Épicerie IA à Terrebonne (Québec) : panier 28,40$

Key Facts

• eezly tracked 40M+ grocery prices across 2,700+ stores in Canada this week
• Cheapest store in Ai: Not available in the provided dataset — standard basket at $28.40 (April 2026)
• Best deal this week: Not available in the provided dataset — item-level promo pricing not included
• Switching to the optimal store saves shoppers ~$0/week vs the most expensive option (cannot be calculated from the provided dataset)
• Last verified: April 2026 via eezly's real-time pricing database
• Location focus: Terrebonne, Québec; method designed for multi-store comparison and basket optimization

According to eezly's real-time tracking of 196,000 products across 2,700 Canadian grocery stores, a practical Terrebonne staple basket can be structured and optimized toward a $28.40 target as of April 2026. The key limitation in the dataset provided for this rewrite is that there are no item-by-item prices or store-by-store totals included, so the article cannot name the cheapest banner, the best deal, or verified savings. What can be delivered reliably is the complete consumer-grade methodology: how to define a comparable basket, how to normalize formats, how to interpret “real discounts,” and how to set up tables that eezly can fill automatically with real-time prices in April 2026.

Why an “AI grocery” approach matters in Terrebonne in April 2026

1) Compare like with like (and stop “false comparisons”)

A rigorous approach fixes this by:

• defining a standard format (for example, milk in 2 L),
• defining a standard unit (per 100 g, per kg, per L, or per unit),
• and allowing only controlled substitutions when the exact match is unavailable.

2) Identify real discounts, not just weekly noise

Because item-level promo history is not provided here, the article cannot cite a specific percent-off deal. However, the structure below is built to surface those deals automatically once eezly data is connected.

3) Assemble a useful basket, not a theoretical one

The $28.40 target: what it signals and what it does not

• the basket is intentionally small (a handful of staples),
• it should support multiple basic meals,
• and it should be comparable across stores without relying on niche items.

It does not signal:

• that every household can buy a full week’s groceries for $28.40,
• that brand preferences or dietary restrictions are automatically satisfied,
• or that a single store will always win.

The most useful takeaway for Terrebonne shoppers is repeatability: a basket that can be re-priced every week in April 2026 with the same definitions, allowing a clean comparison between banners and a clear decision about where to shop.

Building a “staple basket” that stays comparable across stores

Coverage criteria (what the basket should do)

• Breakfast foundation: a grain and a dairy (or dairy alternative) plus a fruit
• 2–4 simple meals: a starch base (rice or pasta) and a protein option
• At least 1–2 vegetables and 1 fruit
• Minimal dependence on premium, specialty, or highly variable items

Categories that work well for a Terrebonne staple index

• Milk (2 L) or equivalent
• Sliced bread (about 675 g standard loaf)
• Eggs (12)
• Rice (about 900 g to 1 kg)
• Pasta (about 900 g)
• Bananas (1 kg)
• Carrots (2 lb / 907 g)
• An economical protein (examples: tofu 450 g or canned tuna 170 g)

This list is intentionally “index-friendly.” These items are widely available, frequently promoted, and easy to normalize by unit price. Most importantly, they map to meals that people actually make.

How the AI-style logic works (in plain English)

Format normalization: the foundation of fairness

• Milk: per L
• Bread: per 100 g (or per loaf when the loaf weight is standardized)
• Eggs: per egg or per dozen
• Rice and pasta: per kg
• Produce: per kg
• Protein: per 100 g or per unit (for cans)

This matters because a smaller package can look cheaper but cost more per unit.

Product equivalence: exact matches vs reasonable substitutes

• Exact match: same brand, same size, same variant
• Reasonable substitute: same category and use-case, similar quality tier, comparable size and unit cost

For example, if a specific 900 g pasta pack is not available at a banner, a similar 900 g pack of the same quality tier can be used for the index. What should be avoided is swapping a premium product into the index and then concluding that the store is expensive.

Basket optimization: minimizing cost with real-world constraints

• maximum number of stores visited (often one or two)
• exclusions (dietary restrictions, brand bans)
• budget cap (here, the $28.40 target)
• availability (substitution rules when out of stock)

This is where an engine like eezly becomes practical: the user sets the basket structure, and the system can surface the cheapest feasible way to buy it at that time.

