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Notes on building and running AI systems in production, from the engineers who deliver them.

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Forward Deployed Engineering

How to Choose a Forward Deployed Engineering Company: Nine Questions That Separate Them

Nine questions to ask any forward deployed engineering company, with good and red-flag answers and a scorecard to compare FDE partners before you sign.

Oct 7, 202612 min read

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A small robot points at a clipboard listing nine numbered questions: team, production, security, handover, time zone, IP ownership, exit, quality and commercial model

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An engineer at a desk facing a signpost between two doorways: Hire an FDE, with a team at a workflow diagram and a whiteboard checking integrate, customize, deploy and drive adoption; and Don't Hire an FDE, with a quiet office and a whiteboard listing off-the-shelf tool, works out of the box, existing team and no integration needs

Forward Deployed Engineering

When to Hire a Forward Deployed Engineer (and When You Genuinely Do Not Need One)

Split office scene: on the pre-sale side a solutions engineer presents a product demo to two colleagues, on the post-sale side a forward deployed engineer writes code beside a screen linking customer systems, production and real data

Forward Deployed Engineering

Forward Deployed Engineer vs Solutions Engineer: Where the Line Actually Sits

A glass checklist titled 12 Security Controls, ticking off access and identity, data protection, model security, monitoring, incident response and vendor controls, beside a globe carrying a padlock shield and ringed by cloud, database, code, document and team icons

Security, Compliance & Offshore Delivery

Data Security in Offshore AI Development: The 12 Controls to Demand in Writing

Glass tiles climbing a stone staircase on a desk, from state design through checkpointing and durability, human in the loop, retries and timeouts, observability and tracing, and testing and evals to a production-ready panel, beside a notebook sketch titled from prototype to production

Agentic AI & LLM Engineering

LangGraph in Production: State, Checkpointing and the Failure Modes Nobody Warns You About

A glowing MCP hub on a desk wired to AI apps such as ChatGPT, Claude, Gemini, Copilot and VS Code on one side and enterprise databases, files, CRM and ticketing on the other, above three tiles for tools, resources and prompts

Agentic AI & LLM Engineering

What Is MCP? The Model Context Protocol Explained for Enterprise Buyers

A signed contract and pen on a desk beside law books labelled US law, UK law and India law, and a wooden box marked IP holding cards for code, data, AI model and prompts

Security, Compliance & Offshore Delivery

Who Owns the IP When You Outsource AI Development? A Plain-English Guide

An empty enterprise boardroom with a laptop and printed reports on the table, and six sourcing channels pinned to the glass wall: global SIs and consultancies, hyperscaler partners, dedicated pods, specialist AI firms, staff augmentation and freelancers

AI Strategy: Pilot to Production

How Enterprises Buy AI Engineering Capacity in 2026

Seven AI development engagement models arranged around a central AI cube: time and materials, fixed scope, dedicated pods, staff augmentation, forward deployed engineers, build-operate-transfer and outcome-based

AI Pods & Engineering Teams

AI Development Engagement Models Explained: Who Carries the Risk, and When Each One Fits

An AI engineer being interviewed across a desk, with a RAG pipeline from documents to retrieve to generate to evaluate, evaluation metrics and a six-step hiring checklist shown alongside

AI Pods & Engineering Teams

How to Hire AI Engineers in 2026: Roles, Screening, Red Flags

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