On-device clinical triage
Intake notes had to be classified without any patient data leaving the hospital network.
A quantized small language model running fully offline on existing hardware.
A sample of recent engagements.
Intake notes had to be classified without any patient data leaving the hospital network.
A quantized small language model running fully offline on existing hardware.
An overnight batch was too slow to stop fraud while it was happening.
A Kafka/Flink stream scoring every transaction in under a second.
Regulatory questions spanned many linked rules that flat search could not follow.
A knowledge-graph retrieval layer with fully traceable sources.
Cross-border model work was blocked by privacy rules on real customer data.
Privacy-safe synthetic records with measured re-identification risk.
Routine calls needed handling in four Nordic languages with natural turn-taking.
A low-latency speech-to-speech agent with clean human handoff.
A silent quality regression was reaching users before anyone noticed.
End-to-end tracing and continuous evaluation on live traffic.
Keyword search missed relevant products across a ten-million-item catalogue.
A re-architected embedding and re-ranking pipeline, hybrid with keywords.
Research work needed gathering and cross-checking across many sources, reliably.
Cooperating agents with human checkpoints at each stage.
AI systems needed classifying and documenting ahead of a conformity assessment.
Risk classification, documentation and human-oversight controls.
Sensor anomalies in the field had to be flagged without reliable connectivity.
On-device models with alerts streamed back in seconds.
A public launch needed assurance against jailbreaks and bias gaps.
Structured adversarial testing with severity-ranked findings.
Search latency threatened to climb as the corpus grew into the millions.
A cost-tuned vector database and chunking strategy.
Figures are representative of the type of outcome and will be replaced with client-approved numbers.
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