AI Conversational Search & Discovery
Answers Buyers Can Trust
Search and discovery experiences tuned for how buyers actually ask questions, with structured content, retrieval, evaluation, and UX patterns reviewed by experienced operators.
When This Fits
When site search and filters don't match natural language needs.
Buyers describe problems, not SKUs. Static search boxes and faceted nav alone miss intent, especially in complex catalogs.
High-consideration queries
Comparisons, compatibility, and sizing need answers tied to source content.
Content isn't AI-ready
FAQs, specs, and policies aren't chunked, tagged, or current.
Hallucination risk
Off-brand or wrong answers erode trust and create liability.
Ops can't maintain another silo
Answers should pull from the CMS and commerce systems you already own.
Measurement is fuzzy
You need to know whether discovery changes conversion, assisted revenue, or support load.
Performance budgets
Assistant UI and retrieval can't tank mobile performance.
Search + Engineering
Discovery backed by real content operations.
We connect editorial workflow, schema, and retrieval so the assistant is part of your stack, not a chat bolt-on.
Established Technical Partner
Two-plus decades shipping web platforms across industries and stacks.
Senior-Led Delivery
Engineering and architecture leads stay on the project after the pitch.
One Accountable Team
Strategy, design, engineering, and support work from the same plan.
How We Deliver
Content audit, retrieval design, UX, evaluation.
We define what “correct” means, instrument citations, and iterate with human review loops.
Intent & corpus review
What questions matter and which documents are authoritative.
Retrieval architecture
Hybrid search, embeddings, and freshness rules.
Experience design
Prompting UI, fallbacks, and escalation to human help.
Eval & rollout
Golden questions, regression tests, and staged release.
Relevant Work
Conversational search and discovery launches.
Search and discovery programs where conversational AI improved product findability and conversion rates.
Typical Components
Vector search, LLMs, and your commerce graph.
We integrate with vendors or open models depending on latency, cost, and policy constraints.
Search & vector stores
OpenSearch, Postgres pgvector, or managed retrieval services.
LLM orchestration
Tool use, citations, and guardrails in application code.
Content pipelines
ETL from CMS/PIM with chunking and metadata enrichment.
Frontend patterns
Next.js components, streaming responses, accessibility.
Analytics
Query logs, satisfaction signals, and A/B hooks.
Safety
PII redaction, prompt injection defenses, and audit logs.
Ways to Work Together
From prototype to production assistant.
Validate the idea with a scoped sprint, then scale to a full assistant sized to your catalog complexity and risk tolerance.
Discovery sprint
Answer whether conversational UX moves your metric.
MVP assistant
Scoped corpus, UI, and evaluation harness.
Scale & tune
Latency, cost, and quality improvements post-launch.
Content ops partnership
Ongoing freshness and taxonomy support.
Start With the Problem
Buyers asking questions your search can't answer?
Share the catalog and pain points. We'll propose a discovery approach tied to source content, measurement, and rollout risk.
Related Solutions
Ecommerce discovery siblings and pillar hubs.
Conversational search programs often follow complex catalogs, headless storefronts, and marketing measurement.
Furniture omnichannel
Showroom, delivery, and digital commerce for furniture retailers on Shopify and STORIS.
Shopify Hydrogen
Headless Shopify storefronts with Storefront API, Oxygen, and composable merchandising.
Ecommerce development services
Shopify, migrations, integrations, and omnichannel programs—the ecommerce pillar hub for retailers and brands.
Digital marketing services
Website, design, SEO, PPC, and social programs—the digital marketing pillar hub for measurable growth.