Swyft AI vs TangoEye

Swyft AI is a RevOps platform that uses agents to run workflows across your GTM stack and show what actually happened in your systems. It connects Salesforce, HubSpot, Slack, Gmail, and other tools, then automates processes like expansion signal detection, churn risk alerts, pipeline reporting, and closed-won handoffs.\n\nThe product splits into three layers: Process Execution for building workflows that collect and sync data, Process Intelligence for tracking metrics and trends from that data, and AI Recommendations that surface gaps and suggest workflow improvements. Teams get visibility into what executed, what changed, and what got missed.\n\nBuilt for RevOps leaders, sales operations, and customer success operations at B2B SaaS companies from growth-stage startups to enterprise. Swyft AI is SOC 2 Type II certified and hosts on Heroku with CRM-based SSO sign-in.

TangoEye is in-store retail analytics and automation software that turns existing CCTV cameras into footfall, zone, and conversion data. Instead of manual clickers or new sensor hardware, it plugs into the camera network retailers already run. The platform splits into four modules: Tango Traffic for walk-in counts, Tango Zone for shelf and area heatmaps, Tango RevOp for missed-sale tracking, and Tango StoreOps for camera and network uptime.

Most retail analytics vendors sell dedicated counting sensors or gate hardware. TangoEye's pitch is pure software on top of cameras that are already mounted, which keeps rollout cost lower and avoids ripping out existing infra. That trade-off means you need working CCTV coverage and bandwidth at each store, but you skip the cap-ex cycle of installing parallel sensor networks.

Retail operations teams use it to see bounced visitors, engaged shoppers versus casual walk-ins, and zone-level dwell time without standing at the door with a tally counter. Brand leaders at multi-store chains like Lenskart and Nykaa, listed on TangoEye's site, get a shared view of conversion leaks and infrastructure blind spots across locations. Store managers get hourly alerts when cameras or networks drop so footfall data does not silently go missing.