Factiva AI: Smart Content

Designing a trusted, generative-AI research layer for Factiva that turns manual, time-consuming research into instant, verifiable summaries, company briefings, and a research copilot.

For high-stakes professional decisions, generic AI is not enough. Win on trust, verifiability, and editorial authority.

Overview

Factiva is Dow Jones's professional research platform: a vast licensed archive of news, company data, and market intelligence relied on by analysts, bankers, PR professionals, consultants, and C-suite executives. Its depth is its value, and also its tax. Finding, reading, and synthesising the right material across thousands of sources is slow, manual work.

Factiva AI, internally Smart Content, put generative AI to work on that problem. Not as a chatbot bolted onto the side, but woven into search, reading, and briefing so the value shows up exactly where the work happens. I led design and research across the milestone roadmap, from the hackathon that started it to the shipped milestone builds and the forward-looking Research Copilot.

The project

Transform how Factiva's professional users research by replacing manual reading and synthesis with instant, trustworthy AI. The vision: every answer is one tap from its evidence, AI lives inside the existing workflow, and the experience earns the trust that high-stakes decisions demand.

As Senior UX Architect on Design & Research, I owned the design direction end to end across milestones. I framed the problems, mapped the research workflow from query to insight, designed the summary, briefing, report, and chat experiences and their verification model, kept the work aligned to the Factiva, DJ+, and Index design systems, and produced build-ready specifications and handoff.

Smart Content moved from a hackathon idea into a milestone-driven, shipping initiative with sustained leadership backing. The work reframed Factiva search from find the documents to get the answer, with the documents attached, and established the citation and verification patterns now used across the AI roadmap.

The challenge of professional research

Discovery, grounded in customer interviews and persona analysis, surfaced six recurring problems. The early milestones tackle the blank-slate problem of summarisation; the later ones address how research actually unfolds over time. Five professional personas shaped every decision: analysts and consultants who live in the detail, PR professionals who monitor narrative and risk, bankers and C-suite executives who depend on teams for synthesis, and professional researchers who translate vague requests into search strategies.

Smart Summaries: the anatomy of trust

A Smart Summary offers depth on demand: a Summary view, a Bullets view, and a Go deeper with analysis path into a dedicated Deep Analysis view. Glance first, drill down only when needed, so speed never costs the user thoroughness.

Every claim is backed by inline citations and sources. From any citation a user jumps straight to the underlying article, and the Deep Analysis view keeps headline sources in a right rail. Verifiability was treated as a first-class feature, not a disclaimer.

Users can copy a summary, give thumbs up or thumbs down, and control where summaries appear. Summaries also surface inside Search Builder, where users can enable or disable the display, keeping people in control of the AI.

Company Summaries and Reports

Company Summaries introduced lenses: tabbed perspectives such as news, financials, and strategy. A user pivots the same entity through different analytical frames, each summarised and cited. M2 was where Smart Content stopped being a feature and became a re-think of search itself.

M2.3 Company Reports auto-generate a long-form briefing that combines fixed sections, such as Financial Performance, with dynamic sections, such as an AI Strategy section that adapts to the entity. A sticky table of contents makes a long document navigable, and inline bracketed citations plus a full references section keep it verifiable at scale.

Reports load from cache when fresh, under 24 hours, show a historic view when older, or trigger a new generation. A non-blocking stale-warning banner offers a Generate New Report action, so users always know how current the AI content is without being blocked.

Research Copilot

M1 and M2 solved the blank-slate problem; M3 addresses what happens next, the messy, ongoing research process. Branded internally as Research Copilot, it ladders conversational capability up in stages while keeping the same trust model.

M3.0 bounded chat lets users ask questions against a defined, scoped set of content; M3.1 opens this to unbounded questions across the corpus. Parallel explorations, a Chat Bot concept and FactivaGPT, pressure-tested conversational retrieval and a more open assistant direction.

M3.2 deep research runs multi-step research that extracts the specific answer, not just a list of documents, directly attacking the signal-from-the-noise problem.

M3.11 chat history makes AI output durable rather than disappearing after a session, attacking the stateless problem. M4 adds personalization, memory of a user's and team's interests and portfolios, cross-entity analysis, and shared, saveable research, answering the one-at-a-time and siloed-workflow problems.

From concept to production

I worked in a tight cross-functional team with the Director of Product Design, the VP of Product Platforms, the product lead, and engineering. The initiative began as a hackathon with broad leadership sponsorship, including the SVP of Design and a Senior Principal PM, then narrowed to a focused core team for milestone delivery.

Working with product and analytics, we defined explicit engagement and trust signals up front: clicks on Overview, Bullets, and Go deeper, which view users land on, citation and source clicks, thumbs up and down, which lenses users engage, report freshness states and table-of-contents navigation, and Search Builder adoption.

Each pattern was specified for engineering and aligned to the Factiva, DJ+, and Index systems, so the AI experience shipped as part of the product rather than a bolt-on.

Shaping trustworthy AI for professionals

The defining challenge was not making AI summarise, models do that. It was making professionals trust the output enough to build decisions on it. Designing for verifiability, tethering every claim to its evidence and every report to its freshness, is what separated a credible research tool from a plausible-sounding one, and it is the part of the work I am most proud of.

The other lesson was sequencing: solving the blank-slate problem earned the right to solve the harder, longitudinal problems of memory, multi-entity analysis, and collaboration. If I were starting again, I would push verification and currency patterns even earlier in exploration, because they ended up shaping nearly every downstream decision, and the trust model built here now informs AI work across Dow Jones.