Building an enterprise-grade AI agent isn’t like launching an app over a weekend hackathon. It’s more like a heart transplant: high stakes, deeply technical, and tailored to the unique anatomy of each organization. You need to precisely diagnose the problem, understand the client’s internal systems, work across stakeholders with competing incentives, and then execute flawlessly.
And like a transplant, most of the work happens before the main event (deployment go-live). It’s weeks of mapping legacy systems, aligning with business goals, and understanding edge cases that don’t show up in documentation.
At PolyAI, we’ve been building enterprise AI agents for a long time. And if there’s one thing we’ve learned, it’s that traditional software development and PLG models don’t work in these environments.
Here’s why:
- They assume clear problem statements. But in reality, goals shift mid-flight.
- They assume transactional workflows. But enterprise success depends on trust, context, and iteration.
- They rely on static scoping. But the real issues often only surface once the system is live.
In a typical SaaS model:
- A non-technical project manager scopes “requirements”, writes up a hundred user journeys and tickets.
- Engineers pick up 20 tickets, polish them during a four-week sprint.
- Rinse and repeat.
However, in enterprise AI, real problems don’t come neatly packaged and presented. The “ticket” to be solved might not even describe the problem precisely, but rather a symptom that requires deeper investigation.
When things break, you need people who can debug a flaky API, reroute a flow, and explain the business impact to a stakeholder, all in the same call.
That’s why we built a different model.
We offer clients a team of forward deployed AI engineers, product solution managers, and dialogue designers – a multidisciplinary team that works directly with customers, end to end. They’re builders, analysts, and problem solvers rolled into one. They don’t just ship features, they unblock real outcomes:
- Identify high-ROI solutions
- Integrate and test across fractured legacy systems
- Tune and iterate based on real-world usage and feedback
It’s definitely not easy, but it works. Over time, it compounds. Because we’re not just closing tickets, we’re embedding into the organisation, building trust, and feeding product learnings directly back into Agent Studio, our platform, so that all users can benefit from continuous improvement.
Joe Schmidt said it well in his latest article: service-led growth isn’t just a deployment tactic, it’s a strategy. We didn’t set out to follow any playbook. We just did what made sense for the customer. But looking back, this is the Palantir playbook: deep integration, long-term value, and continuous co-creation with the customer.
That feedback loop, between frontline deployment and platform product, is key.
At PolyAI we empower enterprises to deploy AI agents that do the work of thousands, and actually deploy them.
Want to know more about building an AI agent that really works? Request a demo today.
If you’re excited about solving hard problems at the intersection of AI, design, and enterprise deployment, we’re hiring. We’re scaling our team across multiple locations.
poly.ai/careers
Let’s build agents that do the work of thousands, and actually deploy them.