The honest comparison

Your current agency
runs on gut feel.
We run on evidence.

Traditional recruiters read LinkedIn profiles manually, form an opinion, and call it a shortlist. That's not analysis — it's pattern-matching with no audit trail. When the hire goes wrong, nobody can explain why it was approved.

9cv9 pool quality vs. market average
Market avg 67 9cv9 avg 84 / 100
Assessment area Traditional agency 9cv9 Recruiter Intelligence
Candidate assessment Recruiter reads each profile individually — subjective, inconsistent across reviewers. Six-dimension radar scores every candidate simultaneously: skills, seniority, trajectory, network, stability, prestige.
Skill evaluation "Has Python" means it's listed. No distinction between confirmed depth and keyword. Skill depth matrix distinguishes endorsed/featured depth from listed-only. JD gaps flagged automatically.
Risk detection Counter-offer and job-hopping risk surfaces during references — after submission. Hire risk profiler scores exposure, switch frequency, and tenure stability before submission. Zero high-risk passes unflagged.
Career trajectory Tenure length treated as a proxy. Plateaued careers look identical to accelerating ones. Momentum panel classifies each candidate as accelerating, steady, plateauing, or declining — based on promotions and scope, not just tenure.
Client briefing Manually written paragraph with recruiter's opinion. Not defensible if challenged. One-click AI briefing generator: structured summary, green flags, watch points, pool quality score, market benchmarking.
Shortlist speed 60% of recruiter time spent reading profiles. Hours per pool of 20 candidates. Full analysis runs in minutes. Recruiter time shifts from reading to deciding. ~40% faster shortlist decisions.
Skills match scored across 7 JD requirements
Career momentum: accelerating vs. plateauing
Counter-offer exposure flagged pre-submission
Pool quality benchmarked against market
Client briefing generated in one click
6 radar dimensions per candidate
Hire risk profiled before shortlist
40% faster shortlist decisions
Skills match scored across 7 JD requirements
Career momentum: accelerating vs. plateauing
Counter-offer exposure flagged pre-submission
Pool quality benchmarked against market
Client briefing generated in one click
6 radar dimensions per candidate
Hire risk profiled before shortlist
40% faster shortlist decisions
Head to head

Every step of the process, compared.

Traditional agencies haven't changed their operating model in 20 years. Here's what that actually means for your outcomes.

Traditional Agency Legacy model
9cv9 Recruiter Intelligence Data-backed
Candidate assessment
Recruiter reads each LinkedIn profile individually and forms a subjective impression. No structured scoring. Two recruiters reviewing the same person will produce different results.
Candidate assessment
Six-dimension radar chart scores every candidate simultaneously — skills, seniority, trajectory, network, stability, prestige. Consistent, repeatable, auditable.
6-axis radar · comparable across any pool
Skill evaluation
"Has Python" means it's listed on their profile. Endorsements, projects, depth of usage — all ignored. You're shortlisting based on a checkbox, not competency.
Skill evaluation
Skill depth matrix distinguishes confirmed depth (endorsed, featured in projects) from listed-only. JD gaps flagged in red. Clients see exactly where the shortlist is strong and where it isn't.
Confirmed vs. claimed · JD gap visibility
Risk detection
Job-hopping patterns and counter-offer risk typically surface during references — after submission. By then, you've damaged your client relationship.
Risk detection
Hire risk profiler scores counter-offer exposure, job-switch frequency, recency of last move, and tenure stability before submission. Your riskiest candidates are always visible first.
Pre-submission · 0 high-risk passes through
Career trajectory
Tenure length is treated as a proxy for quality. An 8-year career that plateaued 5 years ago looks indistinguishable from one that's been accelerating.
Career trajectory
Career momentum panel classifies each candidate as accelerating, steady, plateauing, or declining based on promotions, scope increases, and progression pattern — not just tenure.
Trajectory scoring · visible before shortlist
Client briefing quality
A manually written paragraph summarising each CV, with the recruiter's subjective impression. Clients who ask "why this shortlist?" get an opinion, not evidence.
Client briefing quality
One-click AI briefing generator produces a structured, data-backed candidate summary with green flags, watch points, pool quality score, and market benchmarking. Defensible by design.
AI-generated · pool quality score included
Shortlist speed
60% of recruiter time spent reading and re-reading profiles. A pool of 20 candidates takes hours to process. Decisions delayed. Mandates at risk.
Shortlist speed
Full analysis triggered in one click. All scoring, risk profiling, and AI insights run in minutes. Recruiter time shifts from reading to deciding.
40% faster shortlist decisions
The real cost

A wrong hire isn't just an inconvenience. It's a client relationship.

