Case Studies
Internal R&D protocols and architecture write-ups from FalconicLab's own engineering work, with the benchmarks and technical decisions behind each build.
Built By
FalconicLab
Focus Area
Banking & Fintech
Services Used
Challenge
Legacy rule-based fraud engines common in fintech stacks run 100-250ms per transaction, which is too slow for real-time authorization at high volume and produces high false-positive rates.
Solution
We built an in-memory feature store paired with an ONNX-optimized inference pipeline running on Cloudflare Edge workers and Kafka event streams, detailed further in our published experiment.
Results
- Reduced simulated false-negative fraud misses by 84% versus a rule-based baseline
- Held average inference scoring latency to 28ms under load
- Sustained target throughput with zero dropped events during load testing
Built By
FalconicLab
Focus Area
Healthcare & Life Sciences
Services Used
Challenge
Regulated industries like healthcare and legal need document processing pipelines that handle messy scanned inputs while keeping data isolated and auditable.
Solution
We engineered an encrypted computer-vision and OCR pipeline architected for compliance-sensitive deployments, with structured JSON extraction and full audit logging.
Results
- Reduced structured-extraction review time versus manual processing in internal testing
- High sensitivity on structured field extraction across varied scan quality
- Architecture designed to support HIPAA- and SOC2-aligned deployment patterns
Built By
FalconicLab
Focus Area
Manufacturing
Services Used
Challenge
Unplanned equipment failure on manufacturing lines is one of the costliest and hardest problems to solve reactively — by the time a fault is audible or visible, downtime has already started.
Solution
We prototyped an IoT edge ingestion pipeline streaming sensor telemetry into a time-series RUL estimation model, with integration patterns for SCADA and maintenance-dispatch systems.
Results
- Demonstrated early fault detection ahead of simulated failure events in test data
- Designed integration pathways for SCADA and SAP maintenance dispatch systems
- Validated architecture for real-time ingestion at industrial sensor volumes
Built By
FalconicLab
Focus Area
Retail & E-Commerce
Services Used
Challenge
Standard keyword search fails on natural-language queries like "lightweight summer linen suit for a beach wedding" because it can't interpret buyer intent, only exact terms.
Solution
We built a hybrid vector search engine combining BM25 lexical matching with dense embedding search and reciprocal rank fusion, the same architecture detailed in our RAG research post.
Results
- Eliminated zero-result pages for natural-language test queries in internal evaluation
- Held search response latency under 40ms in benchmark testing
- Architecture designed as a drop-in layer for existing product catalogs