Shopping carts have looked the same for 25 years: a search box and a grid. RightPick replaces that with a guided buying layer that behaves like your best sales rep — recommending the right products, the right companions, and the right quantities, with evidence behind every answer.
Most stores answer "what do you sell?" — not "what should I buy?" RightPick closes that gap.
| Search-and-Grid Store | With RightPick |
|---|---|
| ✕ Shopper guesses which product fits their job | ✓ RightPick maps the job to the right product and explains why |
| ✕ "How much do I need?" goes unanswered | ✓ Quantity logic sizes the order to coverage, area, or run length |
| ✕ Required companions forgotten → failed projects, returns | ✓ Required and optional companion SKUs surfaced before checkout |
| ✕ Marketing copy overstates what a product can do | ✓ "Not suitable for" and "choose instead when" stated honestly |
| ✕ Recommendations are a black box you can't trust | ✓ Every fact carries a source, an excerpt, and a confidence score |
| ✕ Carts are smaller and returns are higher | ✓ Shoppers leave with the right mix and high value-for-money |
RightPick turns shallow catalog data into a decision layer that answers the questions an experienced owner-operator would — for every product, automatically.
Primary and secondary use cases, plus the contexts a product is genuinely suitable for — not just the marketing headline.
Explicit "not suitable for" conditions and "choose instead when" rules, so shoppers avoid the return and trust the store.
Required companion SKUs for the job to succeed — primer, hardener, fixings, consumables — plus optional add-ons.
Quantity and configuration logic that sizes the order to coverage area, run length, pack size, or usage rate.
Ranked alternatives with the trade-offs spelled out, so a shopper on a budget or a deadline still gets the right fit.
Every conclusion is backed by evidence — a source URL, an excerpt, an extraction method, and a confidence score.
RightPick is a pipeline, not a chatbot bolted on top. It ingests your catalog, enriches it with public evidence, and normalizes everything into decision-grade JSON before a single recommendation is made.
API-first wherever possible — WooCommerce Store API and similar — with respectful, robots-aware crawling as fallback. Identity, pricing, variations, images, and JSON-LD captured per product.
Adds evidence from on-site FAQs and help content, public PDFs and technical docs, video transcript signals, and approved peer sources — exactly where merchant copy runs thin.
Converts raw and enriched content into a canonical decision schema: use cases, suitability, companions, alternatives, quantity logic, and risk notes — with provenance and confidence on every fact.
The normalized corpus powers guided selling, comparison, and cart composition — via cloud LLMs or a private small model you control. Low-confidence or safety-sensitive facts route to human review first.
RightPick never turns flowery copy into false certainty. Every product becomes a structured pack of facts — and every fact knows where it came from.
Exported as canonical JSON per product, JSONL fact records, or knowledge packs grouped by category and use-case.
The depth that makes a recommendation trustworthy rarely sits in the product description. RightPick gathers it from the sources that do — and keeps the provenance.
Application notes, usage limits, and "does it work with…" answers that buyers actually ask.
Spec sheets, datasheets, coverage tables, and safety guidance — parsed into structured facts.
Caption and transcript signals from how-to and demo videos, via multiple transcript adapters.
Comparison and limitation language from approved peer or manufacturer sites — per-tenant policy, never unbounded scraping.
Contradiction detection runs across sources, and provenance is never collapsed — conflicting claims are scored, not silently merged.
Picking the product is half the job. RightPick reasons about how much, which configuration, and what has to ship alongside it.
| Shopper Intent | What RightPick Recommends |
|---|---|
| "Coat a 40 m² garage floor" | Quantity from coverage rate × two coats, plus required primer and a recommended top-coat companion |
| "This product, but for outdoor use" | Choose instead — flags the indoor-only limitation and ranks a UV-stable alternative |
| "Cheapest option that still works" | Best value-for-money pick with the trade-offs stated, not just the lowest price |
| "Add the kit to do the whole job" | Composes the cart: base product, required companions, consumables, and quantities |
| "Is this safe for food-contact surfaces?" | Answers only from evidence; if unverified, routes to review rather than guessing |
One codebase, three deployment modes. Keep product intelligence and business data exactly where your compliance team needs it — and feed cloud LLMs or a private small model you train yourself.
Multi-tenant managed cloud. Fastest to onboard, continuously improved extractors, cloud LLMs by default. Ideal for SMB and mid-market brands.
Single-tenant VPC, dedicated VM, or managed Docker. Hybrid model strategy — cloud or private inference — for mid-market manufacturers with data concerns.
Docker Compose or Kubernetes in your environment. Private small models and local retrieval by default — cloud LLM use can be disabled entirely.
The durable advantage isn't the crawler or the chat box — it's the versioned, evidence-backed product intelligence corpus, which can feed both guided-selling apps and your own domain-specific model.
RightPick is in early access. Ingestion, enrichment, and decision-grade exports are taking shape now — join early access for priority onboarding and pricing.
Common questions about RightPick, your data, and how recommendations stay trustworthy.
Schedule a demo to see RightPick turn a real catalog into decision-grade product intelligence.