Solutions (Use-cases)
E-commerce / Retail & Marketplaces
Personalization & Recommendations
Goal: lift CTR, add-to-cart rate, AOV, and conversion.Goal: lift CTR, add-to-cart rate, AOV, and conversion.
Inputs: Product/catalog data, events (views, clicks, carts), orders, user/session profiles.
Deliverables: Candidate generation + re-ranker, real-time API, fallback rules, KPI dashboard.
KPIs: CTR, AOV, revenue/session, recommendation coverage.
Timeline: Quick Win 4–6 w (offline sim + demo) · Pilot 8–12 w (production rollout).
Stack options: embeddings + pgvector/Pinecone/Weaviate, re-ranker (XGBoost/Transformer), FastAPI, Airflow/dbt, MLflow, Evidently.
Risks & controls: cold-start → popularity/backfill; catalog quality → enrichment checks; latency → caching.
Dynamic Pricing & Promotions
Goal: increase revenue/margin with price safeguards.
Inputs: orders, inventory, demand signals, seasonality; optional competitor feed.
Deliverables: elasticity model, policy engine (floors/ceilings), simulation harness, audit log, pricing dashboard.
KPIs: revenue lift, gross margin, price-change acceptance, promo ROI.
Timeline: Quick Win 4–6 w (backtest + sim) · Pilot 8–12 w (limited live scope).
Stack options: Snowflake/BigQuery, dbt/Airflow, model svc (FastAPI/Ray Serve), MLflow, Grafana/Prometheus.
Risks & controls: cold-start → popularity/backfill; catalog quality → enrichment checks; latency → caching.
Catalog Intelligence (enrichment · dedupe · taxonomy)
Goal: improve search relevance and listing quality.
Inputs: seller feeds, titles/descriptions, attributes, images (optional), historical merges.
Deliverables: attribute extraction, normalization, duplicate detection, taxonomy assignment, QA tools.
KPIs: zero-result rate, search-to-click, catalog completeness, duplicate rate.
Timeline: Quick Win 4–6 w (pipeline + QA) · Pilot 8–12 w (production job + API).
Stack options: text embeddings, optional vision OCR, Postgres/Elastic/OpenSearch, Airflow/dbt, MLflow.
Risks & controls: noisy feeds → validation rules; drift → periodic re-train + QA sampling.
LLM Search & Support (RAG)
Goal: reduce ticket volume and time-to-answer; raise CSAT.
Inputs: knowledge base, product docs, policies, solved tickets, release notes.
Deliverables: chunking/ingestion, embeddings, retriever, LLM with citations and guardrails, evaluation dashboard.
KPIs: deflection rate, first-response time, CSAT, retrieval precision@k.
Timeline: Quick Win 4–6 w (internal demo) · Pilot 8–12 w (production with flags).
Stack options: LangChain/LlamaIndex, pgvector/Pinecone/Weaviate, OpenAI/Anthropic or Llama/Mistral, FastAPI, Evidently.
Risks & controls: hallucinations → grounding + refusal behavior; safety → content filters; ops → canary + rollback.
E-commerce / Retail & Marketplaces
Personalization & Recommendations
Goal: lift CTR, add-to-cart rate, AOV, and conversion.Goal: lift CTR, add-to-cart rate, AOV, and conversion.
Inputs: Product/catalog data, events (views, clicks, carts), orders, user/session profiles.
Deliverables: Candidate generation + re-ranker, real-time API, fallback rules, KPI dashboard.
KPIs: CTR, AOV, revenue/session, recommendation coverage.
Timeline: Quick Win 4–6 w (offline sim + demo) · Pilot 8–12 w (production rollout).
Stack options: embeddings + pgvector/Pinecone/Weaviate, re-ranker (XGBoost/Transformer), FastAPI, Airflow/dbt, MLflow, Evidently.
Risks & controls: cold-start → popularity/backfill; catalog quality → enrichment checks; latency → caching.
