Management consulting
Improve what the model already knows
Make your AI better at the tasks your business actually needs.
Taz builds task-specific evaluation suites, feedback loops, and iterative refinement workflows that improve AI output quality for your domain without retraining a foundation model.
The process
Turn observed failures into a repeatable improvement loop.
We move from output that is not good enough to a graded baseline, diagnosed patterns, and measured improvement.
- 01Baseline
Collect real outputs and grade them against your quality criteria.
- 02Diagnose
Identify systematic failure patterns vs. one-off errors.
- 03Improve
Refine prompts, system instructions, retrieval, examples, and routing.
- 04Measure
Verify improvement with the same eval suite before declaring success.
What we improve
- Domain accuracy and factual grounding in AI responses.
- Tone, style, and format consistency across outputs.
- Retrieval relevance — the right sources reaching the model.
- Edge-case handling where generic prompts fail silently.
- Routing decisions — which model and effort level for which task.
The improvement artifact
You receive a graded baseline, a diagnostic report, an improved prompt and system configuration, and a reusable eval suite that measures whether the next change helps or hurts.
baseline: graded output set | diagnosis: pattern map | improvement: prompt + config delta | eval: reusable quality suite
Post-training is not foundation training
Taz improves how an existing model performs on your tasks through prompts, system design, retrieval, and workflow changes. We do not train, fine-tune, or modify foundation model weights. The improvement is in the operating layer — the instructions, context, and feedback loops that shape output quality.
What we make visible
Make the quality gap measurable before trying to close it.
Taz turns vague 'the AI is not good enough' into graded baselines, diagnosed patterns, and measured improvements.
Output qualitygrade the baseline
Failure patternsdiagnose the cause
Improvement deltameasure the change
Regressioncatch the slip
Industry scenarios
See the service in the work it is meant to support.
Illustrative planning scenarios, not client case studies or performance claims.
Direct-to-consumer
Support context audit and response quality improvement
Open scenarioHospitality