A senior AI specialist alongside your team, accountable for the result
A Forward Deployed Engineer from our team works closely alongside your team, accountable for one named outcome.
One senior specialist who knows both Anthropic Claude and ServiceNow, brings over two decades of experience in the operations you are changing, and takes AI from idea to production in your environment. It starts with a fixed-fee diagnostic, so you know the scope and the price before you commit.
It ends with working AI your team owns.
A working AI outcome in your environment, and the capability to keep it running
A Forward Deployed Engineer engagement gives you one senior AI specialist, accountable for one named outcome, working in your environment until that outcome is real. You get a result in production rather than a pilot, built on Anthropic Claude and ServiceNow, and the working AI assets and the knowledge to run them stay with your team.
You work with the specialist
Directly, with no account layer in between.
A named lead on our side owns
Performance, escalation, staffing and change control.
We sell an outcome, not capacity.
You know the direction but cannot name the deliverables, and by the time a statement of work catches up, the technology has moved.
What changes when the right specialist works alongside your team
Your AI work moves at the speed of the technology
Decisions are made in the room, work starts in your environment from the first week, and you see progress instead of reading about it.
You close the capability gap without a hiring search
When nobody in your team has taken this stack into production, the expertise arrives now, and your people learn it by working alongside it.
You get production, not a pilot that stalls
The choices that decide whether an AI use case survives production are made early: what the model may see, where human judgment stays, what gets recorded. An experienced practitioner makes them in the room, and decision records, test discipline and acceptance standards apply from day one.
Momentum survives
One accountable specialist carries the work end to end, so it does not stall every time a project closes and the next one waits for approval.
The capability stays
At handback you keep working AI assets rather than a folder of documents, and a named person on your side who runs them.
We measure all of it on your own numbers, against the success metric agreed before day one.
From fixed-fee diagnostic to formal handback
Every engagement starts with a paid, fixed-fee diagnostic. It ends in a scoped quote and a clear go or no-go decision, and if you go ahead, the fee is credited against the engagement. You commit only when both sides understand the problem, the environment and what success looks like.
Diagnose
We look at the problem, your environment and your data, confirm the four things below are in place, and agree the outcome and its success metric.
Agree the arrangement in writing
What the engagement covers, how decisions are made, your named counterpart, escalation and change control. It runs as an agreed number of days per week rather than full time, so the commitment matches the work and your budget stays predictable.
Build in your environment, to our standards
Your systems, data and constraints. Our decision records, test discipline and acceptance standards. The first is why it is useful, the second is why it holds up after we leave.
Keep responsibility with us
You talk to the specialist directly about the work. Anything that changes what the engagement is for goes through our named lead, so quality never depends on who happens to be in the room that week.
Hand back
Formally, against triggers agreed at the start.
We take a limited number of engagements at a time.
Every specialist is senior, and we would rather do a few of these very well than many adequately.
Four things that make an engagement ready to start
An engagement moves fast when four things are in place before day one. We confirm them together in the diagnostic, because they decide whether the outcome is reachable.
A business sponsor who owns the outcome
Someone in the business, not only in IT, who wants the result and can get decisions made quickly.
A system owner with real access
Your ServiceNow instance, the data and connectors Claude needs, and an EU-resident setup cleared before work starts.
A named successor
The person on your side who takes over at handback. The one most often forgotten, and the one that makes the handback real.
An agreed success metric
What has to measurably change.
Beyond those four, speed comes from your subject-matter experts joining real working sessions, a clear line on what is in scope, and feedback from one voice inside an agreed window.
Handback is an acceptance criterion, not an afterthought
An arrangement with no defined ending becomes a permanent cost that nobody reviews. So handback is written into the engagement as a formal acceptance criterion, with three triggers agreed at the start.
The success metric is met
Your named successor runs the work without us
The knowledge transfer is complete
What stays with you is working AI, not just documentation.
The capability stays in your organization.
Your team carries it
The outcome is achieved and your team carries it.
We run it for you
The outcome is achieved and you choose to have us run it, which is Managed Services.
We stop and say so
The outcome turns out to be the wrong one, and we stop and say so rather than keep billing against it.
Claude and ServiceNow, in one specialist
The center of the work is AI: Anthropic Claude, the context it needs, and ServiceNow, where the result has to run. Usually that takes two teams. Our Forward Deployed Engineer covers both, so nothing is lost between the people who design the intelligence and the people who put it into production.
The work is context and knowledge
What the AI needs to know, where AI stops and human judgment starts, and turning a working idea into something the organization owns rather than one enthusiast maintains.
The work is making the AI count
Workflow, data, integration, governance, testing and controlled deployment, so every decision the AI reaches is recorded, actioned and auditable.
