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Bedrock Claude and Copilot GPT are great for ad-hoc work. Production retail surfaces four hard limits a fine-tuned domain SLM is built to remove.
Generating descriptions, extracting attributes or summarizing reviews for millions of SKUs at $5–$30 per 1K calls breaks every catalog-team budget. A fine-tuned SLM cuts that 50–100x while keeping quality on-brand.
Off-the-shelf models default to generic copy that erodes brand. A model fine-tuned on your existing PDPs, style guide and approved tone produces copy your merchandisers approve without a re-write pass.
Conversational search, recommendations and chat must respond in <500ms or shoppers bounce. A small quantized SLM on your own GPUs keeps p95 latency tight without the variable tail of a public API.
Generalist models translate your catalog inconsistently across locales. A fine-tune that learns your terminology, sizes, and regional conventions keeps every market aligned with the master catalog.
From PIM, reviews and tickets to a deployed retail SLM — in your own environment.
PIM catalog, reviews/UGC, tickets, chat logs, style guides, brand voice docs
Layout-aware parsing, image-attribute extraction, PII scrubbing on UGC, dedup
Brand-voice instruction sets, attribute Q&A, hard negatives for search
Qwen / Llama / Mistral base, SFT + LoRA / QLoRA, DPO for tone & safety
Catalog quality scorecard, search relevance, brand-voice rubric, safety probes
vLLM / SGLang on VPC GPUs for batch jobs & real-time, guardrail SLM, drift alerts
Same dataset hash → recipe → model → scorecard lineage as the rest of the InsightLM platform. Catalog teams get fast retrains; CX teams get version-pinned production behavior.
Each card shows the task, input, output and a target quality / cost bar.
Generate PDP titles, bullets and long descriptions at full SKU scale — tuned to your brand voice, your category templates and your SEO conventions, in every locale.
Extract structured attributes (material, fit, color family, dimensions, compatibility) and classify SKUs into your taxonomy — from messy supplier feeds, free-text descriptions and images.
Summarize thousands of reviews per SKU into shopper-facing pros/cons and merchant-facing aspect scores (sizing runs small, battery life poor, etc.) with sentiment per aspect.
Answer "is this jacket warm enough for skiing in Vermont?" — reasoning over your catalog, attributes, reviews and policies. Returns a ranked product set plus a grounded rationale.
Classify inbound tickets (where-is-my-order, returns, exchange, defect, fraud) and draft a grounded response using order status, return policy and SKU data — routed to the right queue with the draft attached.
Translate PDPs, marketing emails and policies across locales while respecting your terminology, units, sizing conventions and tone — not generic machine translation.
The bar an InsightLM retail SLM is designed and evaluated to. Customer-specific scorecards are produced from held-out evaluation sets during a pilot.
| Retail Task | Metric | Generalist Frontier LLM (Bedrock Claude / Copilot GPT, zero-shot) |
InsightLM Fine-Tuned 7B |
|---|---|---|---|
| PDP description generation | Merchandiser acceptance (no edits) | ~55% | ≥ 85% (target) |
| Attribute extraction | Field-level F1 | ~85% | ≥ 94% (target) |
| Taxonomy classification | Top-1 accuracy | ~80% | ≥ 92% (target) |
| Conversational search | nDCG@10 | ~0.66 | ≥ 0.78 (target) |
| Ticket intent classification | Top-1 accuracy | ~84% | ≥ 92% (target) |
| Median latency (shopper-facing) | p50 / p95 | ~900ms / ~3.5s | ~150ms / ~600ms (target) |
| Cost per 1K calls (typical task) | USD | ~$5–$30 | ~$0.01–$0.20 (target) |
Most retailers already use Bedrock Claude / Copilot GPT for ad-hoc work and a managed search engine for product discovery. InsightLM slots into the high-volume, brand-sensitive layer.
Catalog generation, attribute enrichment, review summarization, conversational search, ticket triage. Tasks that run at SKU / ticket / shopper-session volume where cost-per-call and latency matter.
The SLM handles the bulk of conversational search and ticket triage in-VPC; Bedrock Claude or Copilot GPT picks up the long-tail of edge cases or complex multi-policy reasoning. Single observability and cost dashboard.
Merch team brainstorming, exec memos, one-off campaign-copy drafts. Frontier LLMs are a great fit; an SLM would be over-engineering. InsightLM does not try to win these.
Pick the pattern that matches your data classification, GPU strategy and traffic shape.
vLLM / SGLang on managed GPU instances inside your AWS, Azure or GCP VPC; PIM and CMS connectors run inside the same accounts. Bedrock and Copilot remain available for tasks where they're the better fit.
Run nightly catalog enrichment, translation and review summarization as scheduled batch jobs on spot / ephemeral GPUs to drive cost down further. Real-time shopper traffic stays on dedicated capacity for stable latency.
SLM serves the bulk of shopper-facing conversational search and ticket triage; the orchestrator routes low-confidence or rare-domain queries to Bedrock Claude. One observability stack, one cost dashboard.
For retailers with strict customer-data segregation requirements: fully-private InsightLM with on-prem GPU clusters, no egress to public APIs, full audit trails for loyalty and CDP-adjacent workloads.
InsightLM curation pipelines turn each source into model-ready training data — with PII scrubbing on UGC and lineage tracked end-to-end.
PIM records, supplier feeds, taxonomy, image metadata, brand voice docs, style guides, SEO playbooks.
Product reviews, Q&A on PDPs, social UGC, returns reasons, NPS verbatims.
Tickets, chat logs, agent emails, call transcripts, returns/exchange records, complaint files.
A typical pilot picks one or two of the use cases above, runs end-to-end on a sample of your catalog and tickets inside your VPC, and produces a real scorecard against your current Bedrock / Copilot baseline in 4–8 weeks.
In your VPC • Works at SKU and ticket volume • Complements Bedrock and Copilot