Use-case framing
Define the task, acceptable behavior, human role, risks, and useful measures before selecting technology.
AI Integrations
Add AI capabilities to a product or operation with clear context, evaluation, human controls, and production visibility.
01 / THE PROBLEM
Integrate the behavior, not just the model.
A promising model demo can fail in production when it lacks trusted context, measurable quality, permission boundaries, failure handling, or a role in the surrounding workflow.
A strong fit when
- Products adding grounded generation or intelligent assistance
- Teams connecting models to approved business knowledge
- Operations that need reviewable AI-supported decisions
02 / THE SYSTEM
Capabilities connected around the outcome.
01 02 Context and retrieval
Connect approved data and knowledge with explicit access, freshness, and citation behavior.
03 Product integration
Design AI output, confidence, correction, escalation, and recovery into the user experience.
04 Evaluation and observability
Create test sets and production signals for quality, latency, cost, and failure review.
03 / APPROACH
Make the risky decisions testable early.
We begin with the decision or task AI should improve, then design the retrieval, model, tool, interface, evaluation, and review system around that job.
Typical outputs
- AI use-case and risk brief
- Context and integration architecture
- Evaluation plan and test set
- Product interface and implementation
- Operational review model
Working principles
- Start with a job, not a model
- Grounding has provenance
- Failure is a designed state
- Quality remains observable
04 / Practical answers
Before we start.
The answer depends on the data, providers, hosting model, user permissions, retention requirements, and consequence of the use case. An AI engagement should document approved sources, data flows, model and tool access, retention choices, permission boundaries, human review, logging, and deletion responsibilities. No sensitive data should be sent to a model or service merely because it is technically available.
Team composition follows the engagement rather than a fixed staffing package. Product strategy, experience design, software engineering, AI engineering, and technical leadership are included where the work requires them. The proposal should identify the core roles, their responsibilities, the client counterparts needed, and any specialist capability or third-party dependency.
Related thinking
Structured outputs are contracts, not guarantees
How to use schemas as interface contracts while separately evaluating meaning, evidence, permissions, and consequences.
Read articleRAG quality begins with content ownership
Why content owners, freshness rules, access boundaries, and retrieval evaluation belong in the architecture of a grounded AI system.
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