Case studies
Real projects. Measurable outcomes.
Anonymized snapshots showing what we built, how we shipped, and the results that followed. See the approach before you commit.
We can’t always publish client names. We can share clear, anonymized snapshots that show what we built, how we approached it, and what changed after launch—so you can self‑qualify quickly.
What a snapshot includes
Each snapshot is designed to help you self‑qualify quickly:
- The workflow: what we automated and why it mattered
- Constraints: systems, data, compliance, operational realities
- Approach: conversation/tool design, guardrails, and human handoff
- Architecture (high level): integrations, data flow, logging, QA
- Launch + iteration: monitoring, quality loops, phased expansion
If we talk, we can go deeper (call flows, architecture diagrams, prompts/guardrails) depending on your security requirements.
Browse snapshots
Below are a few common patterns we can ship quickly. If you don’t see yours, send us the workflow—we’ll propose a realistic first version.
Each snapshot includes
- The problem — what the client needed and why it mattered to their revenue.
- What we shipped — scope, systems, timeline.
- The outcome — measurable results and what changed.
Snapshots
These examples showcase our AI agent work. Website sprint case studies are coming soon—in the meantime, they demonstrate our approach to scoping, integration, and delivery.
Project snapshot: AI Intake Assistant for Sales + CRM Hygiene (anonymized)
An AI intake assistant captured lead details from emails/forms/calls, enriched and normalized fields, and routed opportunities to the right owner with clean context.
Outcome: The intake assistant improved data consistency and routing speed for standard inquiries, while keeping humans in control for ambiguous or high-value leads.
Project snapshot: AI Receptionist for Call Routing + Scheduling (anonymized)
An AI receptionist handled common inbound requests, routed to the right person/queue, and scheduled appointments when possible—while making it easy to reach a human.
Outcome: The receptionist pattern improved call handling consistency and reduced front-desk load for the supported intents—while preserving a clear human fallback.
Project snapshot: Voice Agent Triage for Customer Support (anonymized)
A scoped voice agent handled the most common support intents (status checks, basic troubleshooting, policy questions) and routed edge cases to humans with context.
Outcome: After launch, the system provided consistent triage and reduced human time spent on repetitive calls *for the supported intents*.
Ready to see results like these?
Our 14-Day Revenue Website Sprint delivers a modern website with AI chat and Salesforce integration. Tell us about your project and we'll scope it in one conversation.