Scoring that gives the same answer every time.
Fixed 0-to-100 logic with hard eligibility gates, so one input always yields one explainable result — defensible to an evaluator, not a black-box mood.
A score you can defend, line by line.
When a number decides who qualifies, who advances or who gets funded, it cannot change from run to run or come from a model's mood. Deterministic scoring is fixed logic: defined criteria, defined weights, hard eligibility gates. The same input always produces the same score, and every point of it can be traced to a rule.
That is the difference between a tool an evaluator trusts and one they quietly override. A black-box model that scores an applicant differently on Tuesday than on Monday is indefensible the moment someone asks why. We build screening and scoring logic where the answer is consistent, explainable and auditable — the kind of decision engine you can put in front of a review panel or a regulator. We have built exactly this for government and health programmes.
- Fixed 0-to-100 scoring — defined criteria and weights, no drift between runs
- Hard eligibility gates that pass or fail before any score is given
- An explanation for every score — which rule contributed what, and why
- A full, replayable log so any result can be defended after the fact
Fixed logic where a decision has to hold.
A model can read the messy input, but the verdict stays deterministic — consistent, explainable and logged, so it survives a review.
Deterministic scoring engines
Fixed 0-to-100 logic where the criteria, weights and thresholds are defined and stable. The same input yields the same score every time, so a result is never a surprise and never depends on when it was run or which model answered.
Hard eligibility gates
Some rules are pass-or-fail before scoring even begins. We build hard gates that admit or reject on clear criteria first, so an ineligible case is stopped cleanly at the door rather than scraping through on a high score elsewhere.
Explainable results
Every score comes with its reasons — which criterion contributed what, which gate passed or failed. An evaluator sees not just the number but how it was reached, so the decision can be discussed, checked and stood behind.
Audit trail & replay
Every input and result is logged and replayable. Months later you can reproduce a score exactly, show which rules applied, and prove the process was consistent — the evidence a review panel or auditor actually asks for.
Screening & ranking
Beyond a single score, we build the screening around it — eligibility, ranking, shortlisting — so a queue of applications is triaged consistently. Uminber has built this kind of screening logic for government and health programmes.
AI where it helps, rules where it counts
A model can assist — reading a document, extracting a field, summarising an application — but the decision stays deterministic. We use AI for the fuzzy input and fixed logic for the verdict, so you get help without handing the outcome to a guess.
A score you can put in front of a review panel or a regulator.
Same input, same result — gated, explained and logged, not a black-box mood.
Decisions that hold up to scrutiny.
Not a number people quietly override — one they can reproduce, explain and stand behind, case after case.
From rubric to reproducible score.
We turn the policy into explicit rules, gate eligibility first, then build fixed logic that explains and logs every result.
Criteria & weights
We turn the policy or rubric into explicit criteria, weights and thresholds — written down, agreed, and unambiguous before a line is built.
Set eligibility
We define the hard pass-or-fail rules that run before scoring, so ineligible cases are stopped cleanly at the door.
Fixed logic
We implement the scoring as deterministic logic — no drift, no model mood — so the same input always yields the same 0-to-100 result.
Show the reasons
We surface why each score landed where it did — the contributing rules and gates — so every result can be read and defended.
Make it auditable
We log every input and result for replay, so months later any score can be reproduced and justified to a panel or regulator.
Scoring inside a real system.
Deterministic logic pairs with the AI that reads the inputs and the software that runs the queue.
Things you might be wondering.
What is deterministic scoring?
Why not just let an AI model score it?
How is a deterministic score explainable?
Have you built this before?
A decision that has to be consistent and defensible?
Tell us the criteria. We will build scoring that gives the same answer every time — and explains itself.