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Artificial Intelligence

AI at Algotech

We don't sell AI. We embed it where it solves real operational problems for regulated fund institutions.

Every Algotech platform includes AI capabilities built for the buy-side: document understanding, anomaly detection, conversational data querying and process automation. All of it under strict European data sovereignty and source traceability requirements.

AI, platform by platform

Our AI principles

Principles before models

  • Sovereignty first.

    European LLM infrastructure (Anthropic Claude via Anthropic, OpenAI when EU residency is met, open-source models via Ollama for sensitive deployments). GDPR and AI Act compliant.

  • Source citation by design.

    Every AI-generated answer cites its source. In regulated workflows, we have zero tolerance for hallucinations.

  • Fit for the use case, not for benchmarks.

    We choose models for the real operational task, not for their benchmark scores. Sometimes the best model is the smallest one.

  • Predictable costs.

    Open-source models handle high-volume recurring tasks. Frontier models are reserved for tasks where they create measurable value.

AI, platform by platform

What AI does in each platform

AI in AlfaWay (File Monitoring & Reporting)

See the platform

  • Anomaly detection on incoming files.

    Automated controls on file presence, format, integrity and volumetry, plus business-level coherence checks on the NAV calculation files, position files and transaction blotters sent by your fund administrator.

  • Conversational data querying.

    Your operations team can query its own fund administrator data in plain language: “Show me the NAV of fund X on 30 April with breakdown by asset class.” Every answer is cited back to the source file.

  • Intelligent ticket categorisation.

    Tickets your operations team opens with your fund administrator are categorised, prioritised and logged in line with DORA Article 28.5 requirements on operational monitoring of critical ICT third-party providers.

  • Proactive SLA alerting.

    SLA breaches are detected in real time, and incident criticality is classified for DORA-compliant incident logging.

AI in Fund Accounting

See the platform

  • AI accounting interpreter.

    It understands accounting entries, journal lines and general ledger movements in natural language. The interpreter helps fund accountants classify complex transactions, suggests reconciliation matches and explains the accounting logic behind each entry.

  • Automated reconciliation suggestions.

    Machine-assisted matching of cash, position and broker confirmations against NAV calculation lines. Exceptions are surfaced for human review.

  • Anomaly detection on NAV calculation chains.

    Cross-checks between subscription/redemption flows, position movements and NAV variation, with explainable alerts when discrepancies appear.

  • Natural language querying of the general ledger.

    “Show me all entries related to dividend distributions for fund Y in Q2”, with source citation to the underlying ledger entries.

Technical foundation

The stack underneath

  • European-hosted LLM infrastructure.
  • Vector databases for retrieval-augmented generation (PostgreSQL with pgvector, Qdrant).
  • Frameworks: LangChain, LlamaIndex.
  • Open-source model deployment via Ollama for confidential workloads.
  • End-to-end logging and access traceability for regulatory audit.

Next step

Curious how AI could reduce operational cost in your fund operations?

Start with a Platform Diagnostic.

Request a Platform Diagnostic