Arabesque AI (AutoCIO): Initiation of Coverage

GAII Intelligence defines the Quant, Portfolio & Analytics market as software and model services that apply machine learning to the quantitative core of investment management: generating return and risk forecasts, constructing and rebalancing portfolios, measuring and attributing performance, and estimating and minimising the cost of trading. Buyers are portfolio managers, quantitative researchers, heads of trading and the chief investment officers and technology teams who sign off on systematic processes.

Capabilities we treat as mandatory

• Point-in-time and backtest integrity: model inputs and simulations free of look-ahead and survivorship bias, with a documented method a quant team can reproduce.
• Out-of-sample and live evidence: performance or cost-reduction claims supported by live or held-out results, not only in-sample backtests.
• Institutional data handling: a stated position on where client holdings and order data go, how long they are held, and whether they are used for model training.

Capabilities we treat as differentiating

• Explainability of signals: forecasts, weights and cost estimates traceable to their drivers in a form a portfolio manager or risk committee can challenge.
• Independent, verifiable track record: live results published through regulated funds, third-party ratings or client-run A/B tests over several years.
• OMS/EMS and data-platform integration: native delivery into order and execution management systems, FIX, and the firm's data platform and Python stack.
• AI-assistant access: models and analytics reachable from the assistants portfolio managers and traders already use, through APIs or MCP, without loss of rigour.

Arabesque competes with AI portfolio and signal providers such as Qraft, with portfolio analytics and optimisation suites from established risk vendors, and with in-house quant platforms at larger managers.

The following are the capabilities and characteristics that, in our assessment, most distinguish Arabesque AI (AutoCIO) within the Quant, Portfolio & Analytics market. Each is drawn from publicly documented product capability, regulatory filings, published interviews with management, or independent review.

• Portfolio-native design: Construction, optimisation, rebalancing and attribution in one tool for portfolio managers.
• Explainability: Daily per-stock view of the weight given to each model input, plus factor attribution.
• Holdings ingestion: Portfolios uploaded from CSV, Excel or PDF, with custom universes and client ESG data.
• Published prices: Three tiers from $25,000 to $450,000 a year.
• Assistant access: API, Python SDK and MCP, with a Gemini Enterprise listing (April 2026).
• Regulated heritage: Group asset management arms authorised by the FCA and licensed by MAS.

The following are factors that prospective clients should weigh, and areas where our public review did not find sufficient evidence to reach a settled view. Several are common across vendors at this stage of the market and are not specific to Arabesque. We encourage prospective clients to raise each of them directly with the company; we have done the same, and will update this note as answers arrive.

• Corporate scale: Arabesque AI Ltd filed micro-entity accounts for 2025; headcount last disclosed as 30+ in 2024.
• Capital: No funding disclosed since 2020; DWS's current holding not confirmed.
• Evidence: Live results come from group funds and company examples, with mixed AI-enhanced results.
• Assurance: No SOC 2, ISO 27001 or no-training commitment located.
• Methodology: Point-in-time and backtest handling not documented.
• Integration: No OMS/EMS integrations named.
• Leadership: No chief executive of Arabesque AI named since 2023.

Arabesque AI Ltd was incorporated in London in August 2019 to house the AI engine built inside the Arabesque group, founded in 2013 by Omar Selim through a management buyout from Barclays. Georg Kell chairs the group; the AutoCIO board includes Martin Reeves and Joachim Wuermeling. Group offices are in London, Frankfurt and Singapore.

DWS took a 24.9% stake in January 2020. Since June 2023 Arabesque Investment AI Ltd, majority-owned by The Arabesque Partnership LLP, has held 50–75% of Arabesque AI Ltd. The company filed micro-entity accounts for 2025. Revenue is not disclosed.

Company-reported figures are shown as such and are not independently verified. Verification is carried into the vendor questionnaire.

AutoCIO is a single platform with four working surfaces and developer access. Figure 2 sets out our assessment of how it maps onto the quant and portfolio workflow.

Build and optimise

Construct portfolios from scratch or upload existing ones for AI-enhanced rebalancing under constraints.

AI signal

Daily forward-looking forecasts on 50,000+ equities with per-stock explainability.

