Tallarium

A refined UX and design system supporting Tallarium’s real-time pricing data, making complex market signals easier to understand and act on.

 

Client

Tallarium

Sector

Energy Trading
Financial Data Intelligence
B2B / SaaS

Discipline

UX Strategy
Product Design

Information Architecture
Workflow Optimisation
Data Visualisation

Microcopy

 

Context

Tallarium is an OTC energy-trading intelligence platform that converts fragmented chat and call data into real-time, actionable pricing insights. Traders rely on accurate, timely signals to negotiate deals and reduce risk exposure.

The founders needed a clearer, faster, user-validated way for traders to interact with pricing data, understand market signals, and act with confidence. Tallarium uses natural-language processing to extract pricing signals from trader chat and call data. The platform transforms messy, unstructured communication into structured, real-time insights traders can trust.

Challenge

Energy trading desks operate under pressure: incomplete data, inconsistent processes, and tools that often hinder more than they help. Tallarium’s early version surfaced pricing data, but workflows, information hierarchy, and usability weren’t yet optimised for fast, high-stakes decision-making.

The challenge was to reshape the product so traders could:

  • parse real-time data instantly

  • identify signals and anomalies quickly

  • move between dialogue, deal terms, and pricing with minimal friction

  • trust the interface during real trades

My Role

My role focused on shaping the product’s UX foundations and design system.

  • Led UX strategy for the intelligence and workflow layers

  • Directed feature definition, information architecture, and interaction patterns

  • Built high-fidelity prototypes to validate workflows with real trading desks

  • Worked closely with data scientists and founders to align product logic with user mental models

  • Introduced a scalable design language to standardise components and accelerate iteration

  • Interpreted machine-extracted pricing signals into UX patterns that improved trust, transparency, and situational awareness

Key Moves

Key actions that defined how the product evolved into a clearer, more dependable trading tool.

  1. Signal-First Information Architecture
    Reorganised the interface around algorithmically-derived signals — market shifts, anomalies, pricing confidence — giving traders clearer context and reducing cognitive load.

  2. Workflow Redesign for Speed & Confidence
    Simplified multi-step tasks using clearer hierarchies, tighter grouping, and progressive disclosure, enabling faster reading and decision-making.

  3. Interactive Prototypes for Real Desk Testing
    Built realistic Figma flows replicating live trading conditions; validated with traders to refine patterns, interactions, and data presentation.

  4. Scalable Component & Layout System
    Created a consistent component structure — tables, graphs, signal cards, deal panels — ensuring both clarity and future extensibility.

  5. Cross-Functional Product Alignment
    Worked closely with data-science teams to make model outputs understandable, actionable, and trustworthy in high-pressure trading environments.

Impact

The redesign strengthened decision-making, reduced friction, and created a solid base for future scale.

  • Improved comprehension of machine-derived signals

  • Reduced confusion around model confidence and market anomalies

  • Increased trust in automated insights during real trades

  • Provided a clearer mental model for how the intelligence layer behaves

Reflection

Designing for high-pressure environments demands absolute clarity — every pixel must earn its place.