Cut No-Shows 75%. See how InPost hit 98% fill rate with Accept. Read case study →
AcceptMatch — The
Right Worker. Every Time.
AI-powered matching based on skills, reliability, location, and motivation. Data-driven decisions that actually work.
THE CHALLENGE
The Agency
Allocation Problem
Random placement leads to predictable failures.
Lack of skill verification
Getting someone who 'saw a forklift on YouTube' instead of a certified operator.
Ignoring location
40-mile commutes causing inevitable no-shows and late arrivals.
Zero learning
Traditional agencies don't learn from past performance. Mistakes are repeated.
"AI that actually thinks."
AcceptMatch doesn't just look at who is available. It looks at who is **motivated**, who is **reliable**, and who is **qualified** for your specific environment. It moves recruitment from "gut feel" to scientific precision.
THE TECHNOLOGY
The 5 Pillars of Matching
Skills & Certifications
Precise matching of licenses (Forklift, Food Hygiene). No guesswork.
Reliability Score
AI tracks historical attendance and punctuality; high scorers get priority.
Location Intelligence
Factors in commute times and area attendance patterns for every worker.
Experience Match
Prioritizes performers who have previously excelled on your specific site.
Worker Preferences
Matches roles to worker motivations, preferred shifts, and industries.
Data-Driven
Selection
"The system doesn't just find a worker. It finds THE worker for your site."
THE WORKFLOW
AI Recommends. Humans Decide.
Technology assists, but doesn't replace recruitment judgment.
Shift Posted
Job details, site, and requirements entered.
AI Recommends
System scans thousands for the top 5% match.
Human Approves
Our expert recruiters verify the AI's selection.
System Learns
Successes improve the algorithm for your next shift.
Traditional allocation vs.
AcceptMatch
Gut feel gets you bodies. Data gets you the right people.
| Factor | Traditional | AcceptMatch |
|---|---|---|
| Skills matching | Basic or none | Precise skill-to-job alignment |
| Location consideration | Rarely | Prioritised automatically |
| Past performance | Often ignored | Core to every decision |
| Worker motivation | Not considered | Preference-based matching |
| Learning from outcomes | Manual (if at all) | Automatic, continuous |
| No-show prediction | After it happens | Before they're booked |
Continuous Learning Loop
Every shift completes the data cycle, making the engine smarter.
FEEDBACK CYCLE
The System Learns.
Great Match
Successful workers are flagged for future similar roles on your site.
Wrong Fit
'Wrong fit' data is used to find better-suited work for that individual elsewhere.
No-Show / Reliability
Reliability scores drop automatically, deprioritizing them for future assignments.
Stop Guessing.
Start Matching.
Slash your no-show rate and increase productivity by using our AI-powered AcceptMatch engine. The right worker, every single time.