Appiah AI
Work
Representative systems, written with the specifics that matter.
Client details vary, but the patterns are consistent: reduce decision latency, improve quality, preserve control, and give teams a system they can actually operate.
Revenue operations copilot
A B2B services team needed faster account research without losing nuance.
- Built a structured research workflow across CRM notes, proposals, public company signals, and call transcripts.
- Added source-linked briefings, account risk flags, and human approval before outbound use.
- Reduced prep time while improving consistency across senior and junior sellers.
Policy-aware service assistant
An operations group needed frontline staff to answer complex customer questions consistently.
- Mapped policy documents into answerable decision trees and retrieval-backed responses.
- Designed escalation rules for uncertain, high-risk, or exception-heavy cases.
- Created evaluation sets from real service queries to track answer quality over time.
Finance close automation
A finance function wanted to reduce manual variance commentary during monthly close.
- Connected ledger extracts, budget files, and prior commentary into a review workflow.
- Generated draft explanations with evidence links and reviewer controls.
- Defined monitoring for hallucination risk, missing data, and unusual variance patterns.
Executive AI portfolio
A leadership team had many ideas and no consistent way to decide what deserved funding.
- Built a scoring model across value, feasibility, risk, data readiness, and adoption complexity.
- Turned the highest-scoring opportunities into phased business cases.
- Established a steering cadence and production-readiness gates for each initiative.
“The useful question is not whether AI can produce an answer. It is whether the organisation can trust, govern, and improve the system that produced it.”Appiah AI delivery principle