AI chatbots for agents: 2.42 seconds per search - does the math actually work?
Dakota State University and Safety Insurance built "Axlerod," a chatbot that helps independent auto agents pull basic policy details. In testing, it shaved an average of 2.42 seconds off search-oriented tasks like policy numbers, AutoPay eligibility, covered vehicles, and billing plan.
On paper, that's small. In practice, it depends on your volume, your existing software stack, and whether saved seconds turn into real outcomes like more calls completed, faster service, or higher retention.
How the system works (quick context)
Axlerod is a lightweight wrapper around Google's Gemini 2.5 Pro. It connects to internal carrier data through a middleware proxy (LiteLLM) that translates OpenAI-style requests to Google's API and handles Vertex AI authentication. A microframework called "Smoltalk" coordinates tools and agentic behavior.
When a query returns more than five results, it asks for clarification to narrow the search. In testing, the team reported a 93.18 percent success rate and observed no outright failures.
The numbers you actually care about
- Baseline: 7.55 seconds per lookup without the chatbot
- With chatbot: 5.13 seconds per lookup
- Time saved: 2.42 seconds per lookup
- Cost per chatbot call: $0.0075
- Assumed agent pay: $80,000/year ≈ $0.01 per second
At 20 lookups a day, that's roughly 48.4 seconds saved daily and about $121 of annual "labor value." That's not a budget line you can actually cut; it's slack time that might translate into better throughput-or just a shorter pause between calls.
At 80 lookups a day, the math looks better: daily cost ≈ $0.60, daily savings ≈ $1.936, net ≈ $1.336, or a 222 percent daily ROI. The break-even on a per-search basis is favorable because $0.0242 of time saved per query is greater than the $0.0075 query cost.
What agents say from the trenches
Independent shops already lean on platforms like EZLynx to surface carrier data on demand, which removes a large chunk of routine lookups. Many remaining requests are messy: cross-carrier inconsistencies, nuanced policy language, and edge cases where a chatbot can stumble or hallucinate.
That's why agent-facing deployments make more sense than customer-facing ones. Keep humans in the loop to validate outputs, especially in high-stakes situations.
Where this fits (and where it doesn't)
- Good fit: high-volume service desks, call centers, or teams that bounce across multiple systems and screens.
- Good fit: carriers and MGAs with strong internal data access and consistent schemas.
- Weaker fit: low-volume agencies already covered by AMS automations where remaining questions are complex and context-heavy.
Implementation checklist for insurance leaders
- Integrations: Secure read access to policy, billing, and document systems with audit logs.
- Guardrails: Clear system prompts, citation requirements, and a default fallback to the native screen.
- Disambiguation: Require clarifying questions when results exceed a threshold.
- Metrics: Track success rate, time per task, error rate, and deflection to human lookup.
- Cost controls: Set per-user query budgets and alert on spikes.
- QA: Red-team prompts for hallucinations on exclusions, limits, and state-specific rules.
- Training: Teach agents what the bot is good at, where it's weak, and how to escalate.
- Compliance: Authentication, PHI/PII handling, retention policies, and audit trails.
Simple ROI calculator
Daily net benefit = Searches per day × [(2.42 seconds × $0.01) - $0.0075]
- Per-search savings: ≈ $0.0242
- Per-search net after cost: ≈ $0.0167
- At 80 searches/day: ≈ $1.336/day net
The catch: "net benefit" only matters if reclaimed time becomes more completed tasks, faster response times, or fewer escalations. Decide up front how you'll convert seconds into outcomes you can measure.
Bottom line
Saving 2.42 seconds per lookup is small, but it compounds in environments with lots of repetitive searches and screen-switching. If your team averages 80+ quick pulls a day, the cost case works and the time adds up.
Treat the chatbot as a speed layer on top of your AMS and carrier portals, not a replacement for agent judgment. Pilot it on one high-volume queue, measure real workflow impact, then decide whether to scale.
Want your team fluent in agent-facing AI?
If you're planning a pilot or rollout, structured training shortens the learning curve and reduces avoidable errors. Explore role-based AI courses here: Complete AI Training - Courses by Job.
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