Fieldline

Construction jobs carried through to invoice preparation

Services: AI ERP & CRM ; AI Workflow Automation ; AI Fintech
Delivery: Multi-tenant construction operating platform, followed by an AI billing extension. Portfolio alias.

The construction operation used paper estimates, spreadsheet schedules, and manual billing split between crews and office staff, according to the original project brief.

The same job had to be interpreted more than once: first to schedule it, then to manage it, and later to invoice it. The client needed shared records that matched the movement of work between the office and the field.

The initial platform covered customer and vendor records, jobs, crews, roles, scheduling, field operations, and management dashboards.

The later billing extension used completed-job data for AI-assisted invoice preparation. Because the job records were already in the product, the extension could use operational context rather than asking office staff to reconstruct it in a separate tool.

1. Establish the tenant, role, and record structure.
2. Connect jobs, crews, and schedules.
3. Add working views for field operations and management.
4. Extend the same product into AI-assisted billing.

The operating platform came first; AI-assisted billing extended the shared job record.

Keep a job identifiable across departments. Scheduling and billing must refer to the same operational record.

Build the AI extension on recorded work. The invoicing layer uses completed-job information rather than a free-form request without source context.

Retain separate business actions. Preparing invoice content is different from approving charges, issuing the invoice, or collecting payment.

The delivered platform puts field operations and billing in one product. The later AI extension reused the initial job-management foundation.

The project record supports this delivery progression. It does not provide an invoice-volume count, billing-time baseline, or reduction in collection delay, so no financial lift is invented.

Documented stack: Next.js, Node.js, PostgreSQL, Stripe, OpenAI, multi-tenant application architecture.

Hypothetical planning example, not a measured project result or a performance forecast. All volumes and timings below are assumed.

Recover completed-job details, prepare the invoice, and check the proposed charges.

• Workload: 100 completed-job invoice preparations per month.
• Manual handling: 12 minutes per item.
• Assisted handling, including human review: 5 minutes per item.
• Additional exception handling: 60 minutes in the same period.
• Modeled difference: 100 x (12 - 5) - 60 = 640 minutes (10 hours 40 minutes) per month.

The example reserves another 60 minutes per month for missing field records and billing corrections. Approval, issuance, and collection remain separate steps. This is potential staff capacity, not cash savings; setup, training, and software costs are excluded.

How to validate: Measure from an invoice-ready job record to an approved invoice draft. Include manual corrections and missing-data follow-up; track completion-to-issuance delay separately.

Jobs, crews, scheduling, and billing on the same records.
Fieldline began as a construction operating platform and expanded into AI-assisted invoice preparation using completed-job data.

We built Fieldline's job and crew platform first, then extended it with AI-assisted billing. The invoice workflow could use the completed-job records already in the product.