Case Study
Engineers spent 80% of their time answering questions a machine could handle.
Here's what happened when I didn't just throw AI at it.
response time
automated
A 3D printing manufacturer. Engineers — the most expensive people in the company — spent 80% of their time answering standard queries. Three days until a response reached the customer. Three days where no one worked on what they were actually hired for.
The obvious fix: throw AI at it, done, next project. Works too. Until the first audit.
Instead: risk assessment. Governance structure. An agentic AI system, integrated into existing company systems — but not live right away. Shadow mode first: the system ran in parallel, answers were reviewed before anyone had to trust them. Weeks, not hours. Because trust doesn't come from demos. It comes from transparency and data.
The result: 3-second response time. 80% of support queries automated. Engineers back on real work. And a governance structure that carries the operation through any audit.
Shadow mode first. Prove it. Then scale. Not the other way around.
Implementation risks to plan for.
Before writing any code, these are the risks I map and the strategies I choose for each. Technical, operational, and human.
Risk Analysis & Mitigation
Technical
Hallucination
Reduce
RAG enforcement, fallback logic, output validation
Operational
Error in automation
Avoid
Hard guardrails, API checks, human-in-the-loop gates
Ethical / Social
Fear of replacement
Accept & Reduce
Incentivization, AI trainer roles, transparent communication
Guardrails — why clear rules mean speed, not brakes
Clear rules let the system run fast and safe. Without guardrails, “fast” is just “risky.”
How we got there.
Problem Recognition
The most expensive people in the company spent 80% of their time on work a system could handle. Three-day response delays. That's not an AI problem — it's a business problem.
Risk Assessment
Classify the use case under EU AI Act risk categories. Map data flows, GDPR implications, and governance requirements. Before writing a single line of code.
Governance Design
Approval processes, escalation triggers, human oversight points. The structure that makes fast, safe decisions possible.
Shadow Mode
I built the agentic AI system and ran it in parallel for weeks. It answered — but nobody had to trust it yet. Every output was reviewed. The first week was rough: about 60% accuracy. By week three, 95%. That's why you don't skip shadow mode. Not as exciting as 'we shipped in a weekend.' But it works.
Validation
Compare shadow outputs against human answers. Measure accuracy, find edge cases, fix what breaks before it matters.
Scale
Go live with confidence. Engineers back on real work. And a governance structure that survives every audit.
If your team is still the help desk, let's talk.
hi@lexi-energy.com →