Curiosity Call & Advisory
Start here: explore the problem and the options. Focused advisory beyond that.
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lloops. Practical AI solutions for your supply chainmaritimemarketing teams
We turn real business problems into working AI solutions your team can operate, improve and own.
Stage 01 · Curiosity call
Start with a curiosity call
We explore the problem, frame what matters and decide honestly whether a deeper engagement makes sense.
Stage 02 · AI immersion workshop
One day, your team, real problems
Up to ten people. We examine your problems through an AI lens, explore your data infrastructure and agree the outcome we're aiming for.
Stage 03 · Learning loops
Learning in loops
Prototypes, user feedback and fast iterations, in days rather than weeks or months. We build the context, architecture and governance that make AI stick. Capturing exceptions and building context on the go are part of the solution, not an afterthought.
Stage 04 · Confirmed outcome
Deployed, measured, owned
A working solution in operation, measured against the outcome we agreed at the start. Your team keeps the solution and the knowledge.
Ways to work together
AI initiatives stall between experimentation and operational ownership. Five ways to close that gap, with the approach, price and outcome agreed up front.
AI Immersion Workshop
A one-day session for a team of up to ten people, built around your real problems and existing data foundations.
Plan a workshopWorking MVP
Business and IT together, from problem framing to end-to-end deployment.
Scope an MVPEmbedded AI Partner
An experienced AI partner alongside your team: knowledge base, architecture, delivery, capability.
Discuss an engagementCurated Context Graphs
Keep the judgement behind corrections, overrides and exceptions so your AI learns how your organisation really works.
Learn about context graphsWhy lloops
The engagement model is designed so the value stays inside your organisation after the engagement ends.
Learning loops
Every exception your team resolves becomes a reviewed rule. The brain compounds.
Local loops
Deployed once in your infrastructure. Owned by you. Not another SaaS.
Live loops
Not just static software. The brain runs with the team every day, updated with every decision.
Outcome-driven
Every engagement is built around a defined business problem and a measurable result.
Working software
Prototypes and end-to-end deployment, not slideware, with business and IT at the table from day one.
Your context stays yours
A knowledge base keeps organisational context in-house, with your team.
No lock-in
Model- and framework-agnostic, with transparent one-time or outcome-linked fees. No recurring SaaS dependency.
Capability transfer
The engagement leaves behind an in-house AI expert or team, not a dependency on lloops.
Who you'll work with
About Urvesh
I am Urvesh Devani, an AI and technology leader. I have spent 12 years building software, data and AI products, the last 7 of them in global supply chains and shipping.
Most recently I was VP of Technology at Portcast, where I led a fifteen-person team and shipped AI products used by Fortune 500 shippers. The latest was a freight audit product that made invoice checking about ten times faster and saved shippers roughly 20% more in costs.
The technology is rarely the hard part. The real work is picking a problem that is actually worth solving, then building something your team trusts and uses every day.
What makes AI genuinely useful in a business is everything your team already knows: how decisions really get made, which exceptions matter, why the obvious answer is sometimes wrong. My job is to capture that knowledge in a system you own, running in your own infrastructure, so it keeps getting smarter with your team long after my work is done.
Book a curiosity call
A focused conversation to explore the problem, identify options and decide the next step together. The curiosity call costs $0.
Tell me about your use case
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