AI SaaS development for construction records

The problem

Construction projects produce a large amount of information in meetings, site discussions, notes and messages between stakeholders. Decisions often begin as conversation but eventually need to exist as structured, reviewable and auditable project records.

Those records carry weight. They can influence commercial decisions, project responsibilities and later disputes, so output from an AI model could not simply be treated as final. The requirement was a reliable workflow around that output, one a project team could trust.

What we did

We designed the platform as a controlled sequence: capture, process, structure, review, approve, generate and retain. Project communication enters as voice or text, is processed with AI-assisted extraction in more than one language, and is shaped into structured project records. Those records go through human review and approval before documents are generated and retained with version history.

Human review is part of the workflow by design. AI-generated content is not treated as authoritative; it moves through validation and approval before it becomes part of the official project record. The system assists the people responsible for the record rather than acting on its own.

The platform runs on Amazon Web Services. Voice notes are transcribed with Amazon Transcribe, AI-assisted extraction runs on foundation models through Amazon Bedrock, generated documents and their versions are stored in Amazon S3, and sign-in and roles are managed with Amazon Cognito. Using managed services for speech, models, storage and identity kept the engineering effort on the workflow itself.

Around that workflow sit the parts of a production SaaS product: project management, user and role management, tenant separation so each customer's data stays isolated, permissions, document history and auditability, responses from external stakeholders, security controls, and usage and operational controls. The architecture also accounts for failure handling in AI processing, the mix of structured and unstructured information, secure external interactions and future scale.

What changed

The platform shows how generative AI can be built into a business workflow where reliability, traceability and human control matter as much as automation. AI takes on the work of turning unstructured conversation into structured documentation, while people keep the final say over what enters the record.

The objective was an operational product rather than a demonstration, and the engineering reflects that: data isolation, permissions, audit trails and failure handling are part of the architecture alongside the AI integration itself.