Return on investment · Customer evidence
ROI case studies
How SME talent teams and recruitment firms convert the Fuku AI decision layer into measurable return — lower cost-per-hire, faster time-to-fill, and more placements per consultant.
Figures below are illustrative engagement models that demonstrate the economics of a Fuku AI deployment. Verified pilot data is available on request.
How to read this
The ROI method behind every story
Each case follows the same structure so the return is comparable across very different organisations: an in-house talent team measures savings, while a recruitment firm measures revenue. Both are driven by the same mechanism — Fuku AI removes manual screening and matching from the hiring loop and returns only decision-ready candidates, with the outcome of every hire fed back to sharpen future decisions.
01 · challenge
The cost of the status quo
Where time, money and quality leak today — manual screening, slow shortlists, agency spend, capped capacity.
02 · solution
How the decision layer deploys
Fuku AI sits inside the existing ATS / agency workflow, scoring and ranking candidates against role-specific success criteria.
03 · results
Before vs after, in numbers
Operational metrics — time-to-fill, screening hours, cost-per-hire, placements per consultant, quality of hire.
04 · return
Net benefit & payback
Annualised benefit set against platform cost, expressed as net return and months to payback.
Consumer marketplace
A high-volume consumer marketplace turns an overwhelmed screening funnel into a decision-ready pipeline — and cuts cost-per-hire by more than a third.
6 days → 1.2 days
S$4.2k → S$2.65k
capacity unlocked
quality of hire
The challenge
A lean talent team faced tens of thousands of applications a year across engineering, product, operations and commercial roles, with recruiters spending the majority of each requisition on manual CV review.
- Screening backlog pushed time-to-shortlist past a week, losing strong candidates to faster competitors.
- Screening quality varied by recruiter and by week, making the funnel hard to predict.
- Hiring managers were pulled into early-stage review, creating a second bottleneck.
The Fuku AI solution
The decision layer was connected directly to the existing ATS — no rip-and-replace.
- Every applicant is scored and ranked against a role-specific success profile, with a written rationale.
- Only decision-ready candidates are routed to recruiters; the long tail is handled automatically.
- Hire outcomes flow back into the model, so screening improves with every requisition.
| Metric | Before | After | Change |
|---|---|---|---|
| Time to first shortlist | 6.0 days | 1.2 days | −80% |
| Recruiter screening hours / req | 9.0 h | 2.0 h | −78% |
| Cost per hire | S$4,200 | S$2,650 | −37% |
| Applications screened / recruiter / mo | ~420 | ~1,430 | 3.4× |
| 90-day new-hire retention | 81% | 93% | +12 pts |
Benefit = recruiter time recovered + reduced cost-per-hire across ~200 annual hires.
Global manufacturing group
A global resource-based manufacturing group standardises hiring across sites and languages, slashing time-to-fill on hard-to-fill technical roles and cutting agency dependence.
58 → 31 days
mid-volume roles
across sites
better-fit shortlists
The challenge
Hiring was decentralised across plants and countries, with heavy reliance on external agencies for technical and engineering roles.
- Slow time-to-fill on specialist roles created real production and project risk.
- Candidate quality and screening standards varied widely between regions.
- Multilingual applicant pools (English, Mandarin, Bahasa) made consistent evaluation hard.
The Fuku AI solution
One decision layer, deployed across sites, with a common standard of evaluation.
- Multilingual screening ranks candidates against plant-specific success profiles.
- Mid-volume technical roles shift from agencies to a faster in-house funnel.
- On-the-job performance data feeds back to refine what “good” looks like per site.
| Metric | Before | After | Change |
|---|---|---|---|
| Time to fill (technical) | 58 days | 31 days | −47% |
| Applications screened / recruiter | 1× | 4× | +300% |
| Cost per hire (operational) | S$3,800 | S$2,240 | −41% |
| External agency spend | baseline | −35% | −35% |
| Offer-acceptance rate | 72% | 81% | +9 pts |
Benefit = reduced agency fees + lower cost-per-hire + production cost avoided from faster fills.
Tech & AI recruitment firm
A specialist tech & AI recruitment firm lifts placements per consultant by half — turning saved screening time directly into fee revenue.
8 → 12
capacity → fees
2 days → 3 hours
34% → 51%
The challenge
Revenue is capped by consultant time — and consultants were spending most of the day on manual sourcing and CV review rather than on clients and candidates.
- Slow shortlist turnaround meant roles were lost to faster competitors.
- Limited bandwidth capped the number of mandates each consultant could work.
- Junior consultants took a long time to ramp to senior-level output.
