FinTech & Financial Services
Trading data, risk models, regulatory reporting — sub-second SLAs and audit-ready governance.
Sub-second latency. Exactly-once. Stateful. Replayable. Without a Kafka platform team.
Real-time used to mean 'every 15 minutes.' Now it means sub-second — for personalization, fraud, recommendations, and live analytics. Logiciel's real-time data pipeline platform delivers production-grade streaming without forcing you to stand up a Kafka platform team.
Common reality in most US data teams:
Teams here typically need:
Real-time without operational debt.
Trading data, risk models, regulatory reporting — sub-second SLAs and audit-ready governance.
Listing data, transaction pipelines, geospatial analytics — multi-source consolidation.
EHR integration, claims pipelines, clinical analytics — HIPAA-aware infrastructure.
Product analytics, customer 360, usage-based billing — embedded and operational data.
Inventory, pricing, order, and customer pipelines — real-time and high-throughput.
IoT, project, and supply-chain data — operational analytics on hybrid stacks.
Embedded data engineering pod aligned to your sprint cadence — typically 3–6 engineers + a US lead.
Senior data engineers, architects, and SMEs slotted into your team to unblock specific work.
Fixed-scope, milestone-driven engagements with clear deliverables and outcomes.
We map your stack, workloads, team, and constraints in a working session — not an RFP response.
Reference architecture grounded in your reality, with capacity, cost, and migration plans.
Iterative implementation with weekly demos, code reviews, and your team in the loop.
Managed operations or knowledge transfer — your choice. Both with US-aligned coverage.
Continuous tuning of cost, performance, and reliability against measurable SLAs.
Kafka, Kinesis, Pub/Sub, Pulsar — exactly-once.
Joins, aggregations, windowing.
Sub-second feature serving via REST/gRPC.
Sub-minute freshness in Snowflake, Databricks, BigQuery.
Idempotent, no consumer disruption.
End-to-end latency SLAs and alerting.
End-to-end p99 of 200ms-1s for typical stream-to-API workloads (personalization, fraud, recommendations, live analytics); end-to-end p99 of 30-60s for stream-to-warehouse workloads (live dashboards, operational analytics). The latency budget breaks down: ingestion (10-50ms p99), stream processing (50-200ms p99 for stateful, lower for stateless), sink writes (50-300ms p99 depending on destination). For sub-100ms hard real-time use cases (ad-tech, high-frequency trading), we recommend evaluating against specialized stream processors; for the 99% of US enterprise streaming use cases, sub-second is sufficient and operationally far simpler. Latency SLAs are part of contracts, not aspirational copy.
Can replace self-managed Kafka or sit alongside Confluent Cloud — your choice. For US teams without a dedicated platform team running Kafka, replacement is usually the right call; operational savings (no cluster ops, no version upgrades, no Zookeeper-to-KRaft migrations) typically outweigh per-message pricing. For teams already heavily invested in Confluent Cloud with KSQL, Schema Registry, and Connect, sitting alongside (using Logiciel for new use cases while keeping Confluent for existing) is often more pragmatic. We don't force migration; we provide migration tooling when you choose. Most US customers consolidate over 12-18 months as Confluent contracts come up for renewal.
Yes — same orchestration primitives, same pipeline definitions, same observability model. Change the input source (a Kafka topic instead of a Snowflake table) and the same transformation logic runs in streaming mode with windowing and exactly-once semantics. This eliminates the 'two architectures' problem where batch and streaming have different code, different lineage, different testing, and different oncall. The unification is one of the highest-leverage Logiciel benefits — operational savings and code maintenance compound over time. About 60% of our customers run unified streaming-and-batch pipelines after 12 months. For workloads that genuinely need different code paths (unusual but real), the platform supports that too.
Native CDC from databases into the streaming platform — Postgres (logical replication), MySQL (binlog), MongoDB (change streams), SQL Server (CDC), Oracle (LogMiner or Goldengate), and 30+ other databases. CDC events flow into Kafka-compatible streams with ordered, exactly-once delivery and full schema evolution support. This eliminates the typical 'two systems for CDC' pattern (Debezium for capture, separate platform for processing). CDC throughput scales with database load; we've supported customers running CDC at hundreds of thousands of events per second across multiple source databases. For high-stakes CDC (regulatory reporting, billing systems), the exactly-once guarantee extends from CDC source through stream processing through downstream sinks.
Native — including stateful operations (joins, aggregations, windowed computations). No manual idempotency keys, no dedup tables, no 'eventually consistent if you squint.' Concretely: streaming sinks (warehouse writes, downstream Kafka topics, API endpoints) are idempotent by default; stateful computations checkpoint with transactional commit semantics; replays of historical windows don't produce duplicates downstream. For US FinTech, billing-critical, and inventory-critical customers where exactly-once is a hard requirement, we've passed independent third-party audits on the guarantee. The exactly-once contract is part of platform SLA, documented precisely so audit and risk teams can validate it. Exactly-once is the platform default; you have to opt out, not opt in.
Flink is an excellent stream processing engine — capable of stateful, exactly-once, sub-second processing at scale. Running Flink well, however, is operationally hard: cluster management, state backend tuning, savepoint management, version upgrades, and SRE expertise that most US data teams don't have on staff. Logiciel gives you Flink-class capability with managed operations — same exactly-once semantics, same stateful processing, same windowing primitives, but no Flink cluster to babysit. For teams running Flink today, we provide a TCO comparison; for teams considering Flink, we typically save 40-60% in total cost when operational overhead is included. We integrate with self-managed Flink for customers who want to keep it.
Per active stream plus storage volume — predictable at scale, contractually capped, with unlimited consumers and producers. Mid-market customers (5-20 streams, moderate volume) typically pay $25-70K ARR; enterprise tiers (50+ streams, high-volume, advanced governance, dedicated TAM, US-citizen support) start at $150K ARR. We don't charge per message (which makes Confluent Cloud bills feel arbitrary) or per consumer (which discourages teams from using streams). Pricing is transparent and includes a workload-grounded TCO comparison against Confluent Cloud or self-managed Kafka at evaluation. For high-volume use cases, savings are typically 40-70%.
Bring your hardest sub-second use case. We'll architect it on Logiciel — including SLA budget, capacity, and migration path.