Sigmoid

McKinsey research found that reusable data products can cut the time to implement new analytics and AI use cases by up to 90% and reduce total cost of ownership by up to 30%. Yet most banks are still stuck in the old model: ✅ Three proprietary warehouses that don't talk to each other ✅ A legacy Hadoop cluster everyone works around but no one decommissions ✅ Hand-coded ETL pipelines for every new Bloomberg or Refinitiv feed ✅ 27–32% of cloud spend wasted on duplicate storage and idle compute Gartner predicts 60% of AI projects will be abandoned without AI-ready data. The reason is rarely the model - it's the fragmented, ungoverned data underneath. Sigmoid builds governed data products, lakehouse architectures, and self-service data platforms for banks ready to move from pipeline bottlenecks to production-grade AI.

Gartner's February 2025 data and analytics trends report is blunt: through 2026, organizations will abandon 60% of AI projects that aren't supported by AI-ready data. And 63% of organizations say they don't have the right data management practices for AI. For banks, this plays out daily: ✅ Risk, compliance, and trading desks run on siloed data warehouses that can't share datasets ✅ Data scientists spend nearly half their time cleaning data instead of building models ✅ Every new use case requires hand-coded pipelines that take weeks to deliver McKinsey found that reusable data products can cut new use-case delivery time by up to 90% and reduce TCO by up to 30%. Sigmoid builds AI-ready data foundations for financial institutions - from lakehouse migration and governed data products to production-grade pipelines that serve quant, risk, and analytics teams from a single trusted source.

McKinsey research found that reusable data products can cut the time to implement new analytics and AI use cases by up to 90% and reduce total cost of ownership by up to 30%. Yet most banks are still stuck in the old model: ✅ Three proprietary warehouses that don't talk to each other ✅ A legacy Hadoop cluster everyone works around but no one decommissions ✅ Hand-coded ETL pipelines for every new Bloomberg or Refinitiv feed ✅ 27–32% of cloud spend wasted on duplicate storage and idle compute Gartner predicts 60% of AI projects will be abandoned without AI-ready data. The reason is rarely the model - it's the fragmented, ungoverned data underneath. Sigmoid builds governed data products, lakehouse architectures, and self-service data platforms for banks ready to move from pipeline bottlenecks to production-grade AI.

Field teams don't resist AI because they fear it. They resist it because most AI tools add clicks without adding value. Sigmoid built pre-call and post-call agents that do one thing well: make the next call easier. Prescribing context, engagement history, and competitive signals, all surfaced before the meeting and logged after it. No new platform or extra workflow. The result? 80% faster call prep, and field teams that actually use the tool.

Your agents are only as trustworthy as the data underneath them. Most pharma commercial teams have five data sources, three CRM layers, and no single version of HCP truth. When you deploy agents on top of that, you don't get intelligence, you get compounded errors at speed. Sigmoid builds one unified HCP signal fabric beneath every commercial agent: claims, consent, CRM, and third-party data, governed, linked, and agent-ready. We then deploy evaluation-gated AgentOps with regulator-ready audit trails so every action is explainable. You own the IP, you own the stack, and we transfer the capability. See how we build agent-ready commercial data foundations!

Your reps spend more time preparing for calls than making them. Sigmoid's agentic pre-call and post-call assistants are live with international field teams today, cutting call preparation time by 80%. Before every HCP interaction, the agent surfaces the right context: prescribing history, last engagement, competitive signals, and a recommended talk track. After the call, it logs outcomes and triggers next-best-action automatically. No extra clicks or new systems to learn, just the next call, ready faster.