Opensee

A genuine and growing AI line sitting on an engine that does not depend on it. The company brands itself AI first and has a real agentic product, but its own description of what it does names the ingredients in the revealing order: it combines high performance computing with artificial intelligence, and its market position was earned as a category leader in risk data aggregation, which is a feat of architecture rather than inference.

The value proposition is real time access to every data point at full granularity across very large volumes and long histories, and that is delivered by a horizontally scalable store and deterministic calculation, not by a model.

Strip Agensee, the generative assistant and the data quality assistants and the platform still ingests, aggregates, recomputes value at risk and serves the same users through its interface and native Python integration, which is what it did successfully for years before the agentic layer existed. The Oxane test confirms the reading: remove the models and this software still has everything to operate on, because the data arrives through ingestion pipelines rather than through extraction.

Oversight properties are asserted with more specificity than most, and the enforcement mechanism is still not described. On the credit side the company states full regulatory audit trails, and the semantic layer is presented as producing information that is auditable and verifiable, with automated explainability offered alongside the natural language interface.

Data versioning is a documented capability, which matters because it lets a user reconstruct what the platform said at a past point in time, an unusually concrete form of accountability for an analytics product. What is missing is the control path around the agentic layer: Agensee is described as automatically building data models, calculators and dashboards, and nothing states who approves a model it generates before risk numbers are produced from it, what happens when it is wrong, or whether a human signs off before an automatically built calculator feeds a regulatory report.

The strongest claim on this axis is also the least checkable one. The semantic layer is described as producing information that is auditable, verifiable and provably correct, and provably correct is an absolute with no proof method attached, which places it alongside the unfalsifiable absolutes already catalogued in this index. Automated explainability is offered without a description of what is explained or how.

For the agentic capability that automatically builds data models and calculators, there is no accuracy measurement, no error rate, no human review rate, no versioning policy for the models it generates and no drift monitoring, which matters because an automatically constructed calculator feeding a regulatory risk number is exactly the artefact a supervisor would ask to validate.

A named tier one bank on the record plus an unusually dense set of independent evaluations. Credit Agricole CIB is quoted describing the platform as a change in the speed, efficiency and governance of risk management for its market activities, and further quoted material credits access to granular and historical information and taking trade analytics further. Taula Capital, a large hedge fund launch, is named as a deployment.

The independent recognition is specific and dated rather than generic: category leader in the Chartis RiskTech Quadrant for market risk risk data aggregation, a twenty place climb in the Chartis RiskTech100 with a special award for risk data aggregation and reporting, inclusion in the Chartis RiskTechAI50 for use of AI in risk data, Risk Data Repository and Data Management Product of the Year at the Risk Markets Technology Awards 2026, and two further 2025 awards.

Held at B because no figure is attached to any named institution: the bank's endorsement is qualitative, and the millisecond value at risk recomputation claim is self reported and demonstrated in the company's own webinar.

Nothing states whether customer data trains the vendor's models. One architectural property runs in the customer's favour and is recorded without being credited as a commitment: the platform is described as delivering analytics without moving the customer's data, and deployment inside the customer's own cloud account means the dataset does not leave infrastructure the institution controls.

That constrains where data sits but is not the same as a statement that it is excluded from any training corpus, and no such statement exists. Given that the platform holds granular positions and exposures for competing institutions, the separation question is a live one for a buyer and goes unanswered.

No privacy posture is published. The data involved is overwhelmingly institutional rather than personal, which lowers the exposure relative to a consumer facing vendor, but nothing addresses retention, access controls over historical positions, or the handling of any personal data reaching the platform through counterparty, client money or retail credit portfolio records, and the credit risk product explicitly covers retail portfolios.

No security certification is claimed in the material reviewed. No ISO 27001, no SOC report of either type, no penetration testing statement and no trust portal. The absence is notable for a supplier whose platform holds full granularity trade, position and exposure data for an investment bank's market activities, which is among the most sensitive datasets a vendor in this index handles.

The deployment architecture mitigates part of the concern, since running inside the customer's own cloud account means the institution's own controls apply to the environment, and that is recorded here rather than credited as a certification.

