Alpha Quant Agent is the governed quant analytics platform built by Nexqion Analytics.
Alpha Quant Agent runs 150+ deterministic quant methods on your data, with approval gates and a full audit trail — built for the teams where Aladdin is out of reach and ChatGPT is banned by compliance.
Institutional validation, risk oversight, and decision-ready reporting delivered through deterministic analytics and controlled execution.
Open solutionBuild client-specific models, diagnostics, and validation logic, then integrate them into the workflow.
Open solutionConnect data ingestion, document flows, approvals, and distribution around the core workflow.
Open solutionDesign the landing zone, identity, storage, and governance needed to run workflows securely in production.
Open solutionAladdin requires $100B+ AUM and multi-year enterprise contracts. General LLMs are prohibited at JPMorgan, Goldman, Deutsche Bank, Citi, and most regulated asset managers for liability and compliance reasons. Alpha Quant Agent is built for the $200M–$2B mid-market segment — institutional-grade governance, without the institutional price tag.
Your analysts spend three days rebuilding the attribution report that the PM changes in the room anyway. Alpha Quant Agent runs the full analysis in minutes — factor attribution, anomaly flags, compliance narrative, and a governed PDF — so your team is adding judgment, not building spreadsheets.
CCO/CRO and PM/Quant teams work from the same platform — same governance layer, same audit trail, same Azure tenant.
Every run produces an immutable record: inputs, methods, outputs, approvals — structured for regulatory examination by SEC, FCA, and ESMA.
150+ validated deterministic methods. No probabilistic output, no hallucination risk. Every number is traceable to a model, a dataset, and a timestamp. Your risk team can validate every figure.
Your analysts run factor attribution, anomaly detection, and a compliance narrative in minutes — not days. The report the PM needs for the committee meeting is ready before the meeting.
Azure-native deployment. No multi-tenant model training. No fund data touches a shared inference endpoint. Enterprise deployments run in your own Azure tenant — full infrastructure ownership on the cloud you already trust.
Walk-forward validated backtests, Fama-French attribution, and portfolio optimisation — governed runs with immutable records, the same audit trail as your risk committee pack.
We start with one workflow, prove the value, then add research engineering, automation, or Azure delivery only when it improves the outcome.
The workflow is the product. Research engineering, automation, and cloud delivery support it when they strengthen the result.
Sophisticated analytics and validation logic, not generic AI wrappers.
Audit-ready outputs and explicit human approval gates — built for compliance-aware investment teams, not generic enterprise AI.
Add research, automation, or Azure deployment around the core workflow as needs grow.
General LLMs generate text that looks like quant finance. Alpha Quant Agent runs the actual methods — deterministic, reproducible, and defensible to a risk committee or regulator.
| Capability | General LLM | Alpha Quant Agent |
|---|---|---|
| Sharpe / Sortino / Calmar | Describes the formula | Computes from your data — same inputs, same result, every time |
| VaR backtest (Kupiec / Christoffersen) | Can explain the test | Runs the statistical test, flags breach rate, logs result |
| Fama-French 5-factor regression | Text description only | Regression computed, rolling exposures charted, attribution decomposed |
| CPCV / Deflated Sharpe Ratio | Can describe the concept | Runs the algorithm, quantifies overfitting risk, returns a p-value |
| SEC Form ADV / PF / 13F validation | Generic text guidance | Schema validation with field-level breach log and deadline check |
| LP pitchbook or monthly factsheet | Can draft prose | Generates a formatted multi-section PDF — no hallucinated exhibits |
| Fund data stays private | Data sent to OpenAI | Runs on Azure — fund data stays within the Azure environment, not sent to external AI APIs |
| Human approval before sensitive runs | No concept of approvals | Enforced checkpoint at API level with full audit trail |
LLMs pattern-match on text that looks like correct output — they don't run the math. Alpha Quant Agent routes every calculation through a governed computation engine: same inputs, same result, every time. Reproducible, citable by methodology, defensible to a regulator.
No fund data is sent to third-party AI APIs. Alpha Quant Agent runs on Azure with AES-256 encryption and client-isolated storage. Enterprise deployments run in your own Azure tenant.
Explicit human approval checkpoints, full audit trails, and reproducible runs — designed for regulated investment teams from day one.
Your data, your Azure tenant
Your fund data stays in your Azure environment. Method execution and computations run in isolation — no portfolio data is sent to external AI APIs.
Deterministic reproducibility
Same inputs · same method · same output — every time. Auditable, citable, reproducible months later.
Full audit chain
Every run logs: who, which data, which method version, who approved, when. Downloadable audit report.
Human approval gates
Enforced checkpoints before any governed output. No bypass possible. SR 11-7 · EU AI Act · ESMA 2024.
Most analytics tools show you numbers. Alpha Quant Agent tells you when something is statistically wrong, when a mandate is at risk, and what the numbers mean — every single run.
After every run, every metric is compared against your own trailing history. Anything more than 2 standard deviations from your average is flagged — with context: '3.3σ below your 14-month average, based on 14 runs.' Activates automatically after 5 runs.
Set limits once in Settings — beta cap, drawdown floor, VaR ceiling, any metric. Every run checks the output against your limits automatically. Breaches are flagged, recorded in the audit trail, and surfaced in the Flags tab. No dashboard to remember to open.
After every analytics run, a plain-English commentary is generated from the computed numbers only — not from general knowledge. Every sentence cites which metric it came from. Your PM gets a narrative that a compliance officer can stand behind.
A practical framework for CCOs and CROs evaluating AI analytics tools — covering audit trail requirements, explainability standards, and the recordkeeping obligations that apply to every AI-informed investment decision.
Most teams do not need a full platform rollout on day one. We start with one workflow, prove it, and expand only where it adds value.