Case study / Operations and AI concepts
Core AI
A company-agnostic AI operations platform concept
Period
Product concept
Role
Product direction and case study
Type
Company-agnostic product direction
Product preview
Illustrative concept screens · Select any image to zoom
Product overview: illustrative concept screen
Core AI · Operations workspace
Concept previewIndependent product direction · synthetic demo records only
01 / Operations
Service overview
A sample view of service status, recent changes, and incidents a team may want to review.
Services in view
04
Fictional services
Needs review
01
Synthetic incident
Recent changes
02
Demo change records
Services
Atlas API
Customer services
No open incidents
Beacon Queue
Event processing
One synthetic alert
Cedar Storage
Data services
No open incidents
Drift Scheduler
Background jobs
Recent change recorded
This concept would connect approved sources selected by each organization. These sample records are static and fictional.
01 / Overview
Core AI is an independent, company-agnostic product concept for organizations that operate complex services. It brings operational context, incident investigation, anomaly detection, team handover, and controlled automation into one adaptable workspace. This case study describes a product direction, not a specific company deployment or a shipped product.
02 / Challenge
Teams supporting complex services often move between monitoring, incident records, runbooks, change reviews, and handovers. Core AI explores how a shared operational workspace could reduce that fragmentation while keeping people in control.
- /Operational context is spread across multiple workflows.
- /Investigations and handovers require teams to bring relevant information together.
- /Automation needs clear safety checks and human oversight.
03 / What I built
- 01Configurable operations overview for services, releases, scheduled jobs, and team planning
- 02AI handover assistant for questions, summaries, and urgency-based prioritisation
- 03Automation workspace with script risk analysis and reviewable, environment-aware execution
- 04Change pre-checks that explain risks and rank suggested actions
- 05Meeting assistant for summaries, decisions, and follow-up actions
- 06Anomaly detection adapted to an organization's operational signals
04 / Product direction
A reusable operations workspace for any organization
For this portfolio version, I’m shaping a generalized product that can adapt to different service environments. The concept focuses on product scope, key workflows, and safeguards needed before implementation: map a team’s operational sources, connect relevant context, and keep people involved in consequential decisions.
Configurable workflows
Let each organization define the services, signals, and routines that matter to its teams.
Flexible AI assistance
Support local or hosted models, with organization-controlled data and provider choices.
Human oversight
Make recommendations reviewable and keep execution behind explicit safety controls.
05 / How the pieces fit
One workspace, several operational loops
Operational context
- Services and releases
- Backups and jobs
- Team planning
Core AI workspace
- Operations overview
- Incident and anomaly investigation
- Handover and meeting insights
- Automation and pre-checks
Team action
- Prioritise
- Review suggestions
- Run controlled tasks
Conceptual workflow only. Specific integrations, data flows, and system boundaries will be defined during product design.
06 / Design principles
Useful context first
Connect the operational picture to the decisions teams need to make.
AI as an assistant
Offer investigation, prioritisation, and summaries while keeping people in the workflow.
Guard the action
Surface risk and require review before a consequential action can run.
07 / Current status
Product definition in progress
The next step is to turn this direction into a standalone, demonstrable product: define a safe sample integration, create a prototype with synthetic data, and validate the workflows before making deployment or impact claims.
08 / Proposed roadmap
From concept to a safe pilot
01
Define the workflow
Confirm the primary user, incident journey, data boundaries, and first useful operational signals.
02
Prototype with synthetic data
Demonstrate overview, incident investigation, and handover; keep suggestions read-only and evidence-linked.
03
Validate security and integrations
Choose an integration pattern and define permissions, retention, audit, and failure behavior before real data.
04
Evaluate a limited pilot
Measure usefulness with an agreed team, then consider narrowly scoped, reversible actions only after review.
Planning only. These are proposed steps; no pilot, integration, or deployment is claimed.