Build a recommendations engine,
or buy one that already works?
Calculate the real 3-year cost — engineering salaries, infrastructure, and maintenance — and compare against Glood AI.
Your engineering team's real cost
+ $252,000/yr ongoing
Live in <90 seconds
What "build" actually costs
Hidden engineering cost
A recommendations engine is not one project. It is data pipelines, model training, ranking, A/B tests, eval frameworks, and a feedback loop — each one needs an owner.
Infrastructure that compounds
GPU inference, vector databases, model registry, observability, retraining jobs. Costs grow with catalog size and traffic, and rarely match initial projections.
Maintenance forever
Models drift. Catalogs change. Shoppers behave differently in BFCM than in February. Maintenance is not an optional add-on — it is most of the long-term cost.
Opportunity cost
Every engineer building recommendations is one not building your differentiated product features. The real question is not "can we?" — it is "should we?"
Same problem. Very different timelines.
9–12 months to v1
- ·Hire ML + backend + frontend team
- ·Design data pipelines and infra
- ·Train, evaluate, A/B test, iterate
- ·Maintain and retrain forever
- ·Compete with focused vendors with 100× the training data
Live in 90 seconds
- →Install Shopify app
- →Agents calibrate to your catalog automatically
- →AOV and ROAS lift visible in week 1
- →Continuous model upgrades — no maintenance
- →Trained on $1.2 Bn+ in real eCommerce data
Frequently Asked Questions
Find quick answers about Glood.AI's platform, integrations, and the AI agents that power discovery, conversion, and retention, everything you need to scale faster.
Why do most in-house recommendations engines underperform?
Most teams underestimate the breadth: data ingestion, behaviour tracking, model training, ranking, real-time serving, eval, and ongoing retraining. v1 ships, then quality plateaus because the team is pulled to other priorities. Glood AI ships the same caliber of model that 30,000+ brands have already battle-tested.What about data ownership and privacy?
Your data stays yours. Glood AI is SOC2-compliant, runs in isolated tenants, and never trains shared models on your proprietary catalog or shopper data. Enterprise plans include private model deployments and contractual data residency.How long does Glood AI take to deploy?
Under 90 seconds on Shopify and Shopify Plus via our app. Custom integrations for headless and enterprise stores take 1–2 weeks with our deployment team. You start seeing AOV and conversion lift within the first month.Can we run Glood AI alongside our existing engine?
Yes. Many enterprise customers A/B test Glood AI against their in-house engine for the first 60 days. Most consolidate fully within a quarter once the contribution margin difference is measurable.
