SOME OF OUR LATEST WORK

What working automation looks like

Real workflows built for real businesses. Every case study includes the specific problem, the system we built, and the measurable outcomes.

These aren't concept projects or hypotheticals. These are automation systems running in live businesses, saving hours every week and creating measurable capacity.

AUTOMATED LEAD QUALIFICATION & ENRICHMENT

Challenge:
Lead qualification was completely manual. Every Apollo CSV export required hours of research: visiting websites, checking for automation opportunities, determining if prospect was legitimate business vs. spam. Senior staff time consumed by research that blocked actual client work.

Specific Pain Point:
Exporting 50 prospects from Apollo meant spending 3-4 hours manually visiting websites, taking notes, scoring fit. By the time research was done, momentum was lost. Hot leads went cold while buried in research backlog.

What We Built:
Automated lead enrichment and qualification pipeline integrated with Apollo exports and Google Sheets.

The Workflow:

  1. Apollo CSV export uploaded to Google Drive triggers automation

  2. System deduplicates against existing database (prevents re-processing)

  3. Automated website scraping extracts company info and platform detection

  4. AI analysis (Claude API) scores each prospect:

    • Automation opportunity score (0-10)

    • Operational complexity assessment

    • Specific processes identified for automation

    • Readiness indicators (existing tools, team size signals)

  5. High-scoring prospects (7+) flagged for immediate outreach

  6. All data organized in Google Sheet with scores, analysis, and source URLs

  7. Slack notification for hot prospects ready for manual LinkedIn review

Technical Approach:
Google Drive webhook (not polling - 99.3% cost reduction vs. polling approach), n8n workflow orchestration, SerpAPI for website scraping, Claude API for intelligent scoring, Google Sheets for data storage, Slack for team notifications.

Implementation:
3 weeks from initial concept to production system. Included iteration on AI scoring prompts and deduplication logic refinement.

Results:

  • Research time per prospect: 2 minutes (down from 5-7 minutes manual)

  • Processing capacity: 50 prospects in under 2 hours vs. 4-6 hours manual

  • Consistency: Every prospect gets same thorough analysis, no shortcuts when tired

  • Quality: AI identifies automation opportunities human reviewers might miss

  • Senior staff time reclaimed: 15-20 hours per week during active prospecting

INTERACTIVE LEAD MAGNET AUTOMATION

Challenge:
Static PDF lead magnets weren't converting. Prospects downloaded guides but never booked calls. No way to personalize content based on prospect's specific situation. Follow-up was manual and inconsistent.

Specific Pain Point:
Creating personalized recommendations for every prospect wasn't scalable. Team knew personalization would improve conversion but couldn't justify the time per lead.

What We Built:
Two interactive assessment tools with automated personalized follow-up.

The Workflow :

  1. Prospect completes website form

  2. Form submission triggers Google Apps Script

  3. Script processes responses, calculates scores per section

  4. Identifies primary bottleneck based on lowest-scoring area

  5. Sends formatted data to n8n webhook

  6. n8n generates personalized email with specific scores and recommendations

  7. Email delivered within 5 minutes of form completion

  8. Results logged in Google Sheet for tracking and follow-up

Technical Approach:
Squarespace forms, Google Sheets, Google Apps Script for processing, n8n for orchestration, Claude API for content generation, Gmail API for delivery. Data structure optimization to handle nested form data properly.

Implementation:
4 weeks from concept to launch. Included significant iteration on:

  • Apps Script data formatting (had to debug double-nesting issue)

  • AI prompt refinement for personalized recommendations

  • Email template optimization for brand voice

  • Follow-up timing and messaging

Results:

  • Personalization at scale: Every submission gets customized response

  • Speed: 5-minute delivery vs. 24-48 hours for manual follow-up

  • Consistency: Brand voice maintained across all automated communications

  • Conversion tracking: Clear data on which assessment questions correlate with call bookings

  • Time saved: 30-45 minutes per lead that would have required manual response

AUTOMATED SALES CALL FOLLOW-UP

Challenge:
Post-call follow-up was inconsistent. Team would finish discovery call with good intentions to send personalized follow-up within 24 hours, but other priorities intervened. Prospects received either delayed generic follow-up or no follow-up at all.

Specific Pain Point:
Each call uncovered unique challenges and opportunities, requiring personalized follow-up. But crafting personalized emails for each prospect took 20-30 minutes. At scale, that's 3-5 hours per week just on follow-up emails.

What We Built:
AI-powered post-call follow-up system that generates personalized recaps based on call notes.

The Workflow:

  1. Discovery call completed, notes captured in standardized format

  2. Notes entered into simple form (or voice-to-text transcription processed)

  3. AI analyzes call notes to extract:

    • Prospect's primary challenges discussed

    • Specific opportunities identified

    • Next steps agreed upon

    • Timeline and urgency signals

  4. Generates personalized follow-up email in brand voice:

    • Acknowledges specific challenges prospect mentioned

    • References concrete examples from conversation

    • Proposes clear next steps

    • Includes relevant case study or resource

  5. Draft delivered to team member for review and minor edits

  6. One-click send via Gmail integration

  7. Follow-up logged in CRM with scheduled reminder for next touchpoint

Technical Approach:
Simple intake form, n8n orchestration, Claude API for intelligent email generation with context awareness, Gmail API for sending, CRM integration for logging. Prompt engineering to maintain brand voice and prevent generic AI-sounding content.

Implementation:
2 weeks from concept to production (launching next week). Includes:

  • Prompt refinement for authentic voice

  • Testing with real call notes from past conversations

  • Brand voice guidelines integrated into AI instructions

  • Review workflow for team approval before sending

Results:

  • Follow-up time: 5 minutes (review + send) vs. 20-30 minutes (write from scratch)

  • Consistency: Every prospect gets timely, personalized follow-up

  • Quality: AI remembers and references specific conversation details

  • Conversion improvement: Expected 15-20% increase from consistent same-day follow-up

  • Time saved: 2-4 hours per week per team member conducting calls

AUTOMATED PROPOSAL GENERATION

Challenge:
Every prospect needed customized proposal. Research their business, understand their needs, craft specific scope and pricing, format in brand template. Process took 2-3 hours per proposal. Senior team members were proposal-writing bottleneck preventing business development.

Specific Pain Point:
Good proposals require research and customization. Can't just use templates - prospects can tell. But custom work for every inquiry wasn't sustainable. Growing meant choosing between proposal quality and volume.

What We Built:
AI-powered proposal generation system that creates customized first drafts from intake forms.

The Workflow:

  1. Prospect completes detailed intake form:

    • Services needed

    • Current challenges

    • Timeline and budget

    • Company info and website

  2. System scrapes prospect's website for context

  3. AI analyzes industry, competitive landscape, and specific needs

  4. Generates customized proposal sections:

    • Executive summary addressing their specific situation

    • Scope of work tailored to services requested

    • Deliverables with timeline

    • Investment breakdown with options

    • Case studies relevant to their industry

  5. Applies brand template and formatting

  6. Creates draft in Google Docs

  7. Notifies team for review and refinement

  8. Team spends 15-20 minutes reviewing and customizing vs. 2-3 hours writing from scratch

Technical Approach:
Intake form (Google Forms or Squarespace), n8n orchestration, SerpAPI for website scraping, Claude API for content generation with industry context, Google Docs API for formatted output, template system for consistent branding. Structured prompts for each proposal section to maintain quality.

Implementation:
2.5 weeks from concept to production (launching next week). Includes:

  • Testing with real prospect scenarios from past 6 months

  • Iterating on prompt structure for each section

  • Refining tone to match brand voice

  • Building quality-check criteria for team review

Results (Projected based on testing):

  • Time per proposal: 20 minutes (review + customize) vs. 2-3 hours (write from scratch)

  • Volume capacity: 3-5x more proposals with same team

  • Quality: Consistent structure and professionalism, human-refined for authenticity

  • Speed: Same-day turnaround vs. 2-3 day typical delay

  • Senior staff time freed: 10-15 hours per week for actual client work

Patterns across all implementations.

These aren't isolated tricks - they represent a systematic approach to automation:

Start with Real Pain: Every project began with genuine operational bottleneck consuming hours of senior staff time. Not "what can AI do?" but "where are we actually stuck?"

Measure Before Building: Time-per-task documented before implementation. Baseline measurements make ROI clear and improvements undeniable.

Build for Real Tools: Integrated with actual systems already in use (Google Workspace, Squarespace, existing forms and workflows). No ripping out infrastructure that works.

Iterate on Quality: First versions worked but weren't great. Refinement of prompts, data structures, and workflows made them production-ready. Automation requires iteration.

Human + AI, Not AI Alone: Best results use AI for heavy lifting (research, draft generation, scoring) and humans for judgment (final decisions, relationship context, strategic customization).

Systematic Process: Every project followed same methodology: map current process, identify bottleneck, design automation, implement and test, measure results, refine. Repeatable approach creates consistent outcomes.

See what's possible for your operations

Every business has processes worth automating. The question is which ones create the most value.

These case studies started as our own operational challenges. We built solutions for ourselves, refined them in production, and now help other businesses implement similar systems.

Book a discovery call and we'll map your specific opportunities with the same systematic approach.

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