Claude reasons, ServiceNow orchestrates and records.
The most valuable problems sit where the two meet.
Governed by default, delivered from the EU.
Our whole team is based in the EU. What the AI may see and do, where human judgment stays and what gets recorded are designed in from the first week, and we establish an EU-resident setup for your data with you before work starts.
Why teams bring us in for this work
An engagement like this is only as good as the person doing it and the practice behind them. That is where we are strongest.
Both platforms, one specialist
We are a ServiceNow Consulting & Implementation and Resell Partner and an Anthropic Claude Consulting & Implementation Partner. The reasoning and the workflow are designed by the same person.
Senior only
The person you meet is the person who works alongside your team: over two decades in commercial, customer and project operations, with no account layer, no bench and no handover to someone you have never met.
We run this on ourselves first
Pulse & Patterns is a BornAI organization. We build and run Claude skills, playbooks, a Company Second Brain and AI agents in our own operations and in client environments, so what we leave with you is what we use every day.
Accountable for the result, not billing for time
We agree what has to be true at the end, we own getting there, and if the outcome turns out to be the wrong one we say so and change course.
A whole practice behind one person
Senior colleagues in process design, integration, governance and every platform next door, so a question outside the specialist's depth gets answered rather than parked.
A way of buying implementation, not a different service.
Same senior people, same standards, a different commercial shape. Discovery decides what is worth building, and where the deliverables are already knowable, a defined implementation project fits better.
Tell us what is stuck.
Describe the AI outcome you cannot reach. We will tell you honestly whether a Forward Deployed Engineer is the right shape, and if it is, scope a fixed-fee diagnostic so you know exactly what you are committing to.
The questions buyers ask us.
What is a forward deployed engineer?
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A senior specialist who works closely with a customer's team on a named outcome, rather than delivering a pre-scoped project from a distance. The model became well known through AI companies who send engineers to work alongside customers on problems that cannot be specified in advance. At Pulse & Patterns it is a dedicated AI specialist across Anthropic Claude and ServiceNow, accountable for one result, with a defined end.
How is it different from a contractor or staff augmentation?
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Contract staffing supplies availability: a competent person who takes work as it is allocated, while measuring whether anything improved stays with you. A forward deployed engagement is accountable for a defined outcome, delivered to the supplier's own standards, with a planned handback. If the arrangement is for a person's time, it is staffing. If it is for a result, and somebody on the supplier side answers for it, it is something else.
When does this make more sense than a project?
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When the scope cannot be defined without doing the work: a first AI use case, new platform capability, or an integration nobody has attempted in your environment. Also when the capability gap is narrow and hiring would take longer than the opportunity lasts. If the deliverables are already knowable and agreed, a defined project is cheaper and easier to govern.
Should we hire this capability instead?
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If the capability is central to your business and you need it indefinitely, hire, and we would rather help you define that role than occupy it. The two work well in sequence: the specialist delivers the outcome and builds the knowledge in your team while you recruit, so whoever you hire inherits something that already works.
Do we have to start with a diagnostic, and how long does an engagement last?
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Yes. The diagnostic is paid and fixed-fee, ends in a scoped quote and a go or no-go decision, and its fee is credited against the engagement if you go ahead. The engagement then lasts as long as the agreed outcome and no longer, as an agreed number of days per week, closing when the handback triggers are met.
How many engagements do you take on at once?
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A limited number, on purpose. Every specialist is senior, and each engagement runs as an agreed number of days per week, so we can tell you honestly when we can start.
What happens at the end, and what do we need to own?
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A formal handback when the success metric is met, your named successor runs the work without us, and knowledge transfer is complete. You keep the decisions, their reasoning and the working AI assets. The one thing you must provide is that successor: an outcome nobody owns afterwards reverts. From there, your team carries it, you choose to have it run as Managed Services, or, if the outcome proved to be the wrong one, it stops.
Can the engineer just work through our backlog?
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No, and that is the boundary rather than an inconvenience. An engagement built around a named outcome is a different thing from one where work is allocated week by week. If working through a backlog under your direction is what you need, that is a legitimate requirement and a different kind of supplier. We will say so in the first conversation.
Why is the engagement built around one specialist?
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Because accountability and speed both improve when one person owns the outcome end to end, with no handover between the person who understands the problem and the person building the answer. If throughput across many workstreams is the real constraint, a defined project fits better and we will say so.
Is this only for AI work?
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The center of it is AI, deliberately: Anthropic Claude and the context around it, and ServiceNow, which runs the result in production. If the work has no AI dimension at all, a defined ServiceNow implementation is the better route.