A software supplier with no licence, authorisation or supervisory relationship of its own. The regulatory language throughout is about what the platform helps a bank achieve, namely auditability, regulatory reporting and compliance with capital and liquidity standards, which is a product claim rather than a statement of standing. No sandbox participation, registration or enrolment in any supervised programme was found.

No fairness or AI governance position of any kind, and no reference to any named framework or to the EU AI Act despite a French base and a European bank buyer base. The direct fairness exposure is lower than for a consumer lending vendor because the outputs are portfolio and market risk metrics rather than decisions about individuals, and that context is recorded so the grade is not read as equivalent to a C on a credit scoring product.

It is not zero: the credit risk product surfaces probability of default, loss given default and exposure at default across retail and small business portfolios, and how those aggregates are constructed and presented shapes where a risk team directs attention.

No recourse position is published. Nothing states what happens when an automatically generated calculator is wrong, when a data quality assistant fails to flag a bad feed, or when the generative assistant answers a risk question incorrectly, and nothing addresses who carries the consequence if an erroneous figure reaches a regulatory submission.

The standing methodological caveat applies and should accompany any published comparison: this is a purely institutional vendor, so every affected party is a sophisticated firm operating under a negotiated contract, and recourse collapses into commercial terms. The C therefore means something narrower here than the identical grade on a product that touches individuals.

No model, family, version or provider is named for any of the AI capabilities. Agensee, the generative data assistant, the automated explainability and the data quality assistants are each described by what they do and none by what they run on, and there is no statement of whether the models are built in house or licensed, no version policy and no per capability breakdown.

Amazon Web Services is named repeatedly, but as deployment and procurement infrastructure rather than as a model provider, and the distinction matters: naming the cloud is not naming the model. The standing sourcing tell is worth applying here on a later pass, since a model provider that has this vendor as a customer may have published the relationship even though the vendor has not.

Designed as an overlay rather than a replacement, which the company states plainly: a cross risk analytics layer that sits on top of existing infrastructure, aligning identifiers and reference data and preserving history across systems that were never built to be queried together. That positioning requires ingesting from market risk, credit risk, liquidity, collateral and trade systems simultaneously, which is real integration breadth.

Native Python integration lets a customer build capital and other models against the same data the end users see, and deployment is available through a customer's existing cloud account. Held at B because no specific risk engine, core banking platform or market data provider is named as a certified integration, so the depth is described in the abstract rather than enumerated.

Better disclosed than almost anything else on this roster, and the position is architectural rather than a policy statement. The platform runs on any cloud, on premise or hybrid cluster, and the company states that a customer accesses analytics without moving their data, which is a substantive answer to where processing happens rather than a promise about it.

Procurement and deployment are available through the customer's own cloud account, which keeps the workload inside infrastructure the institution already governs and already has a residency position for. Held at B rather than A because no specific regions are enumerated, no residency commitment is stated in contractual terms, and there is no explicit treatment of the EU requirements that a European bank buyer would be assessed against.

No price is published: no plan tiers, no unit basis, no data volume bands, no seat cost and no indicative contract size. Every route into the product is a demo request. Queued check, and it follows a standing rule this index earned on Loxon: the platform is available for procurement and deployment through a customer's existing Amazon Web Services account, and cloud marketplace listings routinely carry contract pricing and structured commercial terms that a vendor never places on its own site. That listing was not opened, so this grade is held at C with the check recorded rather than treated as a settled absence.

Genuinely broad across function and buyer type, and unproven on scale. The platform addresses market risk, credit risk, liquidity and asset liability management, trade analytics, collateral management, profit and loss and performance, market data storage and climate risk, and it is sold to banks, asset managers and hedge funds, with separately presented buy side solutions. Named institutions span the range: Credit Agricole CIB on the sell side and Taula Capital on the buy side.

Held at B rather than A because no customer count, assets under service figure or geographic footprint is published anywhere, and the company is a Series A business with modest disclosed funding, so the installed base cannot be characterised from available material.