01
Evaluation
We test your AI system against structured real world scenarios to find where it succeeds, where it fails, and where the risk sits.
- Rubrics and scoring criteria
- Test set creation
- Failure analysis
- Performance reports
Edmonton founded AI performance partner
AI that works in production, not just in demos
LLM Labz helps companies improve AI reliability through evaluation, data refinement, continuous feedback systems, and model optimization when needed.
Most AI systems fail quietly after deployment. Outputs drift, edge cases get missed, and trust erodes. We step in to measure performance, improve quality, and keep systems getting better over time.
Evaluation frameworks
High quality data
Feedback loops
Fine tuning when needed
Founder led delivery
Premium execution with direct accountability
Every engagement is led and executed by the founders. No delegation. No dilution. Just direct accountability from strategy to delivery.
Measure
We define what good looks like before optimization begins.
Improve
We strengthen outputs with better data, testing, and iteration.
Scale
We help teams move from pilot confidence to long term trust.
Why AI systems struggle after launch
Inconsistent outputs
The system handles common cases but breaks on important edge cases.
No clear measurement
Teams know quality feels off, but they cannot prove where or why.
User trust drops
Once people catch mistakes, adoption slows and expansion gets harder.
Improvement stalls
Without feedback loops, systems degrade instead of getting better.
Our services
We focus on the performance layer that sits between a working prototype and a dependable production system.
02
Data refinement
We create and clean high quality examples that teach the system what strong performance actually looks like.
- Curated training data
- Edge case coverage
- Domain specific examples
- Multi pass quality review
03
Feedback loops
We turn real usage into a repeatable system for improving quality over time instead of letting performance quietly drift.
- Correction capture
- Output review pipelines
- Continuous re testing
- Improvement roadmaps
04
Optimization
When the problem calls for it, we improve prompts, system logic, or fine tune models to raise reliability in production.
- Prompt and workflow tuning
- Model optimization
- Fine tuning support
- Before and after validation
How we work
A simple process built to show value quickly and improve performance with evidence, not guesswork.
01
Assess
We define success, gather examples, and build the first evaluation framework.
02
Test
We score outputs across normal cases, difficult cases, and edge cases.
03
Improve
We strengthen the system with better data, revised logic, and targeted optimization.
04
Re test
We validate that performance actually improved and document the gains.
Case study
How LLM Labz helps turn a working AI system into one users can trust.
Government document workflow
An organization deployed an AI system to review documents, generate summaries, and support decision making.
The system worked, but trust was weak
- Summaries missed important details
- Outputs were inconsistent across similar cases
- There was no clear way to measure quality
- Internal users became hesitant to rely on the system
We improved the performance layer
- Built evaluation rubrics and test cases
- Created stronger data for difficult scenarios
- Introduced a feedback loop using real corrections
- Validated improvements after changes were made
More consistency. More confidence. More adoption.
- Better handling of edge cases
- Measurable performance benchmarks
- Higher trust from users
- A stronger foundation for long term rollout
Meet the founders
Founder led delivery means tighter communication, faster iteration, and direct ownership over quality.
Request an AI evaluation
Tell us what your system does, where you think it is underperforming, and what kind of outcome you need. We will respond with a clear next step.
Evaluation first
Premium QA
Edmonton founded