“The response looks correct.”
- Judged primarily by plausibility
- Failure cause remains unclear
- Difficult to reproduce consistently
Independent AI evaluation
Know whether your AI can reason.
PDS designs rigorous evaluations that expose failure modes in reasoning, constraint adherence and tool use—before those failures reach production.
- ✓Decision-specific
- ✓Failure-focused
- ✓Reproducible
Evaluation run 024Reasoning system · controlled test
Evaluation traceEvidence captured
Constraint adherencePass
Multi-stage consistencyReview
Unsupported inferenceFlagged
Evaluation principlesReal tasksMeasurable criteriaTraceable evidenceRepeatable results
Why evaluation matters
Fluent output is not reliable performance.
A compelling answer can still contain a broken inference, violate a critical constraint or fail when a workflow becomes unfamiliar.
Generic benchmark scores rarely explain whether a system is dependable for your actual decision.
“The evidence shows where it succeeds—and fails.”
- Measured against explicit criteria
- Failure modes classified and traced
- Results designed for retesting
What we evaluate
Tests built around the capability that matters.
Each program combines clear success criteria, targeted test design and evidence-rich analysis.
Reasoning evaluation
Test whether a system can solve multi-stage, mathematical and domain-specific problems—not merely imitate a plausible answer.
Logic · Consistency · Long horizonConstraint adherence
Measure whether instructions, policies, formats and operational boundaries remain intact under realistic pressure.
Instructions · Policies · FormatsAgent and tool use
Evaluate planning, tool selection, recovery and final outcomes across workflows with dependent actions.
Planning · Tools · RecoveryReliability and robustness
Expose brittle behavior, unsupported claims and performance changes before they become production failures.
Failure modes · Drift · ReproducibilityThe method
From business decision to defensible evidence.
A focused evaluation process that makes performance visible, comparable and actionable.
Explore AI evaluation- 01
Define the decision
Start with the real task, users, constraints and consequences—not a generic benchmark.
- 02
Design targeted tests
Build evaluation sets that separate genuine capability from fluent pattern matching.
- 03
Diagnose failure modes
Trace errors to reasoning, retrieval, instruction-following, numerical or tool-use failures.
- 04
Improve and retest
Turn findings into a repeatable measurement loop for model, prompt and workflow changes.
What you receive
Not another dashboard. A decision-ready evaluation.
See scores, traces, failure categories and recommended actions in one reproducible evidence package.
Illustrative report interface shown.
Discuss your evaluationEvaluation reportMulti-stage analytical assistant
Action requiredOverall findingStrong baseline reasoning with material failures under conflicting constraints.
Retest readinessTest set and scoring rubric versioned
Problem decompositionMaintained dependent steps
PassedConstraint prioritizationInconsistent under ambiguity
ReviewNumerical verificationUnsupported intermediate value
FlaggedRecommended next actionAdd verification step, revise instruction hierarchy and retest.
Where this helps
For AI decisions that need to withstand scrutiny.
PDS focuses on consequential analytical workflows—not novelty demonstrations.
Teams deploying AI agents
Validate plans, tool calls, recovery behavior and end-to-end outcomes before broader rollout.
Teams comparing model changes
Measure whether a new model, prompt or retrieval strategy actually improves the tasks that matter.
High-stakes analytical workflows
Create domain-aware tests where reasoning quality, traceability and constraint adherence are essential.
PDS also applies predictive analytics, signal intelligence and mathematical modeling across healthcare, finance, property and public data.View broader capabilities →
A rigorous partner
Built for evidence, not theatre.
Independent perspective
Evaluation criteria are shaped by the decision and its risks, not by a model vendor’s preferred metric.
Reproducible by design
Versioned tasks, rubrics and findings make progress measurable as systems change.
Failure-focused analysis
Results identify not only whether performance dropped, but where and why.
Start with the decision
Find out what your AI can actually be trusted to do.
Tell us about the workflow, the stakes and the behavior you need to measure. We’ll shape a focused evaluation plan.