SOLVE, the market data platform provider for fixed-income securities, has expanded its Quotes platform with the launch of Augmented Quotes for municipal bonds and USD-denominated fixed-rate corporate bonds, adding consistently calculated price, yield and spread datapoints to help users compare dealer quotes more effectively. The company said the enhancement will be available not only within the SOLVE Quotes platform, but also through direct data feeds, FIX connectivity and a forthcoming API.
An interesting aspect of this product announcement is the market-data problem it is trying to solve. In fixed income, pre-trade quotes are often difficult to compare directly because contributors express market views in different ways, with quotes expressed as prices, spreads or yields, while related fields may be absent or derived using different conventions. That fragmentation limits the usefulness of quote data in downstream analytics and makes it harder for traders and portfolio managers to assess relative value or identify the most competitive bid or offer across dealers.Making Bond Quotes More Comparable
SOLVE’s argument is that pre-trade quote data becomes more useful when it is translated into a common analytical frame without losing sight of how the original quote was expressed. That is the purpose of Augmented Quotes: not to replace dealer-supplied data, but to make it easier to compare and use.
“Augmentation is designed to fill gaps and ensure consistency, not to reinterpret dealer intent,” Eugene Grinberg, Co-Founder and CEO at SOLVE, tells Market & Alt Data Insight. “When some quotes arrive as a price, others as a spread or yield, direct comparison becomes difficult. SOLVE normalises these to a common basis so clients can conduct apples-to-apples analysis. Critically, clients can always see which elements were sourced directly from the dealer and which were augmented – so the provenance of every data point is fully transparent.”
That emphasis on comparability matters because it shifts the value proposition away from quote aggregation alone. In a market where the underlying data is unevenly expressed, the commercial and workflow value increasingly lies in whether the data can support faster, more consistent decisions rather than simply whether it can be collected and displayed.The Provenance Question
That, however, raises an obvious challenge. Any vendor-led enrichment of market data invites scrutiny over where standardisation ends and interpretation begins. For buy-side and sell-side users alike, enrichment is only useful if it improves comparability without obscuring the original signal. Grinberg’s response is to frame augmentation as additive rather than substitutive.
“The goal is to retain everything that was originally conveyed – the way a dealer expressed their intention is preserved – while adding consistency that improves downstream analytics and workflow tools. What’s gained is better comparability for use cases like predictive pricing and best-bid analysis. Nothing is lost in terms of how the underlying quote was expressed – augmentation is additive.”
That distinction is important in fixed income, where nuance around liquidity, risk appetite and trading intent can be embedded in how a quote is expressed. SOLVE is effectively arguing that normalisation can improve analytical usability without flattening those differences, provided the provenance of the original quote remains visible.
Beyond Aggregation
The broader angle is that this launch reflects a shift in how vendors are positioning pre-trade data. The competitive advantage lies in making contributed quotes usable as a machine-readable analytical layer across trading and analytics workflows. That is how Grinberg characterises the company’s longer-term direction.
“Fixed income markets are definitionally opaque, and SOLVE is focused on improving transparency to accelerate and sharpen decision-making,” he says. “Part of that is democratising access to high-quality quote data. The larger strategic ambition is SOLVE Px – our predictive pricing engine, which applies advanced AI methodologies to model where bonds will trade across both liquid and illiquid cohorts. Enhanced quote data is valuable in its own right, and it’s also a critical input that drives the accuracy of the AI. So yes – we see pre-trade quote data as an intelligence layer, not just a raw feed.”
That is the clearest indication that SOLVE sees Augmented Quotes as more than a standalone feature. The company is positioning enriched quote data as both a product in its own right and as an input into a wider analytical framework, particularly around predictive pricing. In that sense, the launch sits at the intersection of contributed market colour, vendor-derived analytics and AI-driven modelling.
Building an Integrated Decision-Support Layer
The link to SOLVE Px is especially significant because it suggests the company is trying to bring together several parts of the pre-trade process that have often remained separate: quote collection, data normalisation, predictive analytics and user workflow.
“That’s the right way to frame it,” says Grinberg. “We are building a holistic decision-support layer that helps clients make faster, more confident pre-trade decisions. That’s only achievable by orchestrating critical data, analytics, and workflows in a single, integrated experience. Augmented Quotes and SOLVE Px are complementary components of that layer – not standalone products.”
That is where the announcement becomes strategically more interesting. SOLVE is making the case that pre-trade bond data should be delivered as part of a broader decision-support environment, in which transparent quote augmentation and model-driven pricing work together.
Whether that approach gains wider traction will depend on more than the launch itself. The key test will be whether clients view this kind of enriched data as sufficiently transparent, interoperable and operationally useful to support real trading and investment workflows. If they do, the announcement may point to a wider shift in fixed-income market data, away from raw feed provision and towards integrated pre-trade intelligence.
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