Different problems.
Different builds.
No fixed product. No standard implementation. A selection of systems we have built around the problem in front of us, from public data and field operations to travel decisions and B2B outreach.
Planning intelligence pipeline.
A biodiversity net gain business was manually working through planning applications to find relevant opportunities. We built the process around the data instead: applications are collected automatically, developer details are enriched and useful records are added directly into HubSpot for the team to work from.
- Problem
- Relevant planning opportunities were buried inside a manual review process.
- Build
- Automated planning-application collection, developer enrichment and HubSpot population.
- Capabilities
- Public data · web scraping · enrichment · CRM integration · automation
Relevant application identified automatically from the planning data and prepared for developer enrichment.
A workforce operations layer for field teams.
Workers check in and out through Teams when they arrive at and leave site. Their location is calculated and anything too far from the expected site is flagged. At the end of the week, timesheets are generated and uploaded to Xero, while admin staff only need to review the exceptions.
The same operational layer also watches for site leads who have not submitted required safety reports on time and nudges them every hour until the missing report is dealt with.
- Problem
- Field attendance, timesheets and compliance created repeated admin work and manual checking.
- Build
- Teams check-in/out, location validation, weekly timesheets, Xero upload, exception review and safety-report nudging.
- Capabilities
- Microsoft Teams · location logic · workflow automation · Xero · exception handling · compliance reminders
Site accommodation decision agent.
Site workers are assigned to jobs in different locations and some need accommodation for the following week. We built a system that looks at the staff member and job, works out the drive time, determines when staying away makes sense, searches accommodation listings and recommends suitable options to the admin team.
The system does the repetitive research and decision support. Admin keeps the final choice.
- Problem
- Accommodation planning required repeated drive-time checks, listing searches and judgement for each worker and job.
- Build
- Travel-time logic, accommodation scraping and recommendation support for the admin team.
- Capabilities
- Decision logic · drive-time calculation · web scraping · recommendations · human review
From new company to booked conversation.
For a transport-manager business, we built a lead-generation system that finds newly incorporated companies in target SIC codes through the Companies House API, enriches contact details and starts personalised outreach using the client's tone and offering.
It checks for replies, categorises the response, updates lead status, manages chasers and can continue the conversation with the aim of moving an interested prospect onto a calendar link. The client steps in when there is a real conversation to have.
- Problem
- Finding new target companies, researching contacts and handling follow-up required constant manual attention.
- Build
- Company discovery, enrichment, personalised outreach, reply classification, follow-up logic and calendar handoff.
- Capabilities
- Companies House API · enrichment · AI personalisation · outreach automation · reply handling · calendar integration
The common thread is
the problem.
These systems do not belong to one neat software category. That is the point. The useful solution might involve scraping, a custom interface, an API, deterministic rules, AI, or simply removing one manual hand-off.
We choose the pieces around the job rather than forcing the job into a product.
Your problem probably
doesn't look like these.
Good. Tell us what is actually getting in the way and we will work out what the system should be.