Messy inputs
Useful details arrive through emails, forms, phone notes, spreadsheets, and documents in no consistent shape.
Pre-launch
AI-enabled workflows for messy business work.
DG Workflow helps small businesses turn emails, forms, spreadsheets, documents, and website enquiries into structured, reviewable workflows using practical AI, automation, data handling, and cloud tools.
Pre-launch: no paid checkout active. Public demos use synthetic data. Read about the technical grounding.
Problems DG Workflow tackles
Messy inputs slow good work down. Structure them first.
Manual rekeying
People copy, paste, rename, chase, and rebuild the same context before the real work can start.
Scattered systems
Websites, inboxes, spreadsheets, files, dashboards, and cloud tools each hold part of the workflow.
AI without review
Automation is risky when outputs are treated as final instead of structured, checked, and owned.
BeforeInformation arrives as messy emails, web forms, phone notes, attachments, and spreadsheet rows.
AfterAI helps extract the important fields into a repeatable schema that a person can review.
BeforeThe next action depends on whoever remembers to copy details into the right place.
AfterAutomations can draft tasks, update a tracker, prepare a reply, or create a website/admin note.
BeforeA website is separate from the operational process behind it.
AfterThe site becomes one channel in a wider workflow, connected to follow-up, reporting, and handover.
See it in action
Messy input to reviewable action.
One synthetic private-sector engineering example showing the breadth of the work: AI extraction, structured data, human review, automation, website updates, and reporting.
Step 1 of 5
Start with the real mess.
The synthetic workshop receives job details across a website form, an email reply, a phone note, and a spreadsheet row.
01
Messy input
02
AI schema
03
Review
04
Automations
05
Outputs
Mixed inbound request
Synthetic dataWebsite form
Need repeat batch of aluminium pump brackets, around 40 units. We used you last year. Can someone confirm lead time?
Email reply
Drawing attached says rev B but PO draft says rev A. Material should be 6082-T6. Delivery wanted by next Thursday if possible.
Phone note
Caller asked whether the website lead-time page is still accurate. Mentioned tolerance on the slot is tighter than the old job.
Spreadsheet row
Northline Precision | repeat? yes | qty 40 | owner Sam | source web | value unknown | status waiting
AI extraction schema
WaitingRun extraction to create the schema.
The source messages stay visible. The AI step is only useful once it creates a repeatable structure.
Review and outputs
Human reviewReview flags appear after extraction.
The workflow should make uncertainty harder to miss, not hide it behind confident-looking output.
Sources parsed
0
website, email, phone, sheet
Schema fields
0
repeatable job-intake shape
Review flags
0
kept out of automation until checked
Outputs ready
0
after human review gates
CRM-style record
Pending
Internal tasks
Pending
Customer reply
Pending
Website note
Pending
Report row
Pending
Pre-launch service directions
Practical AI and automation, scoped carefully.
The offer ladder starts by understanding one workflow, then uses websites, AI, automation, data, or reporting only where they create a clearer operational result.
Ask about fit before launchWebsite and workflow review
A practical map of where messy inputs, websites, admin steps, data, and AI opportunities create friction.
- Workflow map
- AI suitability notes
- Recommended first improvement
Workflow automation sprint
A focused build around one repeated task, using AI, APIs, automation, and human review where they help.
- Structured inputs
- Human review
- Handover notes
Website modernisation
A clearer, faster site that supports the workflow behind enquiries, updates, and customer action.
- Clear service pages
- Better enquiry capture
- Workflow-aware content
Dashboard and reporting improvement
Cleaner inputs, repeatable reporting, and dashboard views that make operational work easier to manage.
- Data cleanup
- Dashboard view
- Repeatable reports
How projects will work
Start narrow. Prove the workflow.
01
Discover
Clarify the messy inputs, current tools, data sensitivity, and what would count as useful progress.
02
Map
Turn the current workflow into a clear before-and-after plan with limits and assumptions visible.
03
Build
Create the AI-supported extraction, automation, site change, dashboard, or lightweight tool.
04
Review
Test realistic examples, flag uncertainty, keep people in control, and document where the system can fail.
05
Scale
Only extend what works, with ownership, provider choices, cost, and support boundaries made clear.
Start small Prove value Scale with confidence
Trust and data handling
Read the privacy notice ->
Your data. Handled properly.
Minimum useful data
Only collect what the workflow needs. Public demos use synthetic business data.
Human-reviewed AI
AI can extract, summarise, classify, or draft, but people review important outputs before use.
Plain-English limits
Assumptions, exclusions, handover notes, and known failure modes are documented clearly.
Client ownership
Avoid hidden dependencies on personal accounts. Hosting, tools, and access are agreed upfront.
Pre-launch
Talk through one workflow before launch.
We're in pre-launch mode. Email to discuss a possible future AI-enabled workflow, website, or reporting improvement; no paid services or payment capture are active yet.
No pressure chat15-minute discovery call
Practical adviceHonest and helpful
Clear next stepsIf it is a good fit
Email DG Workflow
hello@dgworkflow.com