Scenario / 01
Scheduled collection
The system visits allowed sources, records fetch time, and stores only validated records.
1Source2Fetch3Check4Store
Data Analytics, procurement, and product teams that need recurring external data
Fresh data from sites, APIs, and files — without manual exports
We design durable collection that respects source structure, limits, format changes, and result quality requirements.
01Source
02Snapshot
03Check
04Store
OutputFresh dataset
Structure or completeness changes raise a signal, not a silent failure.
Practical outcome
Sources are collected on a schedule, records are validated, and delivered in an agreed schema
Problem
Exports are copied by hand, formats diverge, and one-off scripts break without an alert
Primary route
source → fetch → normalize → validated dataset
Working scenarios
From source to a validated record
Demonstration routes, not client results. Exact logic depends on your rules, data, and systems.
Scenario / 02
Change control
New values are compared with the previous snapshot; meaningful changes enter a report or alert.
1Snapshot2Compare3Change4Signal
Scenario / 03
Unified directory
File and API data map to one schema, duplicates merge, disputed rows go to review.
1Sources2Normalize3Dedupe4Export
Solution scope
Collection and control loop
- Source and field specification
- Parser and scheduler
- Cleanup and normalization
- Error monitoring
- Data format documentation
Data and systems in the loopREST APIHTMLXML / CSVGoogle SheetsDatabasesWebhooks
A good fit for
Which data streams we collect
Analytics, procurement, and product teams that need recurring external data
Offer monitoring
Regular collection of prices, availability, attributes, or other open parameters from agreed sources.
Catalog collection
Structured cards from sites, APIs, XML, CSV, and other machine-readable formats.
Information aggregation
Merging sources, cleaning values, and preparing one dataset for analysis.
Engagement trigger
Why manual export stopped working
You need regular fresh data with completeness and change control
- 01Data is copied by hand on a schedule
- 02One-off scripts break without notice
- 03Sources use different names and formats
- 04Completeness and freshness of exports are unclear
Fit / boundaries
What must not go unchecked
Source rules, API availability, and collection legality are checked before build
01We only collect data that does not violate source rules or law
02Anti-bot protection, CAPTCHA, and private cabinets need separate research
03Source changes may require parser updates, so monitoring is included
How we launch
From a real process to a working loop
Tools are chosen after we verify inputs, exceptions, and the success criterion.
- 01
We research sources
We check API access, usage rules, robots.txt, structure, and expected volume.
- 02
We lock the data model
We define required fields, types, units, and criteria for a valid record.
- 03
We build collection
We implement fetch, queues, rate limits, retries, and result storage.
- 04
We add control
We track gaps, anomalies, structure changes, and source outages.
- 05
We deliver data
We export to API, sheet, database, or analytics.
Can you scrape any website?+
No. We first check terms of use, data availability, robots.txt, technical limits, and legality of the intended scenario.
What if the site changes markup?+
The system should detect completeness drops or structure changes and alert. Extraction rules are then adapted.
What output formats are available?+
JSON, CSV, sheet, database, or API. Format follows the system that will consume the data next.
Can data refresh automatically?+
Yes. We set a schedule or event trigger plus retry and freshness rules.
Do you clean and merge data?+
Yes when that is in scope: unify formats, remove clear duplicates, and flag disputed rows.
First step
Assess data collection
We'll map inputs, exceptions, and constraints. You leave with a priority scenario and a next step — no obligation to start a project.
Assess data collectionService: Data parsers