Production AI.
Built to ship.
Loom designs, builds, and deploys AI systems for enterprises and high-growth startups.
Pilot in weeks. Production in a quarter.
Where we work
Applied AI
Production AI for the work that matters. Customer operations, revenue ops, internal tooling, decision systems — built to ship and built to run.
Forward-Deployed Engineering
Senior engineers embedded with your team. We don't hand off a deck — we sit alongside your people and ship code in your environment, against your reality.
Foundational Models
Custom model work where general-purpose foundation models aren't enough. Fine-tuning, distillation, domain adaptation, and selective training from scratch.
Most AI projects never reach production.
70% of enterprise AI initiatives stall in proof-of-concept (Deloitte). In July 2024, Gartner forecast that 30% of GenAI projects would be abandoned after proof of concept by end of 2025; the measured outcome was at least 50% (Gartner). The forecast undershot. The industry average to production: 18 months.
We ship in 30 days.
Senior practitioners embedded with your team, shipping in your codebase against your real infrastructure.
Scope
Identify the highest-impact initiative with projected P&L outcomes.
Build
Ship a working, production-grade system on your real infrastructure.
Scale
Expand to full enterprise rollout once the results are clear.
Leadership
Built by senior practitioners.
Loom is led by Mugdha Pandit and Kevin Dickerson — three decades of combined experience across cloud-scale architecture, machine learning research, and enterprise AI deployment.
We also take on advisory roles and board seats for AI startups and enterprises in transition.
Meet the team →Engineering Heritage
Hyperscale cloud engineering
We've led customer engineering and platform strategy inside one of the largest public clouds. We know how the deepest layers of the AI stack actually work, and we use that knowledge in every engagement.
Silicon-level performance
Production AI is bottlenecked by data movement, networking, and latency. We've solved those problems for leading semiconductor and storage companies, and we apply the same thinking to enterprise inference.
Enterprise transformation
We've led Fortune 500 modernization programs and cloud-native transitions across North America and India. We know how to ship in environments where the cost of getting it wrong is measured in billions.
Applied research
Our technical direction is rooted in staff-level research at leading computational institutions, including DARPA-funded work in machine learning. We bridge academic results and production deployment.