Three engagements. Different industries, different problems. One consistent outcome — AI that actually gets used and shows up in the numbers.
A fast-scaling marketing team was spending days each week manually pulling numbers from Google Ads and Meta, formatting reports, briefing copywriters, and chasing approvals. Every step was a handoff. Every handoff created delay. The team was good — they were just spending their time on the wrong things.
We built an end-to-end AI ecosystem that connects directly to both ad platforms, generates live analytics dashboards, formats marketing reports automatically, and feeds brand-aware copy directly to the creative team. Zero manual exports. Zero formatting. Zero waiting.
A national education nonprofit was running programmes across 45 cities with no formal data infrastructure. Impact reporting took weeks. Donor reports were built manually from scattered spreadsheets. Field teams had no consistent way to track learner progress — and leadership was making critical decisions based on gut instinct, not evidence.
We designed and built a low-code, AI-enabled data collection and reporting system — built specifically for volunteers and non-technical staff. No engineering team. No training budget. Just a system that people actually used.
Engineering teams at a fast-growing SaaS company were losing significant developer hours to the bug triage and fix cycle — identifying issues, understanding the codebase, writing fixes, raising pull requests, waiting for review. High volume, highly repetitive, and expensive in both time and focus.
We built an autonomous AI agent that reads a bug report, understands the relevant codebase, writes the fix, and raises a production-ready pull request — without human intervention at each step. Built using Claude and MCP, deployed in production.
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