Economic Dev.AI Automation
Supply SA cut vendor certification from 30–45 min of staff time to a self-serve flow.
A production AI assistant with deterministic branching logic compresses a 30–40 step certification process into 7–11 screens. Vendors complete certification without staff intervention; reviewers step in only on edge cases.
- ~34
- hrs staff time freed / month
- $11.5K+
- annual capacity recovered
Read the case study →Property Mgmt.Ops DashboardConcept Build
A concept build: replacing six spreadsheets with one live rental portfolio dashboard.
A relational Airtable base paired with a live Bolt dashboard, modeled to reclaim hours a week otherwise lost to manual tracking.
- ~6 hrs
- reclaimed / wk (modeled)
- ~310 hrs
- redirected / yr (modeled)
Read the case study →Knowledge BaseAI AssistantConcept Build
A concept build: turning a company's own documents into an assistant that answers in seconds.
A retrieval assistant over a company's document library. Files are chunked, embedded, and searched with a reranking step, so answers stay grounded in the source material instead of guessed.
- Seconds
- to a sourced answer vs. manual searching
- 1 drop
- a new file updates the whole knowledge base
Read the case study →Lead GenAI AutomationConcept Build
A concept build: qualifying, scoring, and nurturing every inbound lead before a human opens the record.
A no-code n8n system that qualifies each lead, saves the rest instead of discarding them, and hands every good one a personalized report plus a week of follow-up. Modeled to redirect ~25 hours a month from manual triage.
- ~25 hrs
- redirected / mo (modeled)
- $625/mo
- modeled capacity recovered
Read the case study →Outbound SalesAI AutomationConcept Build
A concept build: recovering the half of a lead scrape that arrives with no email at all.
Two connected n8n workflows turn a raw Apollo/Apify scrape into personalized cold email drafts. A two-tier lookup recovers the leads the scrape missed, and every attempt is free unless it lands.
- 2 tiers
- LinkedIn URL, then company domain
- $0
- cost per failed lookup
Read the case study →Social MediaContent EngineConcept Build
A concept build: turning an idle lead list into three platforms of content grounded in each lead's own site.
A two-part n8n system that scrapes every lead's site, turns what those companies actually say into five angles, and writes LinkedIn, Facebook, and Instagram versions in parallel once a human approves one.
- ~20 hrs
- research & drafting saved / batch (modeled)
- 3 platforms
- written in parallel from one approved angle
Read the case study →Outbound SalesAI AutomationConcept Build
Tested on 270 real leads: cold emails grounded in the lead's own site, with zero invented details.
Every lead's site is scraped before the model writes a word, so drafts reference something real instead of plausible fiction. A scrape that failed used to feed the model its own error message. That bug got fixed at the root, not papered over with a better prompt.
- 500→270
- raw leads cleaned to verified-email leads
- Zero
- invented details across the tested batch
Read the case study →Sales OpsAI AutomationConcept Build
A concept build: a finished discovery call becomes a branded PDF proposal with nobody re-listening to the recording.
Claude drafts four proposal sections from the call transcript itself, so it can't quote a number that never came up. A code node builds the HTML instead of a second AI call, which removed an entire category of failure at no added cost.
- 14/14
- nodes passed on an end-to-end test run
- ~6–8 hrs
- redirected / mo (modeled)
Read the case study →