Dynamic Pricing & Promotions
Goal: increase revenue/margin with price safeguards.
Inputs: orders, inventory, demand signals, seasonality; optional competitor feed.
Deliverables: elasticity model, policy engine (floors/ceilings), simulation harness, audit log, pricing dashboard.
KPIs: revenue lift, gross margin, price-change acceptance, promo ROI.
Timeline: Quick Win 4–6 w (backtest + sim) · Pilot 8–12 w (limited live scope).
Stack options: Snowflake/BigQuery, dbt/Airflow, model svc (FastAPI/Ray Serve), MLflow, Grafana/Prometheus.
Risks & controls: cold-start → popularity/backfill; catalog quality → enrichment checks; latency → caching.
Catalog Intelligence (enrichment · dedupe · taxonomy)
Goal: improve search relevance and listing quality.
Inputs: seller feeds, titles/descriptions, attributes, images (optional), historical merges.
Deliverables: attribute extraction, normalization, duplicate detection, taxonomy assignment, QA tools.
KPIs: zero-result rate, search-to-click, catalog completeness, duplicate rate.
Timeline: Quick Win 4–6 w (pipeline + QA) · Pilot 8–12 w (production job + API).
Stack options: text embeddings, optional vision OCR, Postgres/Elastic/OpenSearch, Airflow/dbt, MLflow.
Risks & controls: noisy feeds → validation rules; drift → periodic re-train + QA sampling.
LLM Search & Support (RAG)
Goal: reduce ticket volume and time-to-answer; raise CSAT.
Inputs: knowledge base, product docs, policies, solved tickets, release notes.
Deliverables: chunking/ingestion, embeddings, retriever, LLM with citations and guardrails, evaluation dashboard.
KPIs: deflection rate, first-response time, CSAT, retrieval precision@k.
Timeline: Quick Win 4–6 w (internal demo) · Pilot 8–12 w (production with flags).
Stack options: LangChain/LlamaIndex, pgvector/Pinecone/Weaviate, OpenAI/Anthropic or Llama/Mistral, FastAPI, Evidently.
Risks & controls: hallucinations → grounding + refusal behavior; safety → content filters; ops → canary + rollback.
Saas
In-app LLM Assistant
Goal: improve activation and task completion.
Inputs: docs/KB, product telemetry, ticket data; optional tool APIs.
Deliverables: in-app assistant component, tool calling (search, create, update), analytics, feature flags.
KPIs: activation rate, task completion time, deflection, CSAT.
Timeline: Quick Win 4–6 w (limited surfaces) · Pilot 8–12 w (core flows).
Stack options: LangChain/LlamaIndex, OpenAI/Anthropic or Llama/Mistral, FastAPI/GraphQL, flags (LaunchDarkly/GrowthBook).
Predictive Analytics & Alerts
Goal: surface risks and opportunities in near-real-time.
Inputs: product events, billing, CRM/support signals.
Deliverables: scoring service, thresholds/alert rules, action feeds, dashboards.
KPIs: precision/recall vs baseline, alert resolution time, lift in targeted actions.
Timeline: Quick Win 4–6 w (backtest + dashboard) · Pilot 8–12 w (live scoring + alerts).
Stack options: scikit-learn/XGBoost, Airflow/dbt, Kafka/Kinesis (optional), MLflow, Grafana.
Churn & LTV Modeling
Goal: reduce logo churn; improve NRR.
Inputs: usage cohorts, billing, support interactions, entitlements.
Deliverables: risk scores, next-best-action rules, cohort dashboards, experiment plan.
KPIs: churn rate, retention, NRR, save rate on targeted accounts.
Timeline: Quick Win 4–6 w (historical score + actions) · Pilot 8–12 w (live scoring + outreach hooks).
Stack options: LightGBM/XGBoost/survival models, Snowflake/BigQuery, dbt, MLflow, Airflow.