Delivering trading analytics capability
Sustainable change: redesigned processes, deployed tools, trained teams, and measurable results.
Three tools, one system
Each tool can be used independently, enabling rapid uptake — or seamlessly integrated into one system that guides analysts toward robust recommendations, with or without AI.
Model Development & Quality Assurance (MDQ)
A research platform for building and grading models — from guided, guardrailed builds through to advanced, flexible development for trained analysts.
Basic
Simplifies and expedites model development and makes the quality of the resulting trade signal transparent.
- Guided steps — institutionalize the basic work process of building a new model
- Funneled modeling options — prevent users from embedding functions likely to "cause trouble"
- Detailed diagnostics — regression statistics, residual analysis, and history vs. forecast enable comparison of alternative models
- Cumulative error tracking — makes trade-signal quality transparent to analysts, traders, and management
- Gen AI as a complement — rapid customization, speed/scale of model building, and automated quality checks, within guardrails that keep models transparent and aligned with best practice
Advanced
Offers flexibility and more opportunity for trade-signal identification for trained analysts.
- Minimal coding required — enables analysts to quickly enhance models and test more advanced model types
- Flexible yet consistent — system structure embeds a consistent approach while enabling user flexibility
- Diagnostics-driven discipline — MDQ diagnostics and cumulative error tracking prevent complexity being confused with quality
Clio Analytics Tool (CAT)
A live analytics platform that forecasts real time supply and demand balance items across multiple geographies and their aggregations.
- One live tool, AI-ready — replaces spreadsheets, enables a common framework for efficiently highlighting fundamental market drivers
- Built to scale — Databricks-ready, no rework to business logic
- Real time model performance monitoring — all deployed models are continuously evaluated using industry-standard statistical diagnostic tests, allowing analysts to stay ahead of under-performing areas of their balances
- Simple explanations — disaggregates changes in forecasts into their respective fundamental drivers, providing critical insights into what is causing those changes
- Transparent adjustments — analyst overrides are easy to apply and monitor, helping expose systemic bias and improve forecast credibility
- Scenario analysis — easily undertaken, with the impact of changes clearly surfaced
- Continuous forecast performance tracking — forecast v. actual tracking available to analysts, traders, and management
Forecast Analytics & Version Retention (FAVR)
An analytics focused forecast and curve platform for energy and commodity desks that historizes every forecast, enables collaboration, and provides the basis for training future models.
- Tracking — records the full lifecycle of each data point, not just its current value. FAVR pulls the curve as of any cutoff, and provides reviews and explanations on what was knowable then
- Context rich — records not only the numeric history, but the version of the model used to create the number and the text that describes the thinking
- Dynamic forecast v. actual analysis — easily inter-relate actuals to all versions of the forecasts, enabling analysis of the quality of a trade signal over time
- Searchable metadata — tag and discover any series across the whole universe, by horizon, source, or model
- Row-level permissions — creators set read/write/owner rights; keep series private or share deliberately
- Transparency on update history — see what's been updated, when each update occurred, and by whom
- Works where you already are — Databricks, an Excel add-in, Power BI, Python & R, and a REST API, with no exports or stale copies
Apprenticeship: build your own subject-matter experts
Analysts capable of applying judgment to AI recommendations — developed through a staged apprenticeship, not a one-off training session.
Principles & Plan
Clio trains on Advanced MDQ principles; the team aligns on data needs.
Guided Delivery
Clio gives 1:1 direction to build and deliver balances.
Independent Build
The apprentice builds a second set solo; Clio gives feedback.
Teach a Trainee
The apprentice trains a trainee to deliver, with Clio mentoring.
Graduate & Lead
The apprentice graduates and leads new apprentices.
Alongside the apprenticeship track, group training brings the whole analytics organization up to speed on FAVR, MDQ, and CAT — engaging the full team in practical, real work from day one.
Great models have no impact if they're not used for trading
Embedding work processes and managing execution is how model quality turns into P&L. Work processes will be defined, documented and monitored via KPI dashboards.
Model Quality Dashboard
Model quality is not a static point — as markets change, models can drift. Calculated from cumulative model forecasting error, the dashboard keeps quality visible to analysts, traders, and management.
Process Compliance Dashboard
Analytics requires trader engagement to have any impact on P&L. Analysts self-report key observations from trader interactions, giving management the opportunity to verify and intervene where needed.
Every layer must be in place for AI to deliver reliable results
The building blocks of trusted trading AI, from the data foundation up.
Performance Metrics
Objective KPIs tell leaders whether the process is working and give the opportunity to intervene.
Expert Analysis (Apprenticeship)
Trusted subject-matter experts evaluate, challenge, and validate the AI's suggestions.
AI Models & Data Science
Direct access to the AI itself, plus trusted data scientists who build and maintain the tools.
Best Practices & Defined Work Process (CAT, MDQ)
Guardrails that keep analysts and AI aligned to proven methods and a robust, repeatable workflow.
Trusted Data Foundation (FAVR)
Gathers, identifies, and surfaces the key data that powers model training and analytics.
A phased path to sustainable, in-house capability
Designed for sustainable change, with staged contracts that create clear points to check for organizational alignment. Each phase has its own contract and go/no-go decision.
Case for Change & Long-Term Vision
Benchmark your organization against industry best practice, assess at the bench level, and gather feedback. Align on what "good" looks like across people, process, tools, and performance management, and prioritize by P&L potential. Establish an executable project plan, detailed deliverables, and a resourcing plan and timeline.
Rollout & Training
Initiate plan execution across people, process, tools, and planning & performance, with formal program management stood up.
Build Out & Enhance AI Agents
Train AI on the history developed in rollout — supply/demand forecasts, analyst adjustments, and more — and advance trading algorithms.
Sustain
Maintain KPIs and management reviews, with ongoing maintenance support from Clio.