Customer Experience AI Assessment

This engagement centres on a clear goal: enhancing our customer experience by leveraging sentiment analysis across both structured and unstructured data. I need a succinct, expert-level assessment that tells me whether the idea, data, and technical pathway are sound before full development begins.

Scope
• Examine the data landscape. Our customer records live in databases and spreadsheets, while social posts, emails, and support transcripts come in free-text and image form. Detail what must be cleaned, labelled, or augmented so sentiment signals are reliable.
• Recommend the modelling approach. Contrast rule-based, classical ML, and modern transformer techniques, then justify the best fit for accuracy, latency, and cost at our scale.
• Outline an implementation roadmap that covers data ingestion, feature engineering, model training, deployment, and monitoring—highlighting key risk points and mitigation steps.
• Provide an evaluation plan with metrics (e.g., F1, sentiment polarity error) and a sampling strategy to ensure business-relevant performance.
• Deliver a concise report (max 15 pages plus an executive summary) and an optional slide deck ready for stakeholders.

Acceptance Criteria
1. Actionable data recommendations that map source → required preprocessing task → tooling suggestion.
2. Model selection rationale that links technique choice directly to customer experience outcomes.
3. A phased roadmap with milestones, dependencies, and estimated effort bands.
4. Clear success metrics and testing protocol.

Tools & References
Feel free to lean on Python, Pandas, spaCy, Hugging Face Transformers, or similar libraries—specific code is not required, but architectural diagrams, sample pipelines, and reference notebooks are welcome.

Timeline and collaboration details can be finalised once the outline is approved; the emphasis now is on a high-quality, vendor-agnostic assessment that lets us decide with confidence whether to proceed.