Capability existed before FitGrid.
The problem was not to invent a different candidate. It was to select the right role, retrieve the strongest truthful evidence and prepare the candidate to control uncertainty without concealing the gap.
Results and evidence
Outcomes matter. So does what we can honestly infer from them.
Four full FitGrid clients have reached four employment outcomes. The result is meaningful early validation—not a guarantee, a controlled trial or proof that one intervention caused every outcome.
FitGrid records the starting condition, intervention, response, outcome and competing explanations wherever the evidence permits.4/4
full client campaigns
Four people. Four employment outcomes.
The first validation milestone says the complete FitGrid engagement deserves serious attention. It does not say every future client will be employed, every employer decision can be controlled or every observed improvement belongs to FitGrid alone.
The purpose of evidence is not to decorate certainty. It is to improve judgment.
Case evidence / anonymised
Offer after the second interview.
Seven weeks from the first FitGrid applications submitted. The candidate entered the process with substantial capability, a known technical gap and a need to make role-relevant evidence easier for interviewers to use.
Evidence and interview preparation aligned.
Role interpretation, proof selection, interviewer-specific preparation and repeated mock interview feedback were used to strengthen how the candidate performed the decision.
The process converted.
The candidate progressed through two interviews and received an offer seven weeks after the campaign’s first applications were submitted.
Contribution is not monopoly causation.
The timing, employer, labour market and candidate’s own implementation also mattered. The evidence supports a credible FitGrid contribution—not a claim that the same sequence guarantees another person’s result.
How FitGrid reads proof
Six questions—not a wall of praise.
Proof belongs beside the uncertainty it helps resolve. Outcome data is valuable. The causal explanation of why something happened is the diamond.
Role selection
Did the system change what deserved pursuit?
Application response
Did market recognition change?
Interview conversion
Did preparation improve performance or progression?
Employment outcome
Did the candidate reach the right next role?
Causal learning
What evidence suggests why the result occurred?
Continuity
Did the learning remain useful beyond one vacancy?
Your campaign is not a case study yet
Begin with the starting condition.
FitGrid first needs to understand where the campaign is stopping, what evidence exists and which outcome would genuinely count as the right next role.
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