We extract technical lighting specifications, photometric data, Philips Hue catalogues, and B2B project portfolios from Signify. 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 Consumer Lighting objects from signify.com. All fields typed and schema-versioned.
"product_id": "929002216601", "brand": "Philips Hue", "product_name": "White and colour ambiance Play light bar", "category": "Smart lighting", "wattage": "6.6W", "lumen_output": "530", "colour_temperature": "2000-6500K", "ean_code": "8718699706131"
| # | product_id | brand | product_name | category | price | currency |
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Complete list of extractable fields for Professional Luminaires objects from signify.com. All fields typed and schema-versioned.
"sku": "910505101234", "family_name": "CoreLine Panel", "application_area": "Office", "light_source_color": "840 neutral white", "driver_included": true, "ce_mark": true, "warranty_period": "5 years", "input_voltage": "220-240V"
| # | sku | family_name | application_area | light_source_color | driver_included | optic_type |
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Complete list of extractable fields for Technical Specs objects from signify.com. All fields typed and schema-versioned.
"product_code": "8718696073408", "initial_luminous_flux": "3400 lm", "luminous_flux_tolerance": "+/-10%", "initial_led_luminaire_efficacy": "100 lm/W", "median_useful_life_l70b50": "50000 h", "ambient_temperature_range": "-10 to +40 °C", "dimmable": false, "housing_material": "Steel"
| # | product_code | initial_luminous_flux | luminous_flux_tolerance | initial_led_luminaire_efficacy | initial_chromaticity | median_useful_life_l70b50 |
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Complete list of extractable fields for Distributor Network objects from signify.com. All fields typed and schema-versioned.
"partner_id": "PTN-8472", "company_name": "Luminaires Direct", "partner_type": "Certified System Integrator", "city": "London", "country": "UK", "latitude": 51.5074, "longitude": -0.1278, "certifications": "['Interact Pro']"
| # | partner_id | company_name | partner_type | address_line_1 | city | postal_code |
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Complete list of extractable fields for Project Portfolios objects from signify.com. All fields typed and schema-versioned.
"project_id": "PRJ-9921", "title": "Smart City Lighting Upgrade", "industry": "Public Spaces", "location": "Amsterdam", "completion_year": "2024", "products_used": "['CityTouch', 'Luma']", "energy_savings_pct": 65, "image_urls": "['https://signify.com/assets/prj9921-1.jpg']"
| # | project_id | title | client_name | industry | location | completion_year |
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Our Signify pipeline handles the entire catalogue: consumer Philips Hue products, professional Interact systems, and dense technical specification tables.
Extract data across Philips, Interact, Color Kinetics, and all other Signify sub-brands from a single pipeline.
Capture lumen output, wattage, chromaticity, beam spread, and housing materials structured into queryable fields.
Normalise product catalogues across different country locales, capturing regional SKUs and EAN codes.
Extract professional luminaire specifications, driver compatibility, and DALI control interface details.
Scrape partner locators to build a global database of certified system integrators and distributors.
Track energy efficiency classes, median useful life metrics, and power consumption data for compliance reporting.
Map individual SKUs back to their parent product families and system architectures.
Extract IoT compatibility details for Philips Hue and WiZ products, including Zigbee and Matter support.
Receive only updated products and modified specifications on subsequent runs to reduce processing overhead.
Brief in. Clean data out.
Provide target regions, product categories, or specific brand silos. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle regional redirects, and map the complex specification tables.
Schema validation, null-rate checks, and unit normalisation (e.g., standardising lumen formats) before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Signify's web properties use complex configurators and heavily nested specification tables. Here is how we extract clean data.
Professional lighting products often use JavaScript-based configurators to select optics, drivers, and mounting options. We use Playwright to iterate through these combinations and extract the underlying SKU for every valid configuration.
Technical data is presented in deeply nested HTML tables that vary by product category. Our extraction logic normalises these tables into a consistent key-value structure, regardless of the underlying DOM hierarchy.
Signify aggressively redirects traffic based on IP geolocation. We use region-specific residential proxies and strict cookie management to ensure we extract the correct localised catalogue without being bounced to a global splash page.
Case studies and distributor maps rely on infinite scrolling and dynamic map bounding boxes. We intercept the underlying XHR requests to extract the raw JSON payloads directly, ensuring 100% coverage without UI rendering issues.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Lighting manufacturers track Signify's product specifications, efficacy metrics, and warranty terms to benchmark their own portfolios.
Sustainability consultants extract lumen-per-watt ratios and energy classes to build compliance databases for large-scale retrofits.
B2B sales teams scrape partner locators to identify certified system integrators and expand their own distribution networks.
IoT platforms extract protocol compatibility (Zigbee, Matter) from the Philips Hue catalogue to maintain integration databases.
Construction firms aggregate technical specifications across product families to automate bill-of-materials generation for tenders.
Analysts track the shift from conventional lighting to LED and IoT systems by monitoring catalogue composition over time.
"Signify holds the most comprehensive technical lighting dataset globally, but extracting photometric data and regional SKUs requires dedicated infrastructure."
Extracting data from Signify involves navigating complex product configurators, regional catalogue variations, and deeply nested technical specification tables. DataFlirt manages this complexity so your engineering team receives clean, normalised data ready for immediate querying.
Everything supported by our signify.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.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering for product configurators. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions to bypass aggressive locale-based redirection and access specific country catalogues.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About signify.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product catalogues, technical specifications, and distributor lists is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data or circumvent the MySignify authentication walls.
We use region-specific residential proxies and strict session management to ensure we extract the exact catalogue for your target market, preventing Signify's servers from redirecting the crawler to a default global page.
Yes. Professional luminaires often require selecting options (driver, optic, mounting) before a specific SKU is generated. We use Playwright to iterate through these combinations and capture the resulting technical data.
Signify uses varied table structures depending on the product family. We build custom normalisation logic that maps these disparate tables into a consistent, flat JSON schema, standardising keys like 'Lumen output' and 'Wattage'.
For lighting catalogues, weekly or monthly cadences are typical. If you require faster updates for specific product lines, we can configure daily delta runs.
Yes. We provide a sample run of a specific product family or category as part of the pre-engagement scoping process so you can validate the schema and normalisation logic.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete global catalogue extraction or targeted technical specification tracking, we scope, build, and operate the pipeline. Tell us what you need.
