We extract organic grocery listings, veg box contents, recipe instructions, and farm origin data from Riverford. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Veg Boxes objects from riverford.co.uk. All fields typed and schema-versioned.
"box_id": "VB-001", "name": "Medium Organic Veg Box", "price": 16.85, "size_category": "Medium", "veg_item_count": 8, "items_included": "['Potatoes', 'Carrots', 'Onions', 'Cabbage', 'Leeks', 'Beetroot', 'Spinach', 'Mushrooms']", "in_stock": true
| # | box_id | name | price | size_category | veg_item_count | fruit_item_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Recipes objects from riverford.co.uk. All fields typed and schema-versioned.
"recipe_id": "REC-492", "title": "Roasted squash & sage risotto", "prep_time_mins": 15, "cook_time_mins": 40, "servings": 2, "allergens": "['Dairy', 'Celery']", "instructions": "['Preheat oven to 200C.', 'Roast squash for 25 mins.']"
| # | recipe_id | title | prep_time_mins | cook_time_mins | servings | ingredients |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Farm Shop objects from riverford.co.uk. All fields typed and schema-versioned.
"product_id": "PROD-882", "title": "Organic Milk, Whole", "category": "Dairy & Eggs", "price": 1.45, "weight_volume": "1L", "origin_farm": "Riverford Dairy", "organic_cert": "Soil Association"
| # | product_id | title | category | sub_category | price | weight_volume |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing objects from riverford.co.uk. All fields typed and schema-versioned.
"product_id": "PROD-882", "current_price": 1.45, "unit_price": 1.45, "unit_measure": "per litre", "in_stock": true, "seasonal_status": "Year-round", "scraped_at": "2026-05-12T09:14:00Z"
| # | product_id | current_price | unit_price | unit_measure | in_stock | seasonal_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories objects from riverford.co.uk. All fields typed and schema-versioned.
"category_id": "CAT-04", "name": "Meat & Poultry", "parent_category": "Farm Shop", "product_count": 84, "is_seasonal": false, "sort_order": 3, "url": "/shop/meat-poultry"
| # | category_id | name | parent_category | url | product_count | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Riverford scraper navigates seasonal catalogue shifts, postcode-dependent availability, and complex recipe structures to deliver clean, normalised datasets.
Extract the specific items included in weekly veg boxes, tracking seasonal crop rotations and substitutions.
Capture recipe titles, preparation times, step-by-step instructions, ingredient lists, and allergen warnings.
Track the specific farm origin and organic certification details for individual grocery items.
Extract retail prices alongside calculated unit costs (e.g. price per 100g) for accurate market comparisons.
Monitor inventory status and seasonal availability flags across the entire product catalogue.
Map products to their primary and secondary categories, maintaining the site hierarchy.
Extract structured allergen data and dietary tags (vegan, vegetarian, gluten-free) from recipes and products.
Simulate delivery postcodes to capture region-specific availability and delivery window data.
Configure continuous pipelines at daily or weekly cadences to track seasonal catalogue updates.
Brief in. Clean data out.
Select categories, recipe types, or specific veg boxes. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, managing postcode sessions and dynamic content.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Riverford relies on dynamic basket states and seasonal catalogue shifts. Here is how we maintain pipeline stability.
Grocery platforms often gate product visibility based on delivery postcodes. Our crawlers manage persistent sessions, simulating regional postcodes to capture accurate availability data.
Riverford uses dynamic JavaScript for basket updates and recipe filtering. We use Playwright to execute full browser sessions, ensuring all client-side rendered content is captured.
Agricultural catalogues change structure frequently based on seasons. Our selector strategy uses fallback chains to ensure data extraction continues smoothly despite layout updates.
We parse and normalise inconsistent weight and volume strings into structured numeric fields, enabling direct quantitative analysis.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs, providing a clean changelog of seasonal updates and price shifts.
Supermarkets and premium grocers monitor Riverford pricing to benchmark their organic and premium product lines.
Economic analysts track price changes across organic produce and meat to measure sector-specific inflation.
Food platforms ingest structured recipe data, ingredients, and preparation steps to expand their content libraries.
Researchers map farm origins and seasonal availability to understand organic supply chain dynamics.
FMCG brands analyze veg box compositions and seasonal product launches to identify consumer trends.
ML teams use structured ingredient lists and nutritional data to train dietary recommendation engines.
"Riverford provides a highly structured view into seasonal organic agriculture and premium grocery pricing, but tracking weekly availability requires persistent session management."
Extracting data from Riverford involves managing postcode-dependent availability, dynamic basket states, and seasonal catalogue shifts. DataFlirt handles the session orchestration and JavaScript rendering so you receive structured datasets without maintaining custom scrapers.
Everything supported by our riverford.co.uk scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles orchestration and retry logic. Playwright handles JavaScript rendering, cookie sessions, and postcode simulation.
We maintain pools of UK residential ISP proxies to avoid rate limits and blocklists during catalogue extraction.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About riverford.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and recipe data is generally permissible. DataFlirt targets only public, non-authenticated pages. We do not extract personal customer data or bypass authentication walls.
Our crawlers establish persistent sessions and simulate specific UK postcodes to ensure the captured data reflects actual regional availability and delivery windows.
Pipelines can be configured for daily runs to capture overnight catalogue updates, or weekly runs to align with Riverford's seasonal box changes.
Yes. We extract the specific items listed for each box size and maintain a time-series history, allowing you to track crop rotations and seasonal shifts.
Yes. Recipe extraction includes titles, preparation times, ingredient lists, step-by-step instructions, and nutritional information.
Yes. We provide a sample run covering a subset of categories or recipes so you can validate the schema and data quality before proceeding.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off recipe extraction or continuous monitoring of organic grocery pricing, we scope, build, and operate the pipeline. Tell us what you need.
