We extract dynamic pricing models, recipe ingredients, nutritional analysis, and customer reviews from Ollie's DTC platform. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Recipes & Nutrition objects from ollie.com. All fields typed and schema-versioned.
"recipe_id": "REC-BEEF-01", "name": "Fresh Beef Recipe", "meat_type": "Beef", "crude_protein_pct": 9.0, "crude_fat_pct": 7.0, "crude_fiber_pct": 2.0, "moisture_pct": 70.0, "caloric_content_kcal_kg": 1540
| # | recipe_id | name | meat_type | ingredients_list | crude_protein_pct | crude_fat_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dynamic Pricing objects from ollie.com. All fields typed and schema-versioned.
"dog_breed": "Golden Retriever", "weight_lbs": 65, "age_months": 36, "activity_level": "Active", "recommended_recipe": "['Beef', 'Chicken']", "daily_calories_target": 1250, "weekly_price_usd": 54.0, "discount_applied_pct": 50
| # | simulation_id | dog_breed | weight_lbs | age_months | activity_level | recommended_recipe |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Customer Reviews objects from ollie.com. All fields typed and schema-versioned.
"review_id": "REV-98234", "author_name": "Sarah J.", "dog_name": "Max", "dog_breed": "French Bulldog", "star_rating": 5, "review_text": "Max's coat has never looked better since switching to the beef recipe.", "verified_buyer": true, "date_posted": "2023-11-14"
| # | review_id | author_name | dog_name | dog_breed | star_rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Blog Content objects from ollie.com. All fields typed and schema-versioned.
"article_id": "BLOG-402", "title": "How Much Should a Puppy Eat?", "category": "Puppy Care", "author": "Ollie Vet Team", "publish_date": "2023-09-22T14:00:00Z", "read_time_mins": 6, "tags": "['puppy', 'nutrition', 'feeding schedule']", "meta_description": "A complete guide to feeding your new puppy."
| # | article_id | title | category | author | publish_date | read_time_mins |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Subscription Plans objects from ollie.com. All fields typed and schema-versioned.
"plan_type": "Full Fresh", "meal_frequency": 14, "transition_schedule_days": 7, "packaging_size_oz": 16.0, "delivery_cadence_weeks": 2, "price_per_meal_usd": 3.85, "shelf_life_days": 14
| # | plan_type | meal_frequency | transition_schedule_days | packaging_size_oz | delivery_cadence_weeks | price_per_meal_usd |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Ollie's site relies heavily on interactive state machines, specifically its onboarding quiz. Our infrastructure simulates user behaviour to map the entire pricing and recommendation matrix.
Capture ingredient lists, guaranteed analysis (protein, fat, fiber, moisture), and caloric density for all active meal plans.
Automate the onboarding quiz across thousands of breed, weight, age, and activity permutations to map Ollie's hidden pricing algorithm.
Extract customer testimonials, star ratings, and dog profile data (breed, age) to correlate product satisfaction with specific demographics.
Pull full text, metadata, and taxonomy from the Ollie blog to analyse their content marketing and organic search strategy.
Document delivery cadences, transition protocols, and packaging variants offered across different subscription models.
Render the React frontend to capture nested JSON data payloads injected into the DOM before they are painted to the screen.
Bypass rate limits and bot-protection layers using residential proxies and TLS fingerprint spoofing.
Detect changes in pricing, recipe formulations, or promotional offers using hash-based diffing. Only receive new or modified records.
Structure raw ingredient strings into queryable arrays for direct comparison against competitor formulations.
Brief in. Clean data out.
Specify required data points: recipe catalogues, quiz-based pricing matrices, or historical review archives.
We configure Playwright scripts to navigate the onboarding flow and map the pricing API endpoints.
Schema validation, null-rate checks, and pricing logic verification against manual test cases.
Data pushed to your S3 bucket, BigQuery dataset, or Snowflake stage via JSON, CSV, or Parquet.
Extracting data from Ollie requires more than simple HTTP GET requests. The site is a React application heavily reliant on user input state.
Ollie does not publish a flat pricing page. Prices are calculated dynamically based on a dog's profile. We use Playwright to systematically inject thousands of breed, weight, and age combinations into the quiz interface to map the entire pricing matrix.
The site is built as a Single Page Application. Standard HTML parsers fail to capture dynamic content. We execute full browser sessions to wait for API responses and component hydration before extracting the DOM.
Where possible, we bypass the frontend entirely by reverse-engineering Ollie's internal GraphQL and REST endpoints, allowing for faster, more reliable extraction of recipe and review data.
Ollie serves different promotional offers based on user location. We route traffic through US-based residential IP addresses to capture accurate regional pricing and avoid datacenter IP bans.
DTC brands frequently A/B test their funnels. Our pipelines use resilient selectors and API response validation to ensure extraction continues smoothly even when the UI changes.
Rival pet food brands track Ollie's dynamic pricing algorithms and promotional discounts to optimise their own subscription tiers.
Product development teams analyse guaranteed analysis metrics and ingredient lists to formulate competing fresh dog food recipes.
Private equity analysts evaluate pricing power, recipe variety, and customer sentiment to model the DTC pet food sector.
Marketing agencies mine customer reviews to identify common pain points (e.g., transition issues, packaging complaints) for targeted advertising.
SEO professionals scrape the Ollie blog taxonomy to map high-performing canine health topics and content gaps.
Ingredient suppliers monitor shifts in recipe formulations to forecast demand for specific proteins and supplements.
"In the DTC subscription model, the pricing matrix is the product. Without simulating the user journey, you are flying blind on competitor strategy."
Extracting intelligence from modern DTC brands requires navigating complex state machines and interactive funnels. DataFlirt automates the entire user journey—from initial quiz inputs to final checkout pricing—delivering structured competitive intelligence without the engineering overhead.
Everything supported by our ollie.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Playwright manages complex browser states, handling cookies, local storage, and session tokens necessary to navigate multi-step onboarding funnels.
Network traffic analysis identifies backend data sources, allowing Scrapy to query internal APIs directly for faster, more stable extraction.
Redis and Celery distribute thousands of quiz simulation permutations across a Kubernetes cluster, completing exhaustive matrix mapping in hours.
Data delivered to where your team already works — no new tooling required.
About ollie.com scraping, legality, and pipeline operations.
Ask us directly →We build automated state machines using Playwright that programmatically fill out the onboarding quiz. By feeding a matrix of dog breeds, weights, and ages into the pipeline, we generate thousands of unique quotes to reverse-engineer the underlying pricing algorithm.
Yes. We run scheduled extractions of the nutritional analysis and ingredient lists. Our change detection system highlights modifications to crude protein levels, new ingredient additions, or formula substitutions over time.
We strictly extract publicly accessible data such as marketing copy, recipe details, and unauthenticated pricing quotes. We do not bypass login walls to access private user data or order histories. Clients are responsible for ensuring their specific use cases comply with terms of service and applicable laws.
Because the pricing matrix requires thousands of simulated browser sessions, full matrix refreshes are typically scheduled on a weekly or bi-weekly cadence. Specific high-priority profiles can be monitored daily.
Yes. We paginate through all available customer testimonials, capturing the review text, star rating, verified buyer status, and the associated dog profile data provided by the reviewer.
DTC brands frequently update their funnels. We monitor pipeline health using Prometheus and Grafana. If an A/B test or site update breaks a selector, our system alerts the engineering team to deploy a fix, ensuring minimal downtime.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop guessing competitor pricing. Let DataFlirt build a reliable pipeline to extract Ollie's recipes, reviews, and dynamic subscription tiers. Contact our engineering team to define your schema.