Fund search and analytics

Search 64,000+ funds and ETFs; risk metrics and attribution.

Co-pilot and developer access

Natural-language agents, API, Python SDK and MCP for Claude, Gemini and ChatGPT.

Scope of the product today

We did not locate fixed income, derivatives, execution or OMS/EMS integration; optimisation covers single stocks and ETFs.

The AutoCIO signal is a proprietary machine-learning model that forecasts equity returns daily from 87+ features per stock and is retrained as new data arrive. A generative co-pilot with specialised agents sits on top, running on Google Cloud, with access from Gemini, Claude and ChatGPT.

Group funds provide a live record; no independent evaluation is published.

AutoCIO draws on market and public financial data across 50,000+ equities, a fund database of 64,000+ funds and ETFs, and the group's ESG data, and accepts client portfolios and ESG metrics.

Point-in-time handling, survivorship treatment and execution-cost data are not documented.

AutoCIO fits portfolio managers and smaller managers or wealth firms that want AI-assisted construction, rebalancing and reporting without building a quant stack.

Trading and order management sit outside it, and no OMS/EMS integration is named.

AutoCIO runs on Google Cloud, with a single-tenant private-cloud or on-premises tier. Figure 3 sets out what our public review located.

We would encourage buyers to request security attestations, a no-training clause, current financial statements and confirmation of the contracting entity and its ownership.

The offsetting factor is the regulated group: FCA-authorised and MAS-licensed affiliates that run funds on the same engine.

Prices are published: Individual $25,000, Enterprise $120,000 and Single Tenancy $450,000 a year, with query and backtest limits.

Buyers should weigh the entry price against the evidence and the vendor's scale.

Arabesque sits in the Emerging quadrant of the segment matrix, between BestEx Research and Qraft overall, with the strongest portfolio-construction profile of the three.

GAII Quant, Portfolio & Analytics coverage, 21 October 2026, scored on the segment variant of Data & Content and Workflow Fit; not directly comparable with Research & Intelligence or Risk, Compliance & Surveillance scores.

11.1 Where Arabesque is strongest relative to peers

• Against signal vendors, on a complete portfolio workflow.
• Against risk-suite vendors, on price transparency and entry cost.
• Against chat-first AI tools, on constraint-aware construction and explainability.

11.2 Areas of narrower functional focus

Arabesque has built product depth rather than corporate scale or enterprise documentation.

• Scale: micro-entity accounts.
• Assurance: not located.
• Evidence: group-run.
• Integration: no OMS/EMS.

Scores are on a 0–10 scale. Each dimension score is the mean of its sub-criteria, and the GAII Overall Score is the weighted mean of the six dimensions. The detailed sub-criteria scores that build each dimension are set out in Appendix A.

These are the areas where our public review did not find sufficient evidence to form a settled view. We have put them to the company and will update this note when they are answered. Prospective clients may find them a useful agenda for their own evaluation call.

• Contracting entity, ownership and DWS's current stake.
• Headcount and the reason for micro-entity accounts.
• Who leads Arabesque AI.
• SOC 2 or ISO 27001.
• No-training terms for the co-pilot and model providers.
• Point-in-time and backtest methodology.
• Live results of client portfolios, not group funds.
• Auditor behind the "audited" track record.
• OMS/EMS integration plans.
• Nature of the HSBC, DWS and eToro relationships.
• Trial terms.
• Asset-class roadmap.

This note is an assessment of a vendor against a defined framework, not a recommendation to buy or to decline. The following are the considerations we would put at the centre of an evaluation of Arabesque specifically.

If you run portfolios, quant research or trading

• Trial construction on your own holdings and constraints.
• Compare the AI signal with your current process out of sample.
• Plan execution separately.

If you are responsible for technology or vendor risk

• Open with Figure 3; request attestations.
• Confirm the contracting entity and its financial position.
• Review MCP and co-pilot data flows.

If you are assessing the category rather than the company

• AI is reaching portfolio construction, not only research.
• Published prices remain rare in the segment.
• Smaller vendors face viability questions.

Each dimension score in Section 12 is the mean of the sub-criteria below. Data & Content and Investment Workflow Fit use the Quant, Portfolio & Analytics segment variant.