Product-catalog RAG
+25–60%
search → cart rate
Natural-language search that understands attributes, occasions, and intent. Outputs ranked results with reasoning, not just keyword matches.
E-commerce & retail
AI that moves AOV, CTR, and conversion — not just demos.
AISD builds AI for e-commerce and retail — product-catalog RAG, personalized recommendations, customer-service deflection, catalog enrichment, voice-of-customer analysis, pricing copilots. Every engagement measured against real shop metrics.
6 proven patterns · A/B tested · Latency-aware
Use cases
Six places AI moves the needle in retail.
Personalized recommendations
+15–35%
AOV uplift
Collaborative filtering plus LLM re-ranking based on browse context, time of day, and seasonality. Diversifies, prevents filter bubbles.
Customer-service deflection
25–40%
auto-resolution
Returns, order status, sizing, simple disputes — resolved in-chat. Hands off cleanly with context when judgment is required.
Catalog enrichment
10×
speed of catalog onboarding
LLM-generated descriptions, attribute extraction from supplier feeds, automatic categorization, image-tag enrichment. Quality-gated.
Voice-of-customer analysis
Weekly
PM-ready insights
Synthesize reviews, support tickets, and surveys into themes the product team can act on. Topic clusters with citations.
Promotion + pricing copilot
+5–15%
margin uplift
Surface SKU-level pricing recommendations using competitor signals, demand elasticity, and inventory position. Merchandiser stays in control.
How we measure impact
Three layers of measurement.
Product metrics
Search-result CTR, add-to-cart rate, conversion rate, AOV, repeat-purchase rate. Pre-registered hypotheses, A/B with sufficient power.
Quality metrics
Relevance ratings on a labeled sample, hallucination rate on facts, factual accuracy on attribute extraction. Eval harness in CI.
System metrics
Latency p95, cost per session, model availability. Per-call observability on every model invocation.
Services for e-commerce
Workflow case studies
Frequently asked
Common questions.
What AI use cases work for e-commerce?
Five proven patterns. Product-catalog RAG (natural-language search that understands attributes, occasions, and intent). Personalized recommendations (collaborative filtering + LLM re-ranking on context). Customer-service deflection (returns, order status, sizing, simple disputes). Catalog enrichment (LLM-generated descriptions, attribute extraction from supplier feeds). Voice-of-customer analysis (synthesize reviews into product-team-ready insight). Highest impact tends to be search + recommendation when SKU count is high.
How do you measure AI impact on e-commerce metrics?
Three layers. Product metrics: search-result CTR, add-to-cart rate, conversion rate, AOV. Quality metrics: relevance ratings on a labeled sample, hallucination rate on facts. System metrics: latency p95, cost per session. Experimentation discipline: pre-register hypotheses, run A/B with sufficient power, accept negative results. We bake all three into the eval harness before launch.
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
What does it cost to build an AI agent?
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
How do you ensure AI features are reliable in production?
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
Should I use n8n, LangGraph, or build from scratch?
It depends on workflow shape and team. n8n wins when the agent is mostly orchestrating SaaS tools and the control flow is straightforward — deploys faster, easier for non-engineers to maintain. LangGraph wins when the agent has complex branching, multi-agent coordination, or needs tight Python integration with custom code. From scratch wins for simple, high-volume agents where every layer of abstraction is overhead.