Tom Pinder

Operations and systems engineer who deploys AI with guardrails.

I spent 15 years running warehouse and supply-chain systems. Now I deploy AI into production with a governance and accuracy focus. The rare part is the overlap: an operations person who has run a distribution floor and also builds the guardrails that keep AI honest.

What I work on

AI guardrails and safe deployment

I build the layer that stops AI from shipping wrong answers. For PRAPI I built the anti-fabrication system: a claim-gate that blocks unverifiable quantitative claims before anything publishes, a send-guard that refuses unresolved placeholders, and grounding that ties generated output back to a verifiable source. I authored the brief.md spec, an open format for giving an LLM the right inputs so it stops hallucinating. The throughline is integrity: AI systems that refuse instead of guess.

Warehouse and supply-chain operations

I ran Manhattan SCALE WMS across six distribution centers. I built operational reporting on SQL Server and T-SQL, with 10 years in SSRS and shorter stints in Snowflake and Cognos, in Azure. The work was the real core of distribution: WMS rollouts and go-lives, inventory accuracy, throughput and bottleneck diagnosis, slotting, and the reports operators actually use to run a floor.

The intersection

Having run a distribution center's systems, I know where AI helps, where it is theater, and where letting a model touch an inventory decision without a guardrail is a real risk. That is the forward-deployed-engineer-in-logistics profile: translate between the people who run operations and the AI that is supposed to help them, and ship it without breaking trust.

Background

Currently

Open to remote roles where operations depth and correct AI deployment meet: forward-deployed and AI-deployment engineering, implementation and solutions consulting, BI and SQL development, data and supply-chain analysis, and WMS.

FAQ

What does Tom Pinder do?
He is an operations and systems engineer who deploys AI into production with accuracy guardrails. He has 15 years in warehouse and supply-chain systems and builds the anti-fabrication layer that keeps AI output verifiable.
What is Tom Pinder's AI-deployment background?
He built PRAPI's claim-gate, send-guard, and grounding layer, which block fabricated or unverifiable output before it ships, and he authored the brief.md spec for portable context. His focus is deployment and governance, not model training.
What is Tom Pinder's supply-chain and warehouse experience?
He ran Manhattan SCALE WMS across six distribution centers and built operational reporting on SQL Server, T-SQL, SSRS, Snowflake, and Cognos, covering WMS rollouts, inventory accuracy, throughput, and slotting.
Is Tom Pinder available for work?
He is open to remote roles in forward-deployed and AI-deployment engineering, implementation and solutions consulting, BI and SQL development, data and supply-chain analysis, and WMS.