Getpisco provides an AI‑driven platform that lets hardware engineers search for electronic components using natural‑language queries and receive supply‑aware recommendations that prioritize approved parts while flagging duplicates and risky items. The system continuously monitors end‑of‑life and NRND statuses, offers proactive risk alerts, and enables instant "what‑if" scenario modeling to assess design changes, cost impacts, and geopolitical factors, helping teams reduce redesign cycles and accelerate time‑to‑market.
Funding
Funding not disclosed
Founders
Product
Problem
Hardware teams spend extensive time searching fragmented component databases, often selecting parts with incomplete data that lead to supply risks, costly redesigns, and delayed product launches.
Solution
Pisco offers an AI-driven platform that streamlines part discovery and supply‑chain validation for electronic hardware development. Engineers can search for components using natural‑language queries, receiving supply‑aware recommendations that prioritize approved parts and flag duplicate or risky items. The system continuously monitors end‑of‑life (EOL) and not‑recommended‑for‑new‑design (NRND) statuses, providing proactive alerts to prevent supply interruptions. Integrated “what‑if” scenario modeling lets teams assess design changes, cost impacts, and geopolitical factors such as tariffs in seconds. By automating BOM risk assessment and alternative part analysis, Pisco reduces manual review effort and enables faster, more reliable time‑to‑market.
Target Audience
Primary users are design engineers, component engineers, and supply‑chain professionals within electronics OEMs, from early‑stage startups to large enterprises.
Features
- AI‑powered natural‑language search across global component databases with real‑time availability and pricing data
- Automated supply risk assessment that detects EOL/NRND parts and issues proactive alerts
- Prioritization of approved and duplicate‑free parts to minimize redesign cycles
- Instant “what‑if” scenario modeling with change‑impact tracking and tariff monitoring
- Alternative part suggestions with trade‑off analysis and cost optimization
- Collaborative knowledge base that captures engineering decisions and feeds back into AI recommendations