The Algo & Art approach to getting AI into production

Getting AI into production sounds like it should be straightforward. Build an agent, test it, deploy it. But that's the research version. The production version is harder because you're dealing with real data, real systems, real consequences when something breaks.

We've learned a lot from shipping systems that actually have to work. Here's the approach.

Start with the process, not the technology

Most teams think backwards. They start with the question: how do we use AI here? We start with the question: what's actually broken about how you run this now?

An AI agent is the answer to a specific problem. But if you don't understand the problem deeply, you'll build the wrong agent.

We spend the first two weeks just understanding your process. Not asking high-level questions. Sitting with your team, watching how they work, understanding where it's hard, where it fails, what matters.

Then we come back and say: "If we automated X, you'd save Y time and avoid Z problems." That's the business case. If the business case is solid, then we think about the technical approach.

Design for failure first

The best AI systems aren't built to handle the happy path. They're built to handle everything else.

What happens when the data is malformed? When there are multiple valid decisions? When the agent should escalate instead of acting? When the business rules change?

We design all of that before we write the first line of code. We map the edge cases, the escalations, the fallbacks. We know what the agent will do in the weird situations before we build it.

This is the work that separates a system that works 85% of the time from one that works 98% of the time.

Build for observability

An AI agent that you can't see is a system you can't trust. We build observability into the system from day one.

Every decision the agent makes, we log it. Why did it approve that invoice? Why did it escalate that case? What data did it see? Every decision is traceable. Every failure can be understood.

This is boring work. It doesn't impress anyone. But it's what lets you trust the system and understand what's happening when something goes wrong.

Integrate with your systems early

A lot of teams build the AI agent in isolation and then try to wire it into their systems at the end. That usually reveals a bunch of integration problems that take weeks to solve.

We integrate early. Week two, we're hitting your API. Week three, we're reading and writing real data. We know what your systems actually do, not what you think they do, and we can plan around their limitations.

Test with your data, not ours

The most useless test is running the agent on clean, carefully prepared data. The useful test is running it on your actual data, with all the messiness and edge cases that come with it.

We get your data early. We anonymize it if needed. We run the agent against it and see what breaks. A lot of things break in ways you didn't expect. That's the point. Better to find them in staging than in production.

Plan the rollout carefully

You don't go from zero to 100% autonomous on day one. You go to 20%, then 50%, then 80% as the system proves itself.

Week one in production, the agent handles 20% of volume. Your team reviews everything and gives feedback. We make adjustments. Week two, we bump to 50%. The agent is showing us edge cases we didn't anticipate. We tighten the rules.

By week four, we're at 80% autonomous and only the genuinely hard cases go to your team. By week eight, it might be 90%.

This rollout is slow on purpose. Each stage teaches us something. Each stage reduces the risk of a catastrophic failure.

Train your team through the process

Your team isn't watching. They're participating. We're explaining what the agent is doing. We're showing them how to read the logs. We're teaching them what to do if something breaks.

By the time we wrap up, your team could run the system without us. They might choose not to, but they could.

Monitor relentlessly for the first month

After launch, we're watching closely. We're reading logs. We're asking your team what they're seeing. We're looking for patterns in what's working and what's not.

A lot of surprises show up in the first month. Your test data didn't perfectly mirror production. A certain type of case comes up that we didn't anticipate. The downstream system behaves differently than we expected.

We catch those things and fix them while the system is still new.

Define "done" upfront

Before we start, we agree on what success looks like. Not vague ("the system should be reliable"). Specific ("by week eight, it's handling 85% of cases without escalation, taking less than 30 seconds per case, and your team is saving 15 hours per week").

Then we measure against those goals. If we're hitting them, we're on track. If we're not, we understand why and adjust.

What makes this work

This approach works because it's honest about the difficulty. Getting AI into production is hard. It's not hard for the reasons people think it is, but it is hard. The systems that work are the ones that plan for the difficulty upfront instead of pretending it doesn't exist.

It also works because we've done this a lot. We know where things usually break. We know what to look for. We know how fast is too fast to roll out.

Ready to do this right

If you've got a process that needs automation and you want it to work reliably in production, that's what we do. We take the time to understand your problem, design the system properly, and stay with it until it actually works.

Let's talk about what you're trying to automate and what production-ready looks like for your business.