Reliable AI for real work.

No evaluation process existed when we took over the project.

Business rules were conflicting and inconsistently implemented across the existing pipeline.

The root cause traced back to unclear scoping and information gaps at the start of the engagement.

The client had lost confidence in the roadmap and needed a credible path forward.

Paired with a junior/medior colleague to re-scope the problem from first principles rather than patching what existed.

Rebuilt the workflow end to end, replacing ad hoc fixes with a structured pipeline.

Introduced a dedicated evaluation module where none had existed before.

Ran case-by-case error analysis on a sample of production output to categorize failure modes.

Built a prioritized fix roadmap and presented it back to the client.

New pipeline architecture with clear evaluation checkpoints at each stage.

Error analysis broken down per case rather than a single aggregate accuracy number.

A documented, phased improvement plan communicated directly to the client.

Reopened a transparent status conversation with the client after a period of low confidence.

Delivered a concrete, prioritized roadmap that replaced reactive, ad hoc fixes.

Established an evaluation process the team has since reused on other legacy engagements.

Problem understanding has to happen before modeling starts, not after.

An evaluation framework is not optional overhead. It is what makes iteration possible at all.

Pairing a junior colleague on the rebuild doubled as an informal mentoring track.

The prior developer had left inconsistent, hard-to-maintain code across the pipeline.

No monitoring existed to catch data or model issues before they reached the client.

The business need was to predict which customers were likely to reorder, and roughly when, so the client's sales team could act on it proactively.

Split the system into three workstreams: database schema and ingestion from XML source data, the prediction model, and the cloud infrastructure.

Modeled repeat-purchase behavior with a probabilistic, Poisson-based approach instead of a black-box model, to keep outputs interpretable for the client's sales team.

Added monitoring across the pipeline to catch data and model issues early.

Ran an internal testing phase before any recommendation reached the client.

Kept the client in the loop with regular follow-ups to check outputs against real purchasing patterns.

A production system spanning ingestion, modeling, and Azure infrastructure.

Interpretable next-purchase predictions surfaced directly to the client's team.

Monitoring in place to catch issues before they affected client-facing output.

Client approved the recommendations for use in their own systems.

Established an ongoing feedback loop for the client to validate outputs against real outcomes.

Replaced an unmaintainable inherited codebase with a system the team can extend going forward.

An interpretable, Poisson-based model built more client trust than a more complex black-box approach would have.

Monitoring from day one avoided repeating the previous developer's maintenance problems.

Splitting the rebuild into three clear workstreams made a messy handover tractable.