The use case is too broad
The team wants to “add AI” but has not defined the input, decision, quality metric or acceptable error.
AI Integrations
AI inside an existing workflow—with a measurable task, evaluation and fallback
We design and integrate bounded AI use cases for classification, search, generation and assistance.
We separate a demo from a production contour by addressing data, privacy, provider abstraction, quality, cost and human review.
Assess an AI use caseWhen it is needed
When AI Integrations becomes a practical requirement
The page names observable symptoms instead of replacing discovery with a promised outcome.
Data needs explicit boundaries
Privacy, retention, provider processing and staff access are not established.
Model output triggers a real action
Without evaluation, confidence, fallback and human review, an error becomes a production effect.
What Dazoro does
How we deliver AI Integrations
Work stays within an agreed contour and a verifiable result.
Use-case framing
We define one task, its input data, expected output and a measurable usefulness criterion.
Data and provider boundary
We establish privacy, retention, provider abstraction, cost and context limits.
Prototype with evaluation
We build a bounded flow and test it on representative examples rather than one demonstration.
Controlled integration
We add fallback, human review, observability and safe disablement before real effects.
Process
How the work proceeds
Context and acceptance criteria
We define the task, boundaries, dependencies and completion criteria before implementation.
Solution design
We choose the smallest viable architecture and sequence of changes for the current system.
Bounded implementation
We deliver the agreed diff in testable steps without an optional global refactor.
Validation and handoff
We validate critical scenarios, record limitations and provide a clear rollback.
Boundaries
What is included and what needs a separate decision
Included
- classification, search, generation and assistance
- provider/model abstraction and prompt contracts
- evaluation datasets and quality checks
- privacy boundaries, fallback and human review
Not promised automatically
- autonomous replacement of a team
- accuracy claims without a dataset and evaluation
- sensitive-data transfer without agreed safeguards
- irreversible action based only on model output
Approach
How the result remains verifiable
The solution is tied to observable contracts, tests and a controlled release.
The use case is bounded
The system solves a specific task with a known input, output and acceptable risk.
Quality is measured
An evaluation set and failure categories expose the model's real limitations.
Fallback is built in
Uncertain or unavailable AI does not block the safe base workflow.
Discuss the task
Is there one task where AI should deliver measurable value?
Describe the current workflow, data and decision to improve. We will assess applicability before choosing a model or provider.