Internal data population platform for an enterprise operations team
A custom data-population and synchronisation platform that lets an enterprise operations team load, reconcile and distribute data across multiple internal systems, with a built-in MCP server so the same data can be queried and mutated by AI assistants.
The challenge
An enterprise operations team was spending weeks every quarter loading, cleaning and synchronising data across a tangle of internal systems. Every update was manual. Every mistake had to be found by hand. Every new data source started with "send us a CSV and we'll figure it out". The team wanted a platform where data could be loaded once, validated, reconciled and then pushed out to wherever it needed to go — with an audit trail for every operation.
They also wanted the platform usable from the AI assistants the team was already adopting, without giving those assistants an unguarded pipe into the underlying database.
Our approach
- Built the platform on Laravel with Filament as the operational layer — enterprise-grade forms, tables, roles and audit history out of the box.
- Modelled the data-ingestion pipeline as a sequence of validated, replayable stages so every upload can be retried, compared or rolled back.
- Implemented a role-based permission model mapped to the team's real organisational structure, with least-privilege access to sensitive data sources.
- Built a Model Context Protocol (MCP) server alongside the platform so the team's AI assistants could safely query and mutate data, subject to the same permission model, without bypassing audit logging.
- Integrated with AWS S3 for artefact storage and Stripe for internal billing reconciliation.
Architecture highlights
- Laravel + Filament for the administrative surface
- PostgreSQL with role-scoped, auditable data access
- AWS S3 for raw and processed artefact storage
- Stripe for internal billing reconciliation
- MCP server exposing a safe, permission-scoped API for AI assistants
- Audit log covering every upload, every edit, every export
Outcome
- Weeks of manual data ops compressed into a repeatable, auditable workflow
- AI assistants safely integrated through the MCP server, with the same permission model as human users
- Every mutation traceable to a user, a source artefact and a timestamp
- New data sources onboarded without custom code by reusing the validated-stage pipeline
Let's build something that ships.
Tell us about your project. A senior engineer will reply within one business day, no pitches, no forms-before-forms.