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How Tonic3 Drove Operational Clarity for Solovis with a Blended Delivery Team
Delfina Castrogiovanni
:
Jul 14, 2026 6:13:57 PM
Solovis’s sophisticated institutional clients were operating in a "static" environment. While they had access to historical data, they lacked the power to generate dynamic "what-if" scenarios to understand the future state of their investments. This gap created a bottleneck in strategic agility—investors were forced to rely on manual modeling or guesswork rather than real-time data projections.
By pairing deep domain expertise with a specialized, seven-person strategy, UX, and engineering implementation team, Solovis eliminated manual workarounds, reduced delivery drama, and created a differentiated, AI-ready analytics engine that now powers faster, smarter investment decisions.
- 90 Days from abstract concept to a validated, high-fidelity prototype.
- 100% User-Validated requirements through 1-on-1 stakeholder and customer interviews.
- Zero "Delivery Drama" by matching a high-growth startup pace with a flexible, iterative execution plan.
- 7-Member Specialized Team weaving Strategy, UX, and Engineering into a single workstream.
90 Days
100% User-Validated
Zero "Delivery Drama"
7-Member Specialized Team
Solovis set out to give institutional investors something they’d never had before: a fast, intuitive way to model future-state portfolios with confidence instead of guesswork. In just 90 days—and in close partnership with Tonic3—Solovis moved from abstract idea to a high-fidelity, fully user-validated MVP that puts dynamic “what-if” scenario modeling directly in analysts’ hands. By pairing deep domain expertise with a specialized, seven-person strategy, UX, and engineering implementation team, Solovis eliminated manual workarounds, reduced delivery drama, and created a differentiated, AI-ready analytics engine that now powers faster, smarter investment decisions.
The Challenge: Navigating the Fog of Static Investment Data
Solovis’s sophisticated institutional clients were operating in a "static" environment. While they had access to historical data, they lacked the power to generate dynamic "what-if" scenarios to understand the future state of their investments. This gap created a bottleneck in strategic agility—investors were forced to rely on manual modeling or guesswork rather than real-time data projections.
The Approach: The Tonic3 "Signature Blend" in Action
To bring "Sherlock" to life, we replaced traditional "code-and-pray" methods with The Weave—our proprietary blend of UX, Strategy, and Code.
Engineering Adoption by understanding the users:
- We didn't start with features; we started with people. By conducting 1-on-1 interviews with internal stakeholders and Solovis customers, we unearthed the specific pain points of projection modeling. This research fueled our design thinking exercises and sketching phases, ensuring the final interface felt intuitive to high-level analysts.
Driving Velocity & Rigor:
- Matching the client’s fast-paced startup style, our developers and architects worked in lockstep with the design team. We utilized rapid prototyping and iterative testing to ensure that the UX architecture was not just beautiful, but technically feasible and ready for MVP development.
Architecting for Future Intelligence:
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While "Sherlock" is a projection module, we architected the data flows and UX framework to be AI-Ready. By organizing workflows and requirements today, we cleared the path for future automated predictive modeling and agentic insights.
Outcomes +
Why It Matters
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How did the platform directly improve Operational Efficiency (COO)? *It eliminated manual "what-if" workarounds by providing a standalone, dynamic module, allowing analysts to model future states in a fraction of the time.
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How did Tonic3’s blended skillset reduce execution risk?We used rapid prototyping and user testing to validate the product before full-scale engineering, ensuring Solovis didn't waste resources on features users didn't need.
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How does this platform prepare the client for Intelligent Transformation?
- The "Sherlock" module establishes the structured data environment and user journey mapping required to eventually layer on Agentic AI for automated investment insights.
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