The Fuku AI solution
The decision layer embeds in the consultant's workflow — the firm's “inverse-CAC” growth lever.
- Auto-matches the firm's database and inbound against each live spec, instantly.
- Produces client-ready shortlists with rationale in minutes, not days.
- Standardises quality so a junior consultant ships senior-grade shortlists.
| Metric | Before | After | Change |
|---|---|---|---|
| Time to first shortlist | 2 days | 3 hours | −85% |
| Roles worked / consultant | 1× | 1.6× | +60% |
| Placements / consultant / qtr | 8 | 12 | +50% |
| Role fill rate | 34% | 51% | +17 pts |
| Revenue / consultant | baseline | +42% | +42% |
Benefit = incremental placement fees from added capacity + higher fill rate, net of cost.
Professional search firm
A professional search firm cuts shortlist turnaround by 70% and doubles submission quality — letting consultants spend their time on relationships, not admin.
urgent mandates
1:4 → 1:2
more mandates
freed for clients
The challenge
A relationship-driven search firm was being held back by manual long-listing and admin across many simultaneous live roles.
- Building long-lists by hand was slow and inconsistent in quality.
- Consultants stretched thin across mandates, with urgent client requests slipping.
- Variable shortlist quality strained client trust on premium searches.
The Fuku AI solution
Fuku turns inbound and database into ranked, decision-ready shortlists — consultants stay in the relationship, not the spreadsheet.
- Instant long-list to ranked shortlist with match rationale per candidate.
- Admin load drops sharply, freeing senior time for client and candidate work.
- Consistent quality across every consultant and every mandate.
| Metric | Before | After | Change |
|---|---|---|---|
| Shortlist turnaround | 3.5 days | 1.0 day | −70% |
| Submission : interview ratio | 1 : 4 | 1 : 2 | 2× quality |
| Consultant admin hours / wk | 18 h | 8 h | −55% |
| Placements / consultant | baseline | +38% | +38% |
| Client repeat-mandate rate | 58% | 71% | +13 pts |
Benefit = added placements from recovered consultant time + improved repeat-mandate revenue.
Multi-board recruitment firm
A specialist recruitment firm cuts job-posting costs from US$100–200 to just US$10 per post — advertising more roles, on more boards, for a fraction of the spend.
vs US$100–200
US$150 → US$10
wider reach, same budget
saved
The challenge
The firm advertises every live mandate across multiple job boards — but each post cost US$100–200, and cross-posting was slow and manual.
- Premium board fees of US$100–200 per post made wide advertising expensive at scale.
- High per-post cost forced the firm to under-advertise roles, narrowing the candidate pool.
- Posting and reposting each role board-by-board consumed consultant and admin time.
The Fuku AI solution
Fuku AI's job-posting engine publishes and manages every role from one place.
- One-click distribution pushes each role to 5+ job boards and channels at once.
- A flat US$10 per post replaces US$100–200 board fees — a ~93% cut.
- Cheaper posting means full multi-channel reach on every role; post performance feeds back to optimise where each role runs.
| Metric | Before | After | Change |
|---|---|---|---|
| Cost per job post | US$100–200 | US$10 | −93% |
| Boards reached per post | 1 (manual) | 5+ (1 click) | multi-channel |
| Time to publish a role | ~2 hrs | ~5 min | −96% |
| Live job posts / month | 60 | 110 | +83% |
| Annual job-posting spend | US$108,000 | US$13,200 | −88% |
Benefit = job-board fees saved (≈US$95k/yr) + incremental placements from wider reach.
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The pattern across every story
Two ways the decision layer pays for itself
Whatever the industry, Fuku AI removes manual screening and matching from the hiring loop and returns decision-ready candidates. That single mechanism shows up as savings for in-house teams and as revenue for recruitment firms.
ARCHETYPE A · IN-HOUSE TALENT TEAMS
Lower cost, faster hires
In-house teams recover recruiter capacity, cut cost-per-hire, compress time-to-fill and reduce agency dependence — while quality of hire goes up.
ROI lever: cost & time saved per hire × annual hiring volume.
ARCHETYPE B · RECRUITMENT FIRMS
More placements per consultant
Firms convert saved screening time directly into more mandates, more placements and higher revenue per consultant — with no extra headcount.
ROI lever: incremental placement fees from added capacity & fill rate.
Why the return compounds. Fuku AI is a decision workflow with an outcome-data loop — not a static database. Every hire and placement feeds back into the model, so screening accuracy, fill rates and ROI improve the longer a customer runs on the platform.
Run your own ROI pilot
A 60-day pilot benchmarks your current cost-per-hire, time-to-fill and consultant output — then shows the delta in your own numbers.