Deep Dive · AI Agents · Pulse Software Solutions
Your business runs on decisions. AI agents make them faster. Every industry has workflows buried in emails, spreadsheets, and human memory. AI agents don’t replace your team; they amplify it. Here’s how we build them, and what they can do for you.
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The Problem
Most workflows are invisible until they break
Ask any operations leader where their team spends time. They’ll describe a chain of repetitive tasks: reading documents, matching data against records, sending follow-up emails, waiting for responses, escalating to the right person. Work that doesn’t require genius – but does require constant attention.
That attention is expensive. It’s also fragile. When people leave, the knowledge goes with them. When volume spikes, the backlog grows. When someone makes a judgment call at 4pm on a Friday, there’s no audit trail.
AI agents solve exactly this. Not by replacing human judgment, but by handling the repeatable, verifiable parts of a workflow so humans can focus on the parts that actually require them.
Real Case Study
What an AI Claims agent actually looks like
We recently built an AI Claims Agent for a marine insurance MGA. Their challenge was a familiar one: growing claim volumes, a small team, and a process that lived almost entirely in people’s heads. Here’s how the agent works in practice.
The Decision Framework at a Glance
Red, Yellow and Green with written reasoning behind every result
All docs verified, coverage confirmed, no fraud signals, amount within threshold
One document needs attention or a minor mismatch needs human confirmation
Coverage gap, fraud signals, above authority threshold, or bodily injury involved
The Methodology
How we build AI agents that actually work
The hardest part of building an AI agent isn’t the technology. It’s the discovery. Most organizations have never written down the rules their best people use every day. Before any code is written, we run structured discovery sessions to make that knowledge explicit.
“The classification rules? That’s all in everyone’s heads right now. We just know this is a cargo claim, this is a liability claim.”
Senior Claims Leadership, during discovery session
That’s not unusual. It’s the norm. The first job of an AI project is to turn implicit knowledge into explicit rules. Once you have those rules documented, you have something you can test, refine, and – eventually – automate.
We follow a four-phase approach with every client:
Discovery
Structured sessions with the people who actually do the work. 60+ questions across every workflow stage. We map what currently happens, not what the org chart says should happen.
Rule documentation
All implicit knowledge is turned into explicit, testable rules. Classification criteria. Document checklists. Escalation thresholds. This document becomes the agent’s instruction set and it belongs to the client.
Recommendation mode
The agent goes live in recommendation-only mode. Humans still make every decision. The agent surfaces information, flags risks, and explains its reasoning. Every override becomes a data point.
Calibrated automation
After months of validated recommendations, specific low-risk decision types can move toward auto-processing. Not all at once. Not blindly. With thresholds the team trusts because they watched the agent earn that trust.
Beyond Insurance
Where else do AI agents change the game?
The pattern is the same in every industry: there’s a workflow that requires reading documents, checking rules, making decisions, and communicating outcomes. If you can describe the rules, we can build the agent.
Classify incoming matters, extract key dates from contracts, flag missing clauses, route to the right practice group automatically.
Verify patient eligibility, check procedure codes against plan coverage, pre-screen for approval likelihood before submission.
Extract income and asset data from uploaded documents, verify against stated amounts, flag discrepancies for underwriter review.
Cross-reference shipping documents, flag Certificate of Origin mismatches, validate HS codes against customs requirements automatically.
Verify candidate documents, check compliance requirements by jurisdiction, trigger the right onboarding tasks based on role and location.
If your team reads documents, applies rules, and makes decisions, an AI agent can handle the repeatable parts. Let’s find out together.
Common Objections
Myths vs. reality
AI will replace our team
The agent handles document triage and data extraction. Your experts spend time on judgment and complex cases, the work that actually needs them.
We need thousands of training examples first
Modern AI agents run on rules and context, not training data. Five good examples and five bad ones are enough to start. The system learns from overrides as it operates.
It’ll make confident mistakes we can’t catch
Recommendation-only mode means every decision is reviewed by a human for months before any automation is considered. Confidence is earned, not assumed.
Our process is too complex / unique to automate
Complexity means there are more rules, not that rules don’t exist. The most “unique” processes usually follow the most consistent patterns once documented.
What workflow is costing you the most time?
We start every engagement with a discovery session: no technology, no demos, just your team and the process. If there are rules, there’s an agent. Let’s find yours.
