Home/Results

Twenty-seven engagements. The numbers, with the stories behind them.

We measure two things: hours reclaimed per week and time-to-first-value. Every engagement gets a public-to-you dashboard from day one. Below: four of them, with permission.

42hr
Average weekly hours reclaimed per team after sprint 2
14d
Median from kickoff to first live workflow
93%
Of automations still running 6 months post-handoff
3.4x
Median ROI in month one across 27 engagements
Case studies

Four engagements, four shapes of problem.

Industries are accurate, names are anonymized except where the client gave us permission. Numbers come from the dashboards we build during the engagement.

HVAC distribution · 80 employees

Quote drafting agent for inbound RFQs

Tri-State Mechanical Supply

Problem
35-minute average quote turnaround was costing winnable deals — the customer who replies in 5 minutes wins more than the one with the best price.
Approach
Built an agent that parses RFQ emails, looks up pricing with margin rules, drafts a Quickbooks estimate, and waits for a sales rep to approve before sending.
Stack
n8n · OpenAI GPT-4o · QuickBooks API · Slack · Postgres
Duration
3-week pilot, then a quarter of expansion
47s
Avg quote time
+$184k
Net-new Q1 revenue
2 FTE
Redirected to field
11mo
Still running, untouched
Property management · 140 units

Maintenance request triage + dispatch

Westridge Properties (anonymized)

Problem
Residents were emailing, calling, and texting maintenance requests to four different inboxes. Average resolution time was 4.2 days; complaints were climbing.
Approach
One inbox. An agent that classifies, prioritizes (emergency vs scheduled), and dispatches to the right vendor with a photo and address. Resident gets a status update text.
Stack
Twilio · Anthropic Claude · n8n · Supabase · Stripe Connect (vendors)
Duration
6 weeks · then fractional
17hr
Avg resolution time
+38%
Resident NPS
92%
Auto-classification accuracy
6mo
Live, no rollbacks
B2B SaaS · 60 employees

Sales research agent in Linear

Confidential (Series B SaaS)

Problem
SDRs spent 2.5 hours per qualified meeting on pre-call research. New reps took 6+ months to ramp because no one had time to mentor them.
Approach
An agent invoked from Linear (where their AEs already live) that produces a structured brief: company moves, recent funding, news, top contacts, suggested angles.
Stack
Anthropic Claude · Linear API · Exa search · Clay enrichment · Notion
Duration
8 weeks
18min
Per-meeting prep time
4mo
New AE ramp (was 6+)
+22%
Discovery → demo rate
$0.41
Cost per brief
Accounting firm · 28 employees

Receipt & invoice OCR pipeline

Hartwell & Sons CPAs

Problem
Tax season meant 3 staff doing nothing but data entry from PDF receipts and invoices into QuickBooks. Slow, error-prone, and impossible to scale.
Approach
An ingest pipeline that OCRs anything dropped in a shared Google Drive folder, classifies it, extracts line items, and stages it in QuickBooks for one-click approval.
Stack
Google Drive API · Tesseract + Claude · QuickBooks · Postgres · Retool admin
Duration
5 weeks · then full handoff
2.5 FTE
Equivalent labor saved
0.4%
Error rate (was 2.1%)
94%
Auto-approve rate
14mo
Live, owned by their team
What we measure

Four numbers on every engagement dashboard.

01 / METRIC

Hours saved / week

Measured against a baseline we capture together in week one. Audited monthly.

02 / METRIC

Workflow uptime

SLO target: 99.5%. Pages an engineer if it drops, with a clear runbook.

03 / METRIC

Cost per task

Inference + infra cost amortized across a unit of work, so you can decide what’s worth automating next.

04 / METRIC

Eval pass rate

A regression suite specific to your workflow. Runs on every change. Pages if it drops more than 3%.

Let’s ride

Twenty minutes. One call.
Walk away with a plan either way.

We’ll look at one workflow you hate, sketch what an automation would look like, and tell you straight if it’s worth doing. No deck. No follow-up sequence.