We Replaced a $4,000/Month SaaS Tool With a Custom AI Agent in 6 Weeks

A client came to us in January paying $4,200 per month for a workflow automation platform. It handled their lead routing, internal notifications, and some light data enrichment. They had been on it for two years and had customized it heavily.

The problem was not the price. The problem was that the tool had stopped growing with them. Every new requirement meant a workaround, a new integration, or a Zapier step bolted on top. Their ops manager was spending 6 hours a week maintaining a system that was supposed to save time.

We told them we could replace it in 6 weeks. They were skeptical. Here is what we actually did.

What the Tool Was Actually Doing

Before writing a single line of code, we spent three days mapping every workflow the platform ran. This step is easy to skip, and skipping it is how you end up rebuilding a mess.

What we found: the tool was doing about 14 distinct things. Four of them were genuinely complex. Ten were simple data moves that any decent script could handle. The client had been paying enterprise SaaS pricing to wrap those ten simple things in a pretty UI they barely used.

The four complex ones were the real problem. They involved classification logic, some light natural language parsing, and routing decisions that changed based on context. That is where the AI came in.

The Agent Architecture

We built a single AI agent that sat at the center of the workflow. Incoming data came in from three sources: their CRM webhook, a shared email inbox, and a nightly CSV export from their billing system.

The agent's job was to read each incoming item, classify it, decide what to do, and either act or escalate to a human. For a lead coming in from the CRM, it would check the company size, industry, and recent activity, then route to the right sales rep with a short briefing note. That briefing note alone saved each rep 10 to 15 minutes per lead.

For the email inbox, it handled tier-1 triage: tagging by urgency and topic, drafting a response for the ops manager to approve or send as-is, and flagging anything that looked like a contract or legal question for a human to handle fresh.

The billing export was the simplest part. The old tool was doing rule-based churn flags. We replaced it with a classifier that had better recall on accounts showing early warning signs, because it could read free-text notes from customer calls rather than just numeric fields.

What 6 Weeks Actually Looked Like

Week 1 was the audit and architecture doc. No code.

Week 2 we built the scaffolding: the ingestion pipeline, a lightweight admin dashboard so the ops manager could see what the agent was doing in real time, and a human-in-the-loop review queue for anything the agent was not confident about.

Weeks 3 and 4 were the core logic. The routing and classification pieces. We ran the agent in shadow mode, meaning it made decisions but did not act on them yet, and we compared its output to what the old system had done historically. It outperformed the old tool on routing accuracy within two weeks.

Week 5 was integration testing and getting the ops manager comfortable with the dashboard. This is the week most engineering teams rush or skip. We did not. If the person who owns the workflow does not trust the system, they will work around it, and then you have two systems instead of one.

Week 6 was cutover and monitoring. We kept the old system running in parallel for the first 10 days, then shut it down.

What It Cost and What They Saved

Our fee for the build was $28,000. That sounds like a lot until you compare it to $50,400 a year in SaaS fees, which is what they were on track to pay over the next 12 months.

The system runs on roughly $180 a month in infrastructure and API costs. That is it. They own it. When they need a change, they call us or their internal developer makes it, because it is just code with good documentation.

The ops manager got her 6 hours a week back. The sales team got better lead briefings. The churn detection caught two accounts in the first month that the old system had missed.

What This Does Not Mean

This is not an argument that SaaS is bad. Most businesses should be using off-the-shelf tools for most things. SaaS is fast, maintained by someone else, and usually the right call for standard workflows.

The signal that you might have outgrown a tool is not the price. It is when you are spending more time working around the tool than with it. When your ops person is the de facto system administrator for a product you pay someone else to build. When every new business requirement spawns a new integration instead of an update to the core logic.

At that point, a custom agent is not a luxury. It is the cheaper option.

If you have a workflow that has gotten slow or expensive to maintain, we are happy to take a look. Sometimes the answer is a small fix to what you have. Sometimes it is something bigger. Either way, the audit is where you start.

Reach us at simplyship.app.