Diffee VS Prolyz

Prolyz's answer:

Three things that rarely ship together in one platform:

• Built-in OLAP. A ClickHouse engine is part of the product, so interactive analysis and ML forecasting run on live data with no separate warehouse to stand up.
• Causal AI, not correlation. Prolyz Agent uses causal inference (Pearl's framework via DoWhy) to isolate why a number moved, and returns the reasoning with the answer.
• Privacy-first local AI. All reasoning runs on a model inside your environment. Neither data nor metadata leaves it, which is what makes on-premise and air-gapped deployments possible.

Underneath, four governed layers hand data to each other: Data (catalog, lineage, quality), Sync (real-time CDC and ETL), Report (OLAP analytics) and Agent (decisions).

Prolyz's answer:

It depends on what you already run, and we try to be honest about that.

• Against pipeline tools (Fivetran, Airbyte): they move data into a warehouse you still have to buy, model and report on. Prolyz moves it and then governs, analyses and explains it in one platform.
• Against enterprise suites (Informatica): they are deeper in master data management, but reporting and reasoning live in other vendors' tools. Prolyz ships built-in OLAP and causal AI.
• Against BI (Power BI, Tableau): a dashboard shows what changed. Prolyz Agent traces why, with causal inference rather than correlation.

And one hard requirement others rarely meet: everything, including the AI, can run on-premise or air-gapped, with no data or metadata leaving your environment.

Prolyz's answer:

Data and analytics teams in mid-market and enterprise organisations that run business-critical systems (SAP, CRM, operational databases) and are expected to answer questions faster than a nightly batch allows.

Typical buyers: heads of data, BI and analytics leads, data engineers and CTOs. The strongest fit is regulated sectors, finance, insurance, healthcare, public sector and manufacturing, where data has to stay on-premise or in-country and every number needs an audit trail.

Common starting point: a team that already has dashboards, but where the question "why did this change?" still takes days.

Prolyz's answer:

Prolyz was founded in 2026 by two engineers who had spent years building data platforms for enterprises, and kept watching the same thing happen: the pipelines worked, the dashboards were accurate, and nobody could say why a number moved. Answering that took days of exports, meetings and guesswork.

The second pattern was regulatory. In finance, insurance and the public sector, the answer to "can we send this data to a cloud model?" was simply no, which ended most AI projects before they started.

So Prolyz was built around two commitments: reason about causes rather than correlations, and do it entirely inside the customer's environment. The company is registered in London as Prolyz Ltd, with engineering in Istanbul.

Prolyz's answer:

• Services: .NET 8 microservices behind a YARP gateway, OAuth 2.0 / OIDC via OpenIddict, RabbitMQ and MassTransit for events.
• Data path: PostgreSQL for OLTP, log-based CDC into ClickHouse for OLAP, Redis for cache, Qdrant for vector search.
• AI: Python and FastAPI with DoWhy for causal inference and local LLMs (Ollama) so nothing leaves the customer's environment; MCP servers pull external context.
• Frontend: React and Next.js.
• Operations: Docker and Kubernetes with Helm, OpenTelemetry, Prometheus, Grafana, Loki and Tempo.

Deployment: managed SaaS, hybrid, or fully on-premise and air-gapped, from the same codebase.