Our Work

Problem

Manual underwriting teams reviewing 300+ loan applications per day with inconsistent decisioning and 48-hour turnaround.

Solution

Deployed a multi-model AI pipeline that extracts structured data from application documents, scores creditworthiness, and routes decisions to the appropriate approval tier automatically.

Problem

Sales team manually prospecting, qualifying, and onboarding merchants — a 14-day average cycle with high drop-off.

Solution

Built an autonomous lead acquisition agent that identifies target merchants, initiates outreach sequences, qualifies via conversational AI, and triggers onboarding workflows upon sign-up.

Problem

Support team handling 1,200+ tickets/week with 40% being repetitive tier-1 questions, creating backlog and slow CSAT.

Solution

Deployed a RAG-powered support agent trained on documentation and past tickets, capable of resolving tier-1 queries, escalating complex issues, and logging to the existing helpdesk.

Problem

Inventory managers manually tracking stock across 8 warehouses and 4 platforms, leading to stockouts and overstock simultaneously.

Solution

Built an AI inventory agent that monitors stock levels in real time, predicts demand using sales trends, and triggers reorder workflows automatically across supplier APIs.

Problem

Agents wasting hours qualifying inbound leads manually, with high variance in qualification quality across the team.

Solution

Built a conversational AI agent deployed via website chat and WhatsApp that qualifies property leads, captures requirements, and schedules viewings — handing off only warm leads to agents.

Problem

Customer success team building weekly business review reports manually from 6 data sources — 4 hours per client per week.

Solution

Built a data aggregation and report-generation pipeline that pulls from all sources, synthesises key insights using LLMs, and delivers formatted reports on schedule via email and Slack.