Fixed scope or agile sprint — SYSTEMBENDER assembles expert teams and delivers end-to-end across Data & AI, application development, and cloud infrastructure. Four projects delivered. Three detailed below.
Click any project to see the full story — the problem, the solution, and the numbers.
The client's legal team spent 60–70% of their time manually reviewing, classifying, and tracking contracts across multiple stakeholders. Critical issues were routinely missed, feedback loops were slow, and document readiness was opaque — creating compliance risk and costly approval delays.
SYSTEMBENDER built a full-cycle AI-powered contract management application — trained specifically on legal language — to automate review, classification, and issue flagging. Every document received a real-time confidence score, stakeholder feedback was streamlined, and the system integrated directly with existing workflows.
We built client trust by aligning the solution with the legal team's specific workflows from day one. We iterated rapidly with a continuous feedback loop and delivered ongoing model updates as legal standards evolved — ensuring long-term accuracy and internal adoption.
A multi-brand retail group's analytics stack was built on SAP BW — a legacy platform struggling with modern data volumes. Building new dashboards required IT involvement, took weeks, and created a decision-making bottleneck across CIO, CFO, and CEO levels. Business teams had no self-service access.
We replaced SAP BW with a modern AWS data lakehouse: Amazon Redshift for scalable compute, S3 for raw data, AWS Glue for ETL pipelines, and QuickSight for business-ready dashboards. The proof of concept deployed Amazon Q — enabling stakeholders to query their own data in natural language, no SQL required.
SYSTEMBENDER spent six months building executive alignment — educating the CIO and CFO on AWS capabilities, co-creating a CEO pitch with sample-data demos, and partnering closely with AWS to structure the commercials. We provided end-to-end pre-sales and delivery coverage throughout.
A Singapore digital transformation initiative needed real-time anomaly detection on industrial equipment sensor data. Existing monitoring was entirely reactive — failures were identified only after they occurred, causing unplanned downtime. Leadership also needed contextual intelligence beyond raw alerts.
SYSTEMBENDER built an end-to-end MLOps pipeline on AWS: SageMaker for anomaly detection model training with hyperparameter tuning, AWS A2I for human-in-the-loop validation, Glue for data transformation, and QuickSight with Amazon Q for interactive natural-language dashboards — contextual insights surfaced alongside every alert.
We built executive confidence through close collaboration with the client's leadership team and AWS. We proposed the POC architecture, managed stakeholder coverage across multiple teams, and structured the commercials through the AWS partnership — delivering end-to-end accountability from day one.
Legal, consulting, and advisory firms needing AI to scale knowledge work
Fast-moving AI companies that need senior technical horsepower without the overhead
Banks, fintechs, and insurers leveraging AI for trading, fraud detection, and analytics
Multi-brand and omnichannel retailers modernising analytics and personalisation
Industrial operations using ML and IoT for predictive maintenance and quality control
Global shipping companies (including NYK Line) modernising operations with data and AI
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