We embed within client teams, diagnose where AI creates genuine operational leverage, and build working systems. Every engagement ends with a working system and a team capable of running it independently.
Most AI transformation fails not because the technology does not work but because the people implementing it are too far from the problem.
Advisors produce roadmaps. Vendors sell tools. Implementation partners arrive after the strategy is set and build what they are told. By the time anyone who can actually write production code is in the room, the wrong problem has already been defined.
Forward Deployed Engineering is a different model.
A Forward Deployed Engineer embeds directly within your team — inside your codebase, your data, your workflows, your organisational reality. They diagnose the problem from the inside. They build working systems, not proofs of concept. They work alongside your engineers so that when the engagement ends, your team owns what was built and knows how to extend it.
The term was pioneered at Palantir. It is now the model that OpenAI, Deloitte, Accenture, and others are building practices around. The reason is simple: AI systems that work in production require someone who can hold the business problem and the engineering reality in the same frame simultaneously. That is not a skill that sits in a strategy deck or a vendor demo. It sits in an engineer who has shipped production systems and understands how businesses actually operate.
Every previous wave of enterprise technology — ERP, cloud migration, mobile — produced a generation of implementation partners who learned the technology and sold the deployment. AI is different in three ways that make the forward deployed model not just better but necessary.
The problem definition is harder. With ERP you knew what you were implementing. With AI the highest-value use cases are not obvious from the outside. They require someone embedded in your operations who can see where the data is, where the workflow breaks, and where an AI system would actually change an outcome versus where it would add complexity without value. That diagnosis cannot be done remotely.
The technology is moving faster than any training programme can track. The gap between what is possible today and what was possible six months ago is larger than any previous enterprise technology wave. You need someone who is building with current tooling in production right now — not someone who learned the stack in a course and is now selling implementations.
The failure modes are invisible until they are not. AI systems that work in demos fail in production for reasons that are not obvious until you have seen them. Hallucination in high-stakes workflows. Retrieval quality that degrades with real document corpora. Agent loops that break on edge cases the demo never surfaced. A forward deployed engineer has seen these failure modes. They design around them before they become incidents.
We meet you where the problem is — either as an embedded individual contributor or as the technical execution partner for your delivery pipeline.
We work inside your codebase, your processes, and your delivery cadence. We diagnose where AI creates genuine operational leverage, build working systems, and leave your team capable of running them without us.
Start an EngagementYou own the engagement and the commercial terms. We embed, build, and ship. White-label or co-branded depending on what the engagement requires.
Partner With UsMost AI engagements fail not because the technology doesn't work, but because the people implementing it don't understand economic incentives, organisational behaviour, or how technology actually gets adopted at scale versus how it gets hyped. A productivity gain that looks good in a demo but doesn't survive contact with how a business actually operates is not a gain. This context shapes every engagement I take.
I have shipped SDKs across 15+ languages, architected solutions across enterprise accounts in logistics, fintech, and healthcare, and built live products across HRMS, payroll, school ERP, drone firmware, and precision agriculture — simultaneously, not sequentially. I have operated across AWS, Azure, serverless, cloud-native, and bare metal. React, Python, Ruby, Go, Rust, Java — I meet clients wherever their stack is.
Underneath all of it is deep foundational knowledge of operating systems, networks, and protocols — not as a user of abstractions but as an engineer who has implemented IPSec, PKI, SAML, HTTP proxy internals, SSL termination, and real-time pub/sub infrastructure at production scale. AI tooling — agentic workflows, RAG pipelines, multi-model orchestration across cloud and local models — is embedded into how I operate across my own ventures simultaneously.
Define the problem, the integration points, and the success criteria. No open-ended retainers.
Inside your team, codebase, and delivery cadence. We work the way your engineering team works.
Your team runs the system independently when we leave. That is the definition of success.
Agentic document processing pipelines and semantic search embedded into existing platforms for production accuracy and latency.
Computer vision systems for quality inspection on precision manufacturing lines, identifying defect classes without manual intervention.
Edge AI diagnostics shipped directly into connected hardware to enable predictive failure detection.
Real-time load forecasting and routing systems for operations across regional distribution networks.
Drone telemetry integrated with field sensor data into a centralized operations platform for actionable agronomic decisions.
Agentic workflow automation embedded into existing product teams, shipping AI-native features within their existing delivery cadence.