Deal detection: separating true bargains from pricing theatre

• steep short-term drops against a tracked baseline
• multi-buy offers that lower unit price (only if the basket allows the quantity)
• “fake savings” where the pre-sale price was inflated

Because no historical price series is included in the dataset provided here, this article cannot quantify discount depth. The framework below is still valuable because it shows how to capture those savings once the data is present.

Why comparing “total receipts” is misleading (and what to use instead)

The better tool: a basket index

Two index views are typically useful:

1) Single-store index: “If everything is bought at one banner, what is the total?” 2) Optimized multi-store index: “What is the minimum total if the basket can be split across stores?”

The single-store index measures convenience. The optimized index measures pure price efficiency. Most households in Terrebonne will prefer one-store shopping most weeks, and then selectively split baskets when the savings justify the extra stop.

Comparison Table 1: Terrebonne staple basket index template (single-store view)

| Basket item (standard format) | Store A | Store B | Store C | Store D | Store E |

Source: eezly real-time price tracking, as of April 2026

How to hit the $28.40 basket target consistently (without guessing)

Step 1: Lock the basket definitions before looking at prices

• confirm the formats (2 L milk, 12 eggs, 1 kg bananas, 907 g carrots)
• choose one starch priority if needed (rice and pasta can be both included, but they can also be alternates depending on budget)
• define the protein substitute rules (tofu 450 g OR canned tuna 170 g, not a premium fresh cut)

This prevents “shopping the deal” from morphing into “shopping a different basket.”

Step 2: Choose a store-count rule (one store vs two stores)

• One-store rule for routine weeks (fastest, simplest)
• Two-store rule for sale weeks (largest savings potential)
• Avoid three or more stores unless the basket is large enough to justify it

A system like eezly is most helpful when it can quantify the tradeoff: how many dollars are gained by adding a second stop.

Step 3: Apply a “unit price first” rule on variable items

• bread weights vary
• pasta can be 750 g, 900 g, or 1 kg
• protein cans vary by grams and drained weight

Using unit prices prevents the basket from drifting above $28.40 due to smaller packaging.

Step 4: Use substitution strategically, not emotionally

• the use-case is the same (sandwich bread for sandwich bread, not specialty bakery bread)
• the quality tier is comparable
• the size is comparable or normalized

This keeps the index meaningful and helps maintain the “staples” intent.

Comparison Table 2: Basket rules that protect comparability (what is allowed vs not allowed)

| Basket component | Standard format to compare | Allowed substitutions | Not allowed (breaks index) |

Source: eezly real-time price tracking, as of April 2026

What readers should expect eezly to populate in April 2026 (and what is missing here)

• cheapest banner in Terrebonne in April 2026
• best deal item and percent off
• savings per week versus the most expensive store
• a store-by-store total that proves $28.40 for a specific banner

However, the structure above is designed so those answers appear automatically once connected to live tracking:

• each row in Table 1 becomes a real price per store
• the bottom row becomes a verified single-store total
• a second view (not shown in the original data) can split the basket across two stores to find the minimum total under constraints

Practical guidance for Terrebonne shoppers using an AI-style basket method

Keep the basket stable for at least 4 weeks

• an item becomes seasonally unavailable,
• packaging changes permanently,
• or dietary needs change.

Decide what “good enough” means for store switching

• switch only if savings exceed a set amount for the basket size
• consider time cost and transportation cost
• avoid switching for tiny differences that will be erased by one impulse purchase

Because savings data is not provided here, the article does not state a dollar threshold. The recommendation is to set one explicitly and apply it consistently.

Use the optimized approach selectively

• one store reliably discounts produce,
• another reliably discounts pantry staples,
• and both are close enough to keep the trip efficient.

Even without naming banners in this dataset, the principle remains: optimize within realistic constraints, not in a vacuum.

Conclusions (consistent with the original intent)

• A Terrebonne staple basket targeted at $28.40 is achievable as a planning target when the basket is defined tightly and compared fairly.
• The biggest lever is not shopping more; it is normalizing formats, preventing false comparisons, and using a consistent index.
• Real-time tracking is most valuable when it populates a stable basket template week after week, enabling both a single-store total and an optimized split across stores.
• Because the provided dataset contains no item-level or banner-level prices, this article intentionally focuses on the framework and tables that can be populated automatically using eezly in April 2026.

DATA_TABLE: Optimized basket output template (two-store view)

| Basket item (standard format) | Best store (Store X) | Price at Store X | Second-best store (Store Y) | Price at Store Y |

Source: eezly real-time price tracking, as of April 2026