When you submit a candidate who job-hops, counter-offers, or simply wasn't the right skill fit — that's not a miss. That's an invoice you can't collect and a mandate you won't see again. Every row below is a real cost that data-backed recruitment eliminates.

The hire risk profiler and skill depth matrix don't just improve placements. They protect the relationship that generates the next mandate.

Wasted sourcing timeHours spent on a candidate who falls through
12–20h
Lost placement feeOne wrong submission, one rejected invoice
$5–15K
Client re-briefing costResetting expectations, defending process
3–5h
Mandate loss riskClient moves to a competitor after a bad hit
High
Replacement search costStarting the process again from zero
$2–8K
Total cost of one wrong hire
$7–23K+

Estimates based on typical mid-level placement fees and internal recruiter time. Relationship cost and mandate loss not included in financial figure.

LinkedIn Intelligence · Live candidate pool
Tran N. · 92% match · Low risk
Linh P. · 87% match · Steady trajectory
Minh V. · 83% match · ⚠ Hire risk flagged
What the data shows that gut feel misses

Three candidates. Same match score. Completely different story.

01
Minh has 83% match — but his stability axis is in collapse
Three roles in 26 months, each lateral move. Classic counter-offer cycle. A traditional recruiter sees "83% match" and submits. The hire risk profiler sees a 24% stability score and flags it before the client ever hears his name.
→ Flagged: High counter-offer exposure
02
Linh lists Docker and Kubernetes — but neither is confirmed
The skill depth matrix shows both as "listed only" — no endorsements, no projects, no context. A recruiter scanning profiles wouldn't catch this. The skill matrix flags it instantly, so you can probe before briefing your client.
→ Flagged: 2 JD-critical skills unconfirmed
03
Tran's trajectory is accelerating — invisible in a CV scan
Three promotions in four years, growing team size, consistent scope expansion. A CV shows his current title. The momentum panel shows he's on a trajectory your client hasn't seen in their last three hires — and that's worth leading with in the briefing.
→ Promoted 3× in 4 years · Accelerating
Three things that change

What switching to 9cv9 actually means for your desk.

⚡
Speed
From reading to deciding
Analysis that took hours of manual profiling now runs in minutes. Your team spends time on what only humans do well — building relationships and closing candidates.
40%
faster shortlist decisions
🛡
Risk
No more surprise dropouts
High-risk candidates are visible before you submit, not after. The hire risk profiler catches counter-offer exposure, job-hopping patterns, and career instability the moment profiles are loaded.
0
high-risk passes without flagging
📊
Client confidence
Win the "why this shortlist?" question
Pool quality score of 84 vs. market average of 67. A number that ends the doubt. Clients who ask for justification get a data-backed briefing instead of a recruiter's opinion.
3×
more client-ready briefings per week
Common questions

What prospects ask us — answered directly.

Our recruiters have deep market knowledge. Does AI really add anything? +
Recruiter intuition built on years of market experience is genuinely valuable — and we don't replace it. LinkedIn Intelligence structures what your recruiter already knows so it can be shared, audited, and defended to a client. Your recruiter's read on a candidate is now the last mile of a structured process, not the entire process. The radar doesn't override judgment — it informs it.
We already use an ATS and LinkedIn Recruiter. Why do we need this? +
ATS tools manage workflow. LinkedIn Recruiter helps you find candidates. Neither one tells you which of your found candidates to shortlist, or why. LinkedIn Intelligence sits between sourcing and submission — the analytical layer those tools were never designed to provide. It integrates inside your existing Job Order; there's no new workflow to adopt.
What if the AI gets it wrong? I can't rely on a score blindly. +
You're not supposed to. The radar is a prompt for structured thinking, not a replacement for it. When a stability axis drops, your recruiter doesn't auto-reject — they look closer. The scores surface what deserves attention. The recruiter decides what to do with it. That's the right division of labour, and it's more defensible than the current alternative.
Is this just for tech roles, or does it work across industries? +
The skill depth matrix and hire risk profiler are industry-agnostic — they score based on JD requirements you define, not a hardcoded skills taxonomy. The career momentum analysis reads tenure, promotions, and scope changes, which are meaningful in any sector. It's been built to work across 9cv9's mandate base, which spans technology, finance, operations, and executive roles.
How much disruption is there to our current process? +
Minimal by design. Candidates arrive via your existing Chrome Extension importer or manual entry — no new sourcing step. The intelligence panel is embedded inside the existing Job Order page. You don't change how you source; you change what you do after sourcing. Recruiter time shifts from reading to reviewing structured analysis. Most recruiters adapt within a week.
Ready to move from gut feel to evidence?

The shortlist your client trusts
is the one with data behind it.

LinkedIn Intelligence is live on the 9cv9 Recruiter Portal. Early access is open now — join the agencies already placing candidates with full analytical backing.

Request early access → Talk to our team

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