Automating High-Volume Workflows with Validated AI
Your sales team receives 200 inbound leads per month. Each one needs to be enriched with company data, matched against your ICP criteria, and turned into a personalized outreach message.
Or: Your recruiting team processes 300 applications per week. Each one needs to be parsed, matched against job requirements, and summarized for hiring managers.
Or: Your operations team handles 150 vendor submissions per quarter. Each one needs to be categorized, cross-referenced against compliance requirements, and turned into an evaluation brief.
The pattern is the same:
Intake — Information arrives in high volume (leads, applications, requests, alerts)
Enrichment — You need additional context from external sources
Output — You need to produce something tailored and accurate for each one
Most teams handle this manually. It's slow, inconsistent, and doesn't scale.
AI tools promise to fix this—but they create new problems. They hallucinate details. They produce generic output. They ignore critical requirements. They require heavy editing to fix inaccuracies.
I built an architecture that solves this. To prove it worked, I tested it on something I could fully control and measure: my own job search.
The Story
Job searching is a brutal version of this pattern. 100-300 LinkedIn alerts per month, each requiring 10-20 minutes of manual work—opening job pages, copying descriptions, rewriting resume summaries to pass ATS keyword filters. At scale, that's 30-65 hours of repetitive work every month.
The ATS reality makes this unavoidable. Unless you have a referral pathway, your application enters an Applicant Tracking System alongside hundreds of others. The ATS scans for keyword matches before a human ever sees it. A generic resume, no matter how strong your experience, gets filtered out.
This means job seekers face a miserable choice: submit the same resume everywhere and hear nothing back, or manually customize every application. Read the description. Identify the keywords. Rewrite your summary. Repeat 200 times.
I tried off-the-shelf AI resume tools. They failed in predictable ways—hallucinated skills I didn't have, invented responsibilities I'd never held, produced generic summaries that required constant editing. The insight that shaped everything:
Most AI resume tools optimize for matching—no matter what. They'll bridge the gap between your experience and the job description whether that bridge is real or completely fabricated.
That's a liability. If the AI says you have "extensive experience with Kubernetes" because the job description mentions it, and you've never touched Kubernetes, you've got a resume that will embarrass you in the first interview.
I needed a system that automated the entire workflow while enforcing a hard constraint: enhance what's real, never invent what isn't.
The Goal
Automate a high-volume, manually intensive workflow from end to end—with AI that's accurate enough to trust without editing.
The Strategy
I built a three-phase pipeline: Intake → Enrichment → Validated AI Output.
Phase 1: Automated Intake. Job alerts arrive as cluttered emails—marketing noise, tracking links, inconsistent formatting. A Make.com scenario monitors Gmail, extracts job listings from LinkedIn alerts, strips the noise, and writes clean structured data (role, company, location, URL) into Google Sheets. Every job enters the system instantly.
Phase 2: Automated Enrichment. LinkedIn alerts include a teaser, not the full job description. Phantombuster visits each job URL and scrapes the complete details—full description text, seniority level, employment type, industry, recruiter info. No need to manually open 100+ pages. Every job becomes a complete dataset.
Phase 3: Validated AI Output. This is where most AI fails. Instead of a single "write me a summary" prompt, I built a multi-stage reasoning engine:
AI extracts 125-150 keywords from the job description
Each keyword is classified against my actual resume (exact match, implied, or not present)
Make.com validates each keyword with regex checks against the source text
Only validated keywords pass through to summary generation
The model writes constrained to approved terminology only
A keyword only survives if it appears in the job description, exists in my resume, and passes validation. This list becomes the trusted source of truth. No improvisation. No embellishment. No hallucination.
The Tactics
Make.com workflow automation, Gmail API, Google Sheets, Phantombuster for web scraping, GPT-4.1 with structured prompts, regex validation layer, multi-stage AI reasoning with constraints.
The Results
| Metric | Before | After |
|---|---|---|
| Manual work per job | 10-20 minutes | <2 minutes |
| ATS keyword alignment | ~30% | ~70% |
| Hallucinations/errors | Frequent | Zero |
| Monthly time investment | 30-65 hours | 3-6 hours |
The Results
METRICBEFOREAFTERManual work per job10-20 minutes<2 minutesATS keyword alignment~30%~70%Hallucinations/errorsFrequentZeroMonthly time investment30-65 hours3-6 hours
90% reduction in manual effort. Keyword alignment more than doubled. Zero fabricated skills—every claim backed by real experience.
Why It Matters
| Workflow | Intake | Enrichment | Validated Output |
|---|---|---|---|
| Lead Processing | Inbound form submissions | Company data, ICP scoring | Personalized outreach |
| Recruiting | Applications via ATS | LinkedIn profiles, portfolios | Candidate summaries |
| Vendor Management | RFP submissions | Compliance verification | Evaluation briefs |
| Customer Onboarding | Signed contracts | CRM history, usage data | Personalized plans |
| Compliance | Incident reports | Policy database, regulations | Case summaries |
The job search was the test environment. The architecture is the product.
This same pattern applies anywhere high-volume intake meets a need for personalized, accurate output:
WORKFLOWINTAKEENRICHMENTVALIDATED OUTPUTLead ProcessingInbound form submissionsCompany data, ICP scoringPersonalized outreachRecruitingApplications via ATSLinkedIn profiles, portfoliosCandidate summariesVendor ManagementRFP submissionsCompliance verificationEvaluation briefsCustomer OnboardingSigned contractsCRM history, usage dataPersonalized plansComplianceIncident reportsPolicy database, regulationsCase summaries
The methodology stays the same:
Automate intake so nothing falls through the cracks
Enrich automatically so you have complete context
Constrain AI with validation so output is accurate, not just plausible
The combination matters. Automation eliminates the repetitive steps. Validation guardrails make the AI output trustworthy. One without the other doesn't get you there.