Turning AI-Generated Narrative Discovery into an Analyst-Ready Workflow
Designed an AI-powered Narrative Feed that helped analysts move from large-scale conversation clustering to editable narratives inside Constellation, turning AI output into a usable starting point instead of a disconnected recommendation layer.
Blackbird’s Constellation platform helps intelligence teams discover, investigate, and report on narratives across large datasets.
The opportunity behind the Narrative Feed was clear: use AI to cluster conversations, generate headlines, and surface possible narratives faster than analysts could do manually.
But the workflow had a trust gap.
Analysts still had to:
- jump back and forth between Narrative Builder and Analyze;
- open multiple tabs to compare and refine narratives;
- keep external notes or Google Docs during discovery;
- manually validate whether AI-generated narratives were worth pursuing.
The issue was not lack of AI output. It was the gap between AI suggestion and analyst adoption.
The challenge was not to design “another AI feed.”
It was to make AI-generated narratives:
- easy to scan;
- easy to validate;
- easy to move into the existing workflow;
- and clearly distinct from analyst-authored narratives.
I led the design work across:
- problem framing;
- research synthesis;
- UX strategy;
- feed, detail, and builder integration flows;
- source and state design for AI-generated narratives.
Analysts did not want AI to replace their judgment. They wanted AI to reduce the search space.
The most useful role for the feed was not to deliver a final narrative, but to surface promising hypotheses that could be quickly reviewed and then brought into Builder and Analyze for refinement.
This shifted the design direction:
AI should act as a discovery accelerator, not as an analytical authority.
End-to-end Interactive Prototype
1) Design the feed for scanning, not deep reading
Research showed that headlines were the most valuable part of the feed during early discovery. Analysts wanted to quickly identify potentially relevant conversations before committing to deeper analysis.
So the feed prioritized:
- strong narrative headlines;
- post volume and platform distribution;
- compact summaries;
- quick access to detail and next-step actions.
This reduced the cognitive cost of triage and made the feed feel more operational.
2) Reframe AI output as a starting point, not a final answer
One recurring issue was low trust in summaries, clustering quality, and risk scoring. Instead of over-emphasizing AI confidence, the experience was designed around progressive trust:
- AI suggests a narrative.
- The analyst reviews it.
- The analyst opens details.
- The analyst moves it into Builder.
- The analyst edits, saves, or discards it.
This made the AI useful without overstating its certainty.
3) Connect the feed directly to the core workflow
The feed only became valuable when it stopped behaving like a standalone surface.
The prototype connected the flow across:
- empty state generation;
- populated feed;
- narrative detail;
- Narrative Builder;
- draft and save states;
- version history.
This allowed an analyst to go from “this looks relevant” to “this is now an editable narrative” with much less friction.
4) Make source visible
One of the most important AI UX decisions was showing where a narrative came from.
The system introduced visible cues for:
- AI-generated narratives inside the feed;
- AI-origin narratives inside the selector;
- an “Original AI-Powered Narrative” reference in Builder;
- history states showing when an AI narrative was added and later saved.
This helped analysts understand whether they were looking at:
- an AI-generated candidate;
- a draft derived from that candidate;
- or a saved narrative they now owned.
That distinction was critical for trust.
5) Support editing, saving, and versioning without breaking confidence
The handoff into Builder was not just navigation. It was a state transition.
Once inside Builder, the analyst could:
- expand or refine the query;
- apply filters;
- generate subsets;
- rename the narrative;
- save changes;
- review version history.
This gave the user control over the final analytical object while preserving the value of the AI-generated starting point.
Qualitative
Reduced friction between AI-assisted discovery and manual narrative creation
- Created a clearer bridge between Feed, Detail, Builder, and Analyze
- Made AI-generated narratives feel inspectable and editable rather than opaque
- Improved the product’s ability to support real analyst workflows instead of only showcasing AI output
Product impact
- Positioned the Narrative Feed as a discovery layer connected to Constellation, not as a siloed feature
- Clarified the role of AI in the workflow: suggestion first, validation second
- Established a stronger model for source, narrative ownership, and save-state logic
This project reinforced that AI UX is not mainly about showing intelligence. It is about placing AI output inside a workflow users already trust.
For analysts, the best use of AI was not automatic conclusion-making. It was faster discovery, clearer starting points, and a smoother path into human refinement.
The real design challenge was not generating narratives. It was making them usable.