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Data collection + enrichment

Find it. Clean it. Make it useful.

Collect information from websites, APIs, files and existing systems, then match, enrich and monitor it so the result is structured data your business can actually trust and use.

Start with the data → Build an example dataset
Source-aware, structured and traceable Collection · matching · enrichment · monitoring
ZF
Record resolverEvery field keeps its source
MONITORING
RESOLVED COMPANY

Northstar Travel

94% MATCH
LEGAL NAMENorthstar Travel LtdCompanies data
WEBSITEnorthstar.co.ukWeb match
DIRECTORJames WalkerPublic source
SECTORTravelClassification
LOCATIONEdinburgh, UKCRM
PHONENeeds review2 candidates
FROM INFORMATION TO INFRASTRUCTUREMore than a spreadsheet.
Public dataWeb scrapingAPIsEnrichmentMonitoringData pipelines
The four jobs

Data work is more than collecting rows.

The valuable part is usually the whole lifecycle: finding the information, fixing the inconsistencies, filling the useful gaps and keeping the result current.

01

Collect

Bring information together from public websites, APIs, files, feeds or the systems you already use.

WEBCompany pages

Public details and structured fields.

COLLECT
APIExternal service

Reliable machine-readable records.

SYNC
FILESCSV + exports

Existing lists and operational data.

IMPORT
SYSTEMInternal CRM

Customer and account context.

MATCH
02

Clean

Normalise names, remove duplicates, reconcile formats and decide which records actually represent the same thing.

COMPANYLOCATIONSTATE
Northstar Travel LtdEdinburghKeep
North Star TravelEdinburghDuplicate
Atlas TelecomGlasgowKeep
Harbour EventsLondonKeep
INPUT1,284
DUPLICATES43
CLEAN1,241
03

Enrich

Fill the fields that make the record more useful: website, director, location, sector, technology, contact information or whatever matters to the workflow.

BEFORE

Northstar Travel

WebsiteMissing
DirectorMissing
SectorUnknown
→
AFTER

Northstar Travel

Websitenorthstar.co.uk
DirectorJames Walker
SectorTravel
04

Monitor

Watch the information that matters for changes, then update the dataset, trigger an alert or kick off the next workflow.

Recent changesLIVE
10:42Atlas Telecom changed website

Domain updated and record revalidated.

09:18Harbour Events changed director

Review task created for account owner.

08:07Northstar Travel unchanged

No action required.

CHANGE DETECTED

Do something with it.

The point of monitoring is not the alert itself. It is what the business can do next.

CRM UPDATE ✓
Dataset builder

Decide what matters. Build around that.

Choose the fields you actually need. The example dataset rebuilds itself around those requirements instead of collecting everything just because it exists.

ZF
Dataset specificationExample company intelligence dataset
READY TO BUILD
PREVIEW

Company dataset

4 FIELDS + COMPANY
EXAMPLE RECORDS1,241
SELECTED FIELDS4
MONITORINGON
Output wherever the business needs it.Database · CRM · dashboard · CSV · API · alerts
STRUCTURED ✓
Public data + web scraping

Collect public information at a scale that manual research cannot.

For the right use case, web scraping and automated public-data collection can turn thousands of repetitive lookups into a structured dataset. The build can include extraction, matching, deduplication, enrichment, scheduling and delivery rather than stopping at raw HTML.

The exact approach depends on the source, the information required and how the resulting data will be used.
PUBLIC DATA SYSTEMFrom source to usable record
SCHEDULED + TRACEABLE
01 / DISCOVER

Find the relevant pages

Navigate listings, search results, categories or known URLs to identify the records that matter.

02 / EXTRACT

Pull useful fields

Collect names, attributes, URLs, public contact details, dates or other structured information.

03 / RESOLVE

Match the same entity

Combine multiple sources without treating spelling variants and duplicates as different companies.

04 / DELIVER

Put the data to work

Feed a database, CRM, report, internal tool, API or downstream automation.

Data quality

More records are useless if you cannot trust them.

A serious data build needs rules around identity, validation and provenance, not just a large row count.

ID

Entity matching

Work out when slightly different records represent the same real company, person or object.

  • Names + domains
  • Location matching
  • Confidence rules
VL

Validation

Check formats, required fields and logical consistency before bad data moves downstream.

  • Required-field checks
  • Type + format rules
  • Exception queues
SR

Source traceability

Keep enough context to understand where a value came from and how much confidence to place in it.

  • Source URLs
  • Collection time
  • Confidence metadata
CH

Change handling

Decide what happens when a tracked field changes instead of silently overwriting useful history.

  • Change detection
  • Update rules
  • Alerts + workflows
The output

The dataset is not the end of the workflow.

Useful data usually has somewhere to go. We can build the collection and the system around it so the information arrives where the business can actually act on it.

CRM

Enrichment

Fill missing company and contact context directly inside the customer record.

UPDATE RECORDS
INTERNAL TOOL

Search + review

Give a team a purpose-built interface for finding, filtering and reviewing the collected information.

WORKSPACE
REPORTING

Dashboards

Turn structured data into operational reporting without manually rebuilding the same spreadsheet.

REPORT
MONITORING

Alerts + actions

When something changes, notify the right person or trigger the next automated workflow.

AUTOMATE ✓
How we approach data

Start with the question. Then design the dataset.

01 / DEFINE

Choose the useful fields

Work backwards from the decision, report or workflow the business is trying to improve.

  • Required fields
  • Source options
  • Output format
02 / COLLECT

Build the intake

Connect the websites, APIs, files or internal systems needed to create the initial record.

  • Collection logic
  • Rate + schedule
  • Source capture
03 / RESOLVE

Clean + enrich

Match entities, remove duplicates, validate fields and add the context that makes the data more useful.

  • Entity matching
  • Validation
  • Enrichment
04 / USE

Deliver + monitor

Put the result into the right system and decide what should happen when the information changes.

  • CRM / API / dashboard
  • Monitoring
  • Alerts + automation
CLIENT FEEDBACK

Thinking with the project matters.

“Nathan has outdone himself in many ways. From thinking along with the project, providing options, very fast communication to bringing together a perfectly smooth end result. My next assignment is already almost ready.”

Dave, Netherlands
Start with the information

What data do you wish you already had?

Tell us what you are trying to collect, clean, enrich, monitor or connect. We can work out the right sources, structure and system around it.

Start a project →