Out-of-stock & variant leakage
Default co-visitation and rule widgets recommend sold-out sizes, discontinued finishes, or flood all 4 carousel slots with color variants of the exact same SKU.
Recommendations that respect compatibility, constraints, and margin—not just popular clicks
We audit and engineer product recommendation systems for spec-heavy stores and mid-market catalogs: PDP similar items, complete-the-look bundles, session-aware cart cross-sell, and Learning-to-Rank pipelines.
Why Default Widgets Fail
Four ways naive recommendation carousels leak conversion and AOV
Standard Shopify widgets and uncalibrated collaborative filtering treat a 1,500-SKU furniture or technical catalog like impulse apparel—ignoring dimensions, compatibility, stock depth, and page speed.
Compatibility & constraint violations
Recommending a Queen headboard on a King mattress PDP, an RF-mount lens for an EF camera body, or an indoor-only rug alongside a teak patio sectional.
Price-band & style whiplash
Showing a $4,200 Italian leather sectional on a $450 apartment loveseat page—or cheap replacement parts and swatches where high-margin anchors belong.
Cold-start blindness & CWV bloat
New arrivals and long-tail catalog items get zero impressions under naive click graphs, while heavy third-party script widgets add 140–170ms of main-thread blocking time.
What We Engineer
Two-stage retrieval, hard compatibility filters, and LTR reranking
Whether we deploy a custom model pipeline in your stack or engineer on top of Recombee, Shaped, or Algolia, every recommendation surface is built around structured catalog truth and evaluated with offline NDCG@10 / Recall@K gates plus online A/B testing.
- Substitute / Similar Items (PDP)
- Two-tower candidate generation + multimodal visual and structured attribute embeddings (dimensions, material, style, price band) so every substitute actually fits the shopper’s room or spec.
- Complete the Look & Cross-Category Bundles
- Explicit hard-constraint compatibility rules (bed size, mount, voltage, room collection, finish harmony) combined with co-purchase graph ranking.
- Session-Aware Cart & Checkout Cross-Sell
- Real-time in-session intent reranking (Cross-Encoder / Learning-to-Rank) calibrated to cart margin, price ceiling, and attachment probability.
- Zero-Result & Empty-State Recovery
- When a shopper asks for an unstocked combination, relax one constraint transparently and rank the closest in-stock alternatives instead of dumping generic best-sellers.
Scope & Pricing
Start with a 2-week Audit, or build the recommender end-to-end
In Advisory mode, 100% of your Tier 1 Audit fee is credited toward any Tier 2 engineering build signed within 60 days of the readout.
Tier 1 Audit
Recommendations & Merchandising Audit
Tier 1 Diagnostic · 100% credited to a Tier 2 build within 60 days
- $4,000
- Standard · 2 weeksPDP similar items, complete-the-look bundles, cart drawer cross-sell, cold-start coverage, compatibility & out-of-stock leakage check
- $7,500
- Extended · 2–3 weeksFull store & multi-category recsys evaluation, widget latency/CWV benchmark, click/co-purchase log audit & A/B test specification
Tier 2 Fixed-Scope Engineering Build
Recommendation System Build / Upgrade
For mid-market stores & engineering teams (4–8 weeks)
- $15,000–$40,000
- End-to-End Recommendation PipelineTwo-tower candidate generation, cross-encoder/LTR reranking, complete-the-look compatibility rules, and session-aware cart cross-sell with offline NDCG & online A/B harness.
Need ongoing model iteration? Embed a Resident AI/ML Engineer or view the full 4-tier rate card.
Recommendation engineering questions
Why hire RankTalos instead of installing a $99/month Shopify app? Off-the-shelf widgets rely on coarse popularity or uncalibrated co-clicks and cannot enforce domain constraints (dimensions, compatibility, finish matching, margin rules). Furthermore, after Crossing Minds ($60K/yr) was acqui-hired in June 2025, mid-market brands lost the main provider combining tailored ML recommendation models with a dedicated ML engineer. We fill that exact gap.
Do you build custom models or tune APIs like Recombee, Shaped, or Algolia Recommend? Both. If your team wants low operational overhead, we architect and tune Recombee, Shaped, or Algolia Recommend with custom feature pipelines and compatibility filters. If you want full ownership, we build and deploy a clean-room two-tower + LTR pipeline in your cloud.
How is a Recommendation System Build evaluated before launch? Every build ships with an offline evaluation suite (NDCG@10, Recall@K, catalog coverage, and constraint-violation rate on historical sessions) followed by an online A/B or interleaving experiment measuring revenue-per-session and attach rate.
Is the Recommendations Audit fee credited toward the build? Yes. In Advisory mode, 100% of the Recommendations & Merchandising Audit fee ($4,000 / $7,500) is credited toward a Tier 2 Recommendation System Build ($15,000–$40,000) signed within 60 days.