Anonymous agency · Internal AI operations platform · 2 years
An internal operations platform, two years on
An internal AI operations platform for an agency. It started as a first build, grew into a two-year partnership, and became the platform the agency's team runs its operations on.
44
internal users
The scale the platform supports, not measured time savings or revenue.
400+
connected properties
The scale the platform supports, not measured time savings or revenue.
~220k
rows a day, on average
The scale the platform supports, not measured time savings or revenue.
1Many sources, one shared platform
The challenge
From a first build to the platform a team runs on.
A first build grew into a two-year partnership, and the platform became what the agency’s team runs its operations on. It had to carry a lot of data and operational complexity without asking non-technical users to understand the systems underneath it.
Fragmented work
Prompts, outputs, quality checks, analytics and reporting were spread across separate people and tools.
Scale
Hundreds of connected properties needed dependable data movement and refresh logic.
Non-technical users
Complex configuration and analytics had to make sense without exposing implementation detail.
My role
I ran this end to end, from the first call through discovery, scope, solution design and leading the build with the Adamant Code team, to launch. After the first build, the work continued as a two-year partnership.
- First call
- Discovery
- Scope & proposal
- Solution design
- Build lead
- Launch
- Iteration
The scale the platform supports, not measured time savings or revenue. Diagram only: the client is anonymous.
What I built
One platform for the team’s operations.
The platform connects operational data, user requests, AI generation and review in one place. With the Adamant Code team, I worked across product strategy, workflow design, data engineering, AI behaviour, dashboard UX, permissions and reliability. The aim was to make complex internal operations feel like one coherent product.
- Scheduled pipelines collect and refresh the operational data from the connected properties.
- A user starts from a dashboard or a natural-language request, without managing the systems underneath.
- The platform generates an internal output or analyses the operational data.
- The result stays in the product, so the user can check its quality and move on to the next step.
The product beyond the AI
Built for everyday use.
A platform a team runs on has to work well past a single good request. Permissions, scheduled work and reliability controls carry the team through recurring operations.
Operational data pipelines
Large recurring data flows connect hundreds of properties to internal dashboards and workflows.
Natural-language analytics
Users explore operational information without working directly with the data layer.
Roles and scheduled work
Permissions, recurring jobs and refresh schedules keep access and execution organised.
Reliability controls
Failure handling, API-limit management and data-quality checks support everyday use.
The outcome
Two years on, the team runs on it.
The platform is what the agency’s team runs its operations on, two years after the first build. The numbers below are the scale it supports, not measured time savings or revenue.
44
Internal users. The scale the platform supports.
400+
Connected properties. The scale the platform supports.
