Our approach
An AI project does not succeed because the technology works. It succeeds because it creates value for the people using it. Our engineers work from inside your context, with your teams, your tools and your data, until that is the case.
One success criterion: the value created
Most AI projects do not fail on the technology. They fail on translation: the business describes a problem, engineering ships a feature, and the gap between the two only becomes visible when real users get their hands on the tool.
A forward deployed engineer removes that middle layer. This is an engineer who can read a business process, ask the right questions of a finance controller and of an architect, then write the code that answers the need. The loop between need and solution takes days, not steering committees.
We work alongside your teams, on your tools, your real data and your constraints, on site in Paris or remotely. We are not trying to extend an engagement: we are trying to make sure the project ships and gets used.
The same people analyse your business process and write the code. Nothing gets lost between a scoping note and its implementation.
We work with your teams, your tools and your real data. A business context is learned by practising it, not by reading a requirements document.
A project that ships but goes unused is a failure. We measure adoption and outcomes, and we keep going until the value is there.
Scope
The six parts of a forward deployed engagement. You decide which ones you hand over, and when.
We go and see how the work actually gets done: who looks for what, where information gets lost, what a task costs today. A properly understood need often fits on one page, but someone has to write it.
Every use case is written down with its users, its data, its business rules and its success criteria. That document drives prioritisation and settles arguments later on.
Not all use cases are worth the same. We rank them by expected value and implementation cost, and we tell you which ones should not be started at all.
Specifications, solution selection criteria, workshop facilitation, coordination with your other suppliers and deliverable tracking. You stay the owner, we carry the load.
A POC running on a laptop proves nothing. We handle answer quality evaluation, observability, inference cost control, security and the actual rollout to users.
Once live, we look at what users actually ask, where the answers are poor and which usages emerge. We fix, then we start over.
Why AI projects fail
Three causes we find on almost every project we are called in to rescue.
The project started on an intuition or on competitive pressure, with no identified user and no success criteria. Nobody can describe what success would look like, so nobody can tell that it is not happening.
Workshops and slides on one side, a team coding without seeing the field on the other. Every round trip takes two weeks and the product slowly drifts away from the need.
The demo impresses, then nothing happens. Answer quality unmeasured, access rights untreated, costs uncontrolled, no owner after go-live: the POC stays a POC.
Method
An AI project does not end on go-live day. That is the day you find out what users really do with it.
Immersion in the business, user interviews, a hard look at the existing data and at the tools already in place.
Use cases written down and ranked by value, with the criteria that will tell us whether it works.
Development, quality evaluation, security and rollout to real users, not to a test panel.
Analysis of real usage and failed answers, fixes, new use cases. Back to the start, with more field knowledge.
Engagement models
The cursor is set according to the skills you already have in house.
We scope, formalise and steer, your teams or your suppliers build. Useful when the technical skills are already there but the need is still vague.
Our engineers work inside your teams, on your code and your tools. Your developers build AI skills during the project rather than after it.
We deliver from needs analysis to run, then hand over. The code, the data and the architecture choices are yours.
Independence
Ask This Guy builds its own AI platform. When it fits the need, it saves months, and we say so. When it does not fit, we say that too.
Some use cases are solved with a market model, a tool you already own, or three lines of code with no AI at all. Our job is to get you to the outcome, not to place a licence.
Around the engