ICE reference data, evaluated pricing, analytics, and fixed income indices give Sonαr the institutional market data to test research against true market outcomes.
Credit research is opinion at scale. Thousands of views reach a desk every week as unstructured research, and most are never tested against what the market actually did. Menos AI now integrates ICE's fixed income reference data, evaluated pricing, analytics, and indices into Sonαr. Sonαr can now test the research against the market—and against the record of what happened the last time someone made the same call.
Equity Research Tech Was Never Built for Credit
Most financial AI was modeled on equity workflows: search by ticker, pull the filing, summarize the transcript, look up the price. One issuer, one security, one number. Credit doesn't work that way. One issuer can carry dozens of bonds across a parent and its subsidiaries, each with its own seniority, covenants, and call schedule. A price means nothing until you know which bond it belongs to, where it sits within the structure, what the spread is telling you, and how it compares to its peers and its sector. Equity workflows can be satisfied by retrieval. Credit cannot.
Sonαr was built for macro and credit from the start. ICE is how it goes deeper.
The Data Credit Actually Runs On
Macro and fixed income run on reference, pricing, index, and flow data. Getting that data has never been the hard part. The hard part is that it sits apart from the research and the thesis it informs, so the comparison happens in another system and nothing carries it forward when the market moves.
Intelligence is a team sport. Menos AI integrates with the data providers, technology platforms, and research networks that institutional investors already trust. With ICE, that means reference data, evaluated pricing, spread analytics, and the fixed income indices credit desks already work with, brought directly into the research workflow.
Where It Meets the Research
This is the part that makes it more than a feed. Sonαr already tracks three things that usually live in separate workflows. Trade ideas, in Trade Idea Hub, each with a thesis and captured levels. What the institutional research community believes, in the Narrative Network, as dated and attributed claims. And who has been right, in Voice Scoring. ICE data gives all three a common market reference.
A credit idea logged at a spread target is measured against ICE evaluated pricing, not a screenshot from last week. A narrative building on energy credit spreads across hundreds of sell-side theses sits next to where those spreads actually are. You can see when the Street's belief and the market's price part ways. An analyst's track record on a name is scored against what the bonds did.
We are not adding ICE data to the research. We are holding the research to it.
What It Unlocks on the Desk
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Work the structure, not the ticker. ICE reference data and pricing resolve the debt stack, so an issuer's bonds, seniority, and terms are part of the analysis rather than a separate exercise.
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Track theses against the market. A spread target becomes a live, measurable thesis rather than a static note.
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Compare issuers consistently. Peer and sector relative value evaluated on the same market-data basis, every time.
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Align research, PMs, and trading. Everyone sees the thesis, the supporting research, and the market evidence in one place.
Toward the Virtual Credit Analyst
This is where Sonαr Studio is heading: research capacity as a product. A virtual credit research analyst that works the way a credit team does, issuer by issuer, structure by structure, name against peer. Each skill answers a question on request or runs on a schedule as standing coverage: relative value screens, peer analysis, thesis monitors, event monitors, refreshed as the corpus and market move.
A virtual analyst is only as good as the evidence it can reason from. Narratives and trade ideas are the research memory it reasons from. Structured market data is the evidence it reasons against. Capital structure analysis needs the debt stack resolved. Relative value needs pricing and spreads. Peer and sector analysis need a consistent basis for comparison. This integration is what lets those skills reason from the same institutional market data used across the desk, so coverage expands with the corpus, not the headcount.
We are grateful to ICE for the collaboration and to the desks whose workflows shaped it.
See what this unlocks in practice. Connect with us: https://www.menosai.com/contact
