Diagnose the bottleneck
We start with real execution drag: slow response loops, broken handoffs, inconsistent quality, and reporting blind spots.
→About
Practical AI implementation for real operations
The goal is not “more AI.” The goal is stronger execution: less manual work, better customer response quality, and clearer KPI visibility your team can actually use.
Business-first architecture
Workflow clarity before automation
Maintainable handoff by default
↓ 42%response lag in triage flows
↑ 18%lead booking conversion
↓ 31%launch rework overhead
↑ 24%SLA adherence reliability
Our approach
We use a simple, repeatable model so AI improves operations without creating fragile complexity.
Design the workflow
Before automation, we define owners, SLAs, handoff rules, and acceptance criteria so the system behaves predictably.
→Apply AI where it pays
We target repetitive, high-volume, low-judgment work: triage, classification, drafting, summarization, and routing support.
→Measure, harden, hand off
We validate results against KPIs, tighten controls, and deliver documentation + playbooks so your team owns the outcome.
Business-firstOutcomes drive every build decision.
Operator-friendlyClear interfaces and practical guardrails.
Governed rolloutQA, escalation paths, and change control.
What we track to prove impact
Nine practical views we use to evaluate system performance across lead ops, support, delivery, and nurture.
Lead Response Time
Hours to first meaningful response
Result: 4.1h → 1.9h average response time
Lead Stage Conversion
Inquiry → Qualified → Booked
Inquiry 72%Qualified 55%Booked 36%
Support SLA Adherence
Within SLA vs at-risk vs breached
Within 74%Risk 18%Breach 8%
Ticket Aging Curve
Open tickets by age bucket
Goal: shift mass into 0–24h bucket
Project Milestone Health
Plan confidence by phase
ScopeBuildQALaunch
Defect Burndown
Open defects across sprint
Target: zero critical defects at go-live
Email Funnel
Sent → Opened → Clicked
Sent 100%Open 41%CTR 8.7%
Reply Velocity
Reply rate improvement by iteration
Observed: +11% quarter-over-quarter lift
Automation Coverage
Manual vs AI-assisted task split
Lead Ops 61%Support 75%Nurture 83%
What changes after implementation
Before
- Leads wait too long before first response
- Support quality depends on individual heroics
- Handoffs get lost between teams
- Executives lack a single KPI source of truth
After
- Faster, prioritized responses with routing discipline
- More consistent support outcomes and SLA reliability
- Clear ownership and escalation pathways
- Operational dashboards tied to business decisions
Who we are best for
Good fit if you want:
- Operational systems, not just strategy slides
- Measured outcomes tied to real KPIs
- A partner who builds and documents
- Incremental rollout with governance controls
Not the best fit if you want:
- “AI for AI’s sake” without process discipline
- A one-click magic solution with no team ownership
- Unmeasured experimentation with no acceptance criteria
- Opaque implementation with no handoff artifacts
Engagement model
Straightforward advisory + build partnership with practical weekly momentum.
$150/hr
Transparent hourly pricing with scoped milestone checkpoints
Typical 4–6 week delivery
- Week 1: diagnostics + baseline metrics
- Week 2: workflow design + acceptance criteria
- Weeks 3–5: implementation + QA + iteration
- Week 6: launch support + runbooks + handoff
What you receive
- Operational architecture mapped to outcomes
- KPI dashboard views for weekly leadership review
- AI decision-assist playbooks for frontline teams
- QA and governance checklist
- Documentation set for internal ownership
Common first systems: lead intake triage, support prioritization, delivery governance, and email nurture optimization.
Want to see what this would look like in your business?
We can map one priority workflow, define KPI targets, and recommend your first practical AI sprint.
How can AI help your business? Let’s chat.
Fast response with recommended first steps.