At webAI, success means giving companies the ability to create their own AI, run it, and make it stronger over time.
First principles as our guide. We anchor our work in foundational ideas that shape how we think and build.
Make AI economically viable. Reducing hardware costs and optimizing models ensures AI is affordable to explore and scale.
Make AI a team sport. Navigator opens AI development to entire organizations, turning it into a collaborative process rather than a specialist bottleneck.
Empower customers to build moats. Owning experts, data, and interaction feedback creates defensibility that compounds over time.
The result. AI becomes a core capability you own, improve, and differentiate with—not just another line item or experiment.
From our First Principles Series: Essays on the foundational ideas and practices that guide how we build at webAI
At webAI “customer success” means something very specific: Our customers succeed when they can create their own AI that solves real business problems, then run and manage it over time to win in the new AI world.
That definition of success is the North Star guiding every design decision we make. And it requires solving three hard problems along the way. We’ve gone so far as to codify these into product principles:
These aren’t the only principles we build by, but they are important ones. Let’s unpack what we mean by each in turn.
Right now, AI is expensive. Sometimes prohibitively so. High demand has made GPUs scarce and costly, both on-prem and in the cloud. Companies looking for access to high-end hardware often face long wait times and steep financial commitments in either environment.
The result: many AI solutions fall outside the economically viable range for exploration and deployment. Consider Khan Academy’s educational chatbot “Khanmigo,” which they estimated costs between $5 and $15 per user per month in compute alone. Before you account for any of the other costs of running a service at scale, that math breaks most subscription models.
This is the tension every company faces: balancing competitiveness with the reality of AI’s variable costs and premium pricing. If compute is too expensive, you either price yourself out of the market or run unprofitable services.
We address the economics head-on. The webAI platform supports hardware that delivers dramatically better cost-performance, like Mac Studios and Mac minis powered by Apple’s M-chips. Customers can get substantially higher performance per dollar on Apple hardware compared to Nvidia, giving them enterprise-grade compute without enterprise-grade pricing. These machines are optimized for AI workloads and provide excellent performance-per-watt efficiency.
We extend that advantage by enabling on-device execution on iPhones and iPads, offloading inference costs directly onto user devices. This can eliminate a significant portion of server-side spend.
And we do all this without compromising quality. Small-footprint experts, combined with proprietary technologies like EWQ (which reduces memory requirements by 18–22%) and webFrame (which partitions models across multiple systems), allow customers to run powerful models on cost-efficient hardware.
Economics shouldn’t be the blocker for innovation. Making AI viable at scale is the first condition for customer success: it gives companies the ability to explore, iterate, and grow AI that truly works for their business.
The next barrier isn’t just about cost. It’s about talent.
To be successful with AI, companies have to align a lot of moving parts across the business with the AI-powered products they’re creating. AI fundamentally generates flywheels that either create or destroy value. Which means making AI a team sport isn’t just about “enablement” or “empowerment.” It’s about matching outcomes to technology and solution design—getting the right people involved at the right time so the flywheel spins in your favor.
Historically, AI work has been the domain of specialists fluent in calculus, linear algebra, information theory, and a stack of other technical fields. Most companies don’t have these experts. Or if they do, they have very few. And with studies suggesting that more than eight out of ten AI use cases never make it to production, relying solely on scarce, expensive specialists is a poor ROI.
That’s why we built Navigator: to broaden the base of who can participate in AI and to align their contributions with the outcomes that matter.
Navigator provides a visual, no-code environment with prebuilt building blocks and templates. Business users can prototype at the speed of thought, quickly validating which ideas have potential and which don’t. This accelerates learning and fuels the flywheel of experimentation and refinement.
And it doesn’t stop at prototypes. Some solutions can go straight from Navigator to production. Others can be passed to ML specialists for performance tuning. Navigator also includes advanced controls, APIs, and SDKs so technical users can create custom AI elements and integrate directly into existing apps and workflows.
This combination keeps you from bottlenecking on a handful of experts. Instead, your whole organization contributes: business users test ideas, engineers productionize them, and the loop continues. That collaboration ensures the technology evolves in lockstep with business goals—keeping the AI flywheel compounding value, not eroding it.
When AI becomes a true team sport, customer success stops depending on a few specialists and starts depending on the collective creativity and alignment of the entire organization.
The ultimate goal of customer success isn’t just to adopt AI. It’s to build defensible advantage with it.
By creating their own experts, companies create unique and defensible intellectual property, rather than risking their data and workflows being commoditized into someone else’s product or model.
Features like Language Q&A give customers the tools to manage feedback loops that strengthen this moat over time. After an expert is deployed, users can provide feedback on responses; thumbs up, thumbs down, or open comments. That feedback is reviewed in Navigator to identify opportunities: adding new documents for training, refining existing experts, or spinning up new ones to handle specialized domains. With each cycle, the expert becomes sharper and more attuned to the company’s needs.
Contrast that with third-party AI services. At best, your feedback goes into improving their product. At worst, you’re locked out of the process entirely. Either way, your input strengthens their moat, not yours—and leaves you more dependent on their pricing and roadmap.
Owning your own experts also opens up a new layer of defensibility: the interactions themselves. Traditionally, a company’s moat was defined by its proprietary data—manufacturing guides, internal documents, customer records. That’s still true. But with AI, the way your people interact with that data becomes a new and equally valuable source of IP.
And this kind of defensibility is only possible because the experts live in your environment, not in someone else’s cloud. The local-first nature of webAI means you own not just the data and the interactions, but also the feedback loops, metrics, and levers of control through Navigator—capabilities that cloud-centric solutions keep for themselves.
Every question asked, every correction given, every feedback signal becomes part of an evolving record of how your organization works. And that record is just as defensible as the underlying documents, because it’s unique to your people and your workflows.
Imagine repair technicians working with a repair expert. The immediate value is in getting accurate answers. But the greater value is in the accumulated interaction history. Over time, the system can:
In other words: your IP isn’t just your manuals and datasets anymore. It’s also the interaction data generated as your teams work with those assets. And because the system keeps folding that interaction data back into itself, the moat compounds indefinitely.
The beauty of these feedback loops is that your experts don’t just get better, they get better in ways unique to your organization. That compounding advantage increases your competitiveness and can even open the door to monetization, where you provide solutions to others.
And because that knowledge lives with your experts, it doesn’t walk out the door when an employee leaves or stay trapped in a single department. The interaction data becomes a building block of institutional knowledge: Accessible across teams and time zones, persisting beyond org charts or communication bottlenecks. It turns individual expertise into organizational capability.
And this is what it means to become AI Native. Learning to tune the entire process—curating data, running evals, performing error analysis, and iterating with feedback—builds institutional muscle. But now that muscle isn’t just about managing data; it’s about harnessing the interaction data as well. Together, those two layers create an engine of continuous improvement that transforms culture and creates lasting defensibility—something no amount of “prompt engineering” can replicate.
At webAI, customer success means giving companies the ability to create their own AI, run it, and make it stronger over time.
That’s why our product principles focus on the three barriers that stand in the way:
Together, these principles ensure that AI doesn’t just become another line item or experiment. AI becomes a core capability you own, improve, and differentiate with.
That’s what winning in the new AI world looks like: affordable, collaborative, and defensible AI, built for and owned by your business.
