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Custom SoftwareCRMs, portals, dashboards + internal tools. Automation + IntegrationsConnected workflows + automatic hand-offs. DataCollect, clean + monitor business data. AIAgents, assistants + knowledge systems. WebWebsites, performance + conversion.
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Data collection + completion

Find it. Clean it. Make it useful.

Collect information from websites, files and existing systems, then match records, fill in the useful gaps and keep it updated so your business has data it can actually trust and use.

Start with the data → Build an example dataset
Source-aware, structured and traceable Collection · matching · filling gaps · 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 A WORKING SYSTEMMore than a spreadsheet.
Public websitesAutomated collectionConnected systemsFill gapsMonitoringDelivery
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, files, feeds or the systems you already use.

WEBCompany pages

Public details in consistent fields.

COLLECT
SYSTEMExternal service

Clean records ready for your systems to use.

SYNC
FILESCSV + exports

Existing lists and operational data.

IMPORT
SYSTEMInternal CRM

Customer and account context.

MATCH
02

Clean

Standardise names and formats, remove duplicates and work out which records actually represent the same thing.

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

Fill the gaps

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

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 checked again.

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 · alerts
STRUCTURED ✓
Public data + web scraping

Collect public information at a scale that manual research cannot.

For the right use case, automated website data collection can turn thousands of repetitive lookups into a clean dataset. The system can collect the information, match records, remove duplicates, fill missing details, keep it updated and deliver it where you need it instead of leaving you with raw page data.

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 small spelling differences and duplicates as different companies.

04 / DELIVER

Put the data to work

Send the finished information into a database, CRM, report, internal tool or the next automated process.

Data quality

More records are useless if you cannot trust them.

A serious data build needs rules for matching records, checking quality and keeping track of where each value came from, not just a large row count.

ID

Record matching

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

  • Names + domains
  • Location matching
  • Matching confidence
VL

Quality checks

Check formats, required fields and obvious conflicts before bad data moves into another system.

  • Required-field checks
  • Format checks
  • Review queues
SR

Know where it came from

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

  • Source URLs
  • Collection time
  • Confidence notes
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

Fill missing details

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 clean data into useful 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, files or internal systems needed to create the initial record.

  • What gets collected
  • Collection schedule + limits
  • Keep the source
03 / RESOLVE

Clean + complete

Match records, remove duplicates, check fields and add the context that makes the data more useful.

  • Record matching
  • Quality checks
  • Fill missing details
04 / USE

Deliver + monitor

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

  • CRM / dashboard / other system
  • 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, fill in, monitor or connect. We can work out the right sources and system around it.

Start a project →