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Real before-and-after resume tailoring.

8 examples across roles. Each shows the original summary, the AI-tailored version, the ATS score lift, and exactly what changed. Anonymized real-world output.

Fintech

Senior Backend Engineer

ATS 47 → 89 (+42)

Before

5+ years building scalable distributed systems. Strong in Python, Go, and AWS. Passionate about performance.

After AI tailoring

5 years building payment-rail infrastructure at scale — handled $4B/yr in transaction volume across 3 services. Python + Go primary, deep AWS (RDS, Lambda, EventBridge). Shipped the latency reduction (180ms → 42ms) called out in the JD.

What changed

  • •Replaced 'distributed systems' (generic) with 'payment-rail infrastructure' (the JD's vocabulary)
  • •Surfaced $4B/yr volume — was buried in a bullet on page 2
  • •Pulled the latency cut into the summary — JD specifically asks about latency optimization
  • •Removed 'passionate about performance' (filler recruiters discount)

B2B SaaS

Senior Product Manager

ATS 52 → 84 (+32)

Before

Product leader with experience launching features and managing cross-functional teams. Worked on enterprise SaaS products.

After AI tailoring

PM lead at a B2B SaaS company (Series C, 200 customers). Owned the activation funnel — drove trial-to-paid from 8% to 14% in two quarters. Led 4-engineer pod + 1 designer; partnered with sales on enterprise deal motion.

What changed

  • •Added concrete scale (Series C, 200 customers, 4-engineer pod)
  • •Quantified outcome (8%→14% trial-to-paid)
  • •Replaced 'cross-functional' with the specific functions (eng, design, sales)
  • •Removed 'product leader' (vague) for 'PM lead' (specific title)

Enterprise SaaS

Account Executive

ATS 58 → 91 (+33)

Before

Top-performing AE with strong track record of closing enterprise deals. Skilled in pipeline generation and stakeholder management.

After AI tailoring

AE at impress.ai — closed $4.2M in net-new ARR FY24, 142% of quota. Avg deal size $185K, sales cycle 90 days. Sold into HR tech buyers (CHRO, VP People). Salesforce + Outreach + Gong stack.

What changed

  • •Replaced 'top-performing' (subjective) with 142% quota attainment (verifiable)
  • •Added deal size + cycle length — recruiters scan for these numbers
  • •Named the buyer persona (CHRO, VP People) instead of 'stakeholders'
  • •Listed the actual sales stack — ATS reads this section heavily

Consumer app

Growth Marketing Manager

ATS 44 → 81 (+37)

Before

Marketing professional with experience driving growth across multiple channels. Strong analytical background.

After AI tailoring

Growth marketer at a 5M-MAU consumer fitness app. Owned paid (Meta + TikTok, $400K/mo budget) and lifecycle (Braze). Cut blended CAC 38% in 6 months by killing 3 underperforming channels and doubling down on 2.

What changed

  • •Specified the channels and budgets (Meta/TikTok, $400K/mo)
  • •Quantified outcome (CAC -38% in 6 mo)
  • •Showed thinking, not just doing ('killed 3 channels, doubled down on 2')
  • •Removed 'multiple channels' for the specific stack

Healthcare

Data Scientist

ATS 50 → 86 (+36)

Before

Data scientist with experience in machine learning and predictive modeling. Strong Python skills.

After AI tailoring

DS at a Series B health-tech startup. Built the patient-readmission risk model now serving 12 hospital systems — 0.83 AUC, deployed on SageMaker. Python, scikit-learn, and pandas primary; comfortable in PyTorch when needed. HIPAA-compliant pipeline experience.

What changed

  • •Named the actual model + outcome (readmission risk, 0.83 AUC)
  • •Added scope (12 hospital systems)
  • •Mentioned HIPAA — JD specifically required it
  • •Differentiated primary stack from secondary (Python primary, PyTorch when needed)

B2C marketplace

UX Designer

ATS 49 → 82 (+33)

Before

Product designer with passion for user-centered design. Experience across mobile and web.

After AI tailoring

Senior product designer at a 12M-MAU marketplace. Owned the seller-onboarding flow end-to-end — redesigned to cut drop-off from 47% to 28% (q3'24). Figma + Maze for testing; ran 6 user research sessions per quarter.

What changed

  • •Removed 'passion for user-centered design' (filler)
  • •Added measurable outcome (47%→28% drop-off)
  • •Specified scale (12M MAU) and tooling (Figma, Maze)
  • •Showed research cadence — JD asks about user research ownership

Logistics startup

Operations Manager

ATS 41 → 78 (+37)

Before

Operations professional skilled in process improvement and team management.

After AI tailoring

Ops manager at a 200-person logistics startup (Series B). Owned the warehouse-to-delivery handoff — cut order-to-delivery time from 4.2 days to 2.7 days by re-sequencing the pick-pack flow. Led an 8-person team across 2 fulfillment centers.

What changed

  • •Added scale (200-person Series B, 8-person team)
  • •Quantified outcome (4.2 → 2.7 days)
  • •Showed mechanism — recruiters want to know HOW, not just what
  • •Replaced 'process improvement' with the specific process (pick-pack flow)

Edtech

Career changer — Teacher → PM

ATS 31 → 74 (+43)

Before

8 years teaching high-school math. Looking to transition into product management.

After AI tailoring

8 years teaching — last 3 leading a 6-person curriculum redesign for the AP Statistics program (district adopted, ~1,200 students/yr). Strong in stakeholder management (parents, admin, district), data-driven iteration (3 successive curriculum versions based on test outcomes), and cross-functional execution. Career-pivoting to PM at an edtech company.

What changed

  • •Surfaced the team-lead + scope detail buried in description
  • •Reframed teaching skills using PM vocabulary (stakeholder mgmt, data-driven iteration)
  • •Made the pivot explicit (no recruiter has to guess)
  • •ATS jumped because the JD was looking for 'iterative product development' — the curriculum work counts

See your own before / after.

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