Clarity
Use-cases grounded in business outcomes and operational constraints.
Consulting Service
AI Implementation
AI capability integration into products and operations, with quality controls and workflow design.
AI capability integration into products and operations — designed with evaluation, quality controls, and workflows that hold up in production.
Quality
Evaluation systems, guardrails, and review gates for reliability.
Integration
LLM systems embedded into real workflows, not demos.
Outcomes
What you'll get
Typical deliverables and outcomes for this service line.
AI use-case analysis
LLM integration
Quality evaluation systems
Team implementation support
Scope
What we implement
We implement AI workflows across product and internal operations: triage, drafting, retrieval, and decision support — where it makes operational sense.
Work is designed around quality and safety: evaluation, monitoring, and human review where needed.
Approach
How we keep it production-grade
We start with use-case clarity and measurable outcomes, then design system boundaries and failure modes.
Implementation includes evaluation harnesses, integration details, and team enablement so the system remains maintainable.
Delivery models
Engagement structure
Flexible structure based on scope and operational requirements.
Project
Fixed scope with defined deliverables, timeline, and ownership.
Retainer
Continuous execution capacity with monthly delivery cadence.
Embedded
Integrated with internal teams for complex systems and scaling work.
Timeline
Weeks 1–2
Define
Use-case selection, constraints, success metrics, and workflow mapping.
Weeks 3–6
Integrate
LLM integration, retrieval patterns, and workflow wiring into production systems.
Weeks 5–8
Evaluate
Quality evaluation, monitoring signals, and review gates tuned to the operational context.
Next step
Discuss scope and delivery structure.
If you're evaluating this service line, we can align on requirements, engagement model, and expected outcomes.