Create the Detail detail product requirement

Executive Overview Modern retail‑media platforms increasingly depend on opaque ML ranking, pricing and pacing models. Advertisers, regulators and internal operators now demand concrete answers to why an ad won, how budgets shifted, or where an outage started—all in real time. We therefore propose an Explainability Fabric built on two pillars: Unified, structured, high‑fidelity logging across transactional, configuration and operational events—each stamped with correlation IDs and shipped via a resilient streaming backbone.  Best‑practice patterns from micro‑service observability (JSON logging, correlation IDs, OpenTelemetry) lower MTTR and power RCA at scale. LLM‑powered analytics layer that parses, correlates and narrates those logs using Retrieval‑Augmented Generation (RAG), fine‑tuned domain models and tool‑assisted agents for deep root‑cause analysis while mitigating hallucinations. Together they convert raw events into auditable, human‑readable explanations that raise advertiser trust, speed incident response, and deliver a data‑driven edge in a $129 B retail‑media market. 2  Problem & Requirements Pain Points Opaque outcomes: Why did bid A beat bid B? Why did CTR drop 20% yesterday? Slow incident RCA: Distributed services (> 250 K TPS) lack end‑to‑end traces, stretching MTTR to hours. Regulatory risk: US FTC draft rules require auditable ML decisions. Core Requirements Category Requirement Target Metric Observability 100 % of production requests carry a correlation ID 0 % orphan logs Log freshness Ingest to searchable index < 5 s p95 Real‑time alerts Explainability 95 % of LLM summaries cite underlying logs Trust score ≥ 0.9 SLA impact MTTR for ad‑delivery incidents ↓ 50 % < 30 min p50 3  System & Sub‑System Architecture 3.1 Logical View 3.2 Data Contracts Transactional schema v1.0: auction_id, correlation_id, bids[], clear_price, floor_price, user_id_hash. Config schema v1.0: change_id, entity_type, parameter, old_val, new_val, actor_id. Operational schema v1.0: OTLP span‑ids + resource metrics. All messages enveloped in CloudEvents‑compatible JSON; PII fields salted‑hash or tokenised per GDPR. 5   Business Capability Framework Capability System Component(s) KPI Impact Competitive Edge Transparent Auction Insights Transactional + RAG explainability Advertiser trust ↑; win‑rate optimisation decisions 5× faster Meets ANA transparency guidelines. Real‑time RCA Tool‑assisted LLM agent, OTLP spans MTTR ↓ 50 % Faster than legacy Splunk‑only flow. Config‑to‑Outcome Traceability Config logs + correlation IDs Detect misconfig < 5 min Reduces wasted spend. Compliance & Audit Immutable GCS Bucket + signed logs Pass SOC 2 & GDPR audits Avoids regulatory fines. Proactive Optimisation Signals Vector similarity on historical incidents 10% uplift in ROAS via early anomaly alerts Differentiates vs. Amazon AMC. 6   Request for Proposal (RFP) 6.1 Scope & Deliverables Logging Backbone—Design & deploy high‑throughput Kafka/Kinesis clusters with schema‑versioning and OTLP export. LLM Explainability Service—Fine‑tune a 13B open‑weights model on provided labelled log‑explanation pairs; implement RAG and guardrails. Tool‑Assisted RCA Agent—Integrate TAMO‑style plugins for metric/trace correlation. UI & APIs—Dashboard and REST/GraphQL endpoints for explainability, with role‑based access control. Security & Compliance—Encryption, RBAC, audit trails, PII masking, retention policies. Knowledge Base Build‑out—Vectorise internal docs, run nightly refresh pipeline.

SYSTEM: Executive Overview 
Modern retail‑media platforms increasingly depend on opaque ML ranking, pricing and pacing models. Advertisers, regulators and internal operators now demand concrete answers to why an ad won, how budgets shifted, or where an outage started—all in real time. We therefore propose an Explainability Fabric built on two pillars:
Unified, structured, high‑fidelity logging across transactional, configuration and operational events—each stamped with correlation IDs and shipped via a resilient streaming backbone.  Best‑practice patterns from micro‑service observability (JSON logging, correlation IDs, OpenTelemetry) lower MTTR and power RCA at scale.
LLM‑powered analytics layer that parses, correlates and narrates those logs using Retrieval‑Augmented Generation (RAG), fine‑tuned domain models and tool‑assisted agents  for deep root‑cause analysis while mitigating hallucinations. 
Together they convert raw events into auditable, human‑readable explanations that raise advertiser trust, speed incident response, and deliver a data‑driven edge in a $129 B retail‑media market.

2  Problem & Requirements Pain Points
Opaque outcomes: Why did bid A beat bid B? Why did CTR drop 20% yesterday?
Slow incident RCA: Distributed services (> 250 K TPS) lack end‑to‑end traces, stretching MTTR to hours.
Regulatory risk: US FTC draft rules require auditable ML decisions.











Core Requirements
Category
Requirement
Target Metric
Observability
100 % of production requests carry a correlation ID
0 % orphan logs
Log freshness
Ingest to searchable index < 5 s p95
Real‑time alerts
Explainability
95 % of LLM summaries cite underlying logs
Trust score ≥ 0.9
SLA impact
MTTR for ad‑delivery incidents ↓ 50 %
< 30 min p50


3  System & Sub‑System Architecture 
3.1 Logical View

3.2 Data Contracts
Transactional schema v1.0: auction_id, correlation_id, bids[], clear_price, floor_price, user_id_hash.
Config schema v1.0: change_id, entity_type, parameter, old_val, new_val, actor_id.
Operational schema v1.0: OTLP span‑ids + resource metrics.
All messages enveloped in CloudEvents‑compatible JSON; PII fields salted‑hash or tokenised per GDPR.

5   Business Capability Framework 
Capability
System Component(s)
KPI Impact
Competitive Edge
Transparent Auction Insights
Transactional + RAG explainability
Advertiser trust ↑; win‑rate optimisation decisions 5× faster
Meets ANA transparency guidelines.
Real‑time RCA
Tool‑assisted LLM agent, OTLP spans
MTTR ↓ 50 %
Faster than legacy Splunk‑only flow.
Config‑to‑Outcome Traceability
Config logs + correlation IDs
Detect misconfig < 5 min
Reduces wasted spend.
Compliance & Audit
Immutable GCS Bucket + signed logs
Pass SOC 2 & GDPR audits
Avoids regulatory fines.
Proactive Optimisation Signals
Vector similarity on historical incidents
10% uplift in ROAS via early anomaly alerts
Differentiates vs. Amazon AMC.


6   Request for Proposal (RFP) 
6.1 Scope & Deliverables
Logging Backbone—Design & deploy high‑throughput Kafka/Kinesis clusters with schema‑versioning and OTLP export.
LLM Explainability Service—Fine‑tune a 13B open‑weights model on provided labelled log‑explanation pairs; implement RAG and guardrails.
Tool‑Assisted RCA Agent—Integrate TAMO‑style plugins for metric/trace correlation.
UI & APIs—Dashboard and REST/GraphQL endpoints for explainability, with role‑based access control.
Security & Compliance—Encryption, RBAC, audit trails, PII masking, retention policies.
Knowledge Base Build‑out—Vectorise internal docs, run nightly refresh pipeline.