Retail360 analytics
AI-assisted analysis for approximately 50 sales and operations stakeholders across 5+ retail accounts, designed to shorten the path from data to decision.
Estimated impact25% faster analytics turnaround
Flagship engineering case study
From enterprise knowledge to reliable AI action.
A public-safe view of three related systems: decision-support analytics, retrieval-grounded answers, and agent-assisted operational triage.
Evidence policy
Usage counts below are reported scale from the supplied professional résumé. Percentage improvements are explicitly labeled as estimates. Internal data, prompts, infrastructure details, and confidential business logic are intentionally anonymized.
5+ accountsreported analytics coverage
≈1,500 docsreported retrieval corpus
300+ / monthreported user questions
20+ pipelinesreported agent monitoring scope
Three systems, one operating model
Ground the model. Constrain the action. Measure the result.
The useful pattern is not “add a chatbot.” It is to connect trusted context, explicit tools, evaluation, and human ownership around a clear workflow.
Document intelligence
A retrieval workflow indexing approximately 1,500 uploaded documents and supporting 300+ monthly questions with context-grounded answers.
Estimated impact35% less research time
Multi-agent reliability
Agents monitoring 20+ data pipelines and triaging approximately 50 failures per month, with alerts that keep people in control of recovery.
Reported impact10% lower incident response time
Deep dive · Retrieval-augmented generation
Try the transparent demo
Designing answers that can point back to evidence.
The RAG workflow treats retrieval quality, citation traceability, access boundaries, and fallback behavior as product requirements—not implementation details.
Sanitized system map
Human checkpoint at every consequential action
Knowledge-to-answer flow
01Knowledge sourcesApproved documents and structured data
02IngestionAccess checks, parsing, chunking, metadata
03RetrievalSemantic search, filters, reranking
04Grounded responsePrompt contract, citations, abstention
05Evaluation loopQuality, latency, cost, feedback
Identity & authorizationPII and content controlsTrace IDs and observabilityHuman escalation
Engineering decisions
The architecture is the set of tradeoffs.
Preserve meaning before chasing size.
Split on document structure where possible, retain source metadata, and test overlap against retrieval accuracy rather than selecting a universal token count.
Combine similarity with business filters.
Semantic relevance cannot override access control, freshness, document status, account boundaries, or known source quality.
Make abstention a valid outcome.
The answer contract requires evidence, exposes citations, and declines when retrieved context is insufficient instead of filling the gap with confidence.
Observe the chain, not only the model.
Trace retrieval, token use, latency, user feedback, and failure categories so the team can distinguish model, data, prompt, and infrastructure problems.
Evaluation contract
Quality is a release decision.
A credible AI feature needs repeatable checks before rollout and feedback signals after it reaches users.
Inspect the work
From polished claim to inspectable artifact.
The demo and documents below use sanitized portfolio data, but preserve the engineering decisions that matter in a production system.
01Retrieval Systems LabRun queries, inspect ranked chunks, tune thresholds, and see evaluation feedback in the browser.↗
02API contractA sanitized OpenAPI contract for ingestion, retrieval, answers, feedback, and health checks.↗
03Evaluation checklistRelease criteria spanning data, retrieval, groundedness, security, latency, cost, and operations.↗
04Incident retrospectiveA reusable template that connects containment, root cause, corrective action, and learning.↗
Want to inspect the retrieval behavior yourself?
The live lab keeps every step visible: query terms, ranked evidence, score thresholds, citations, and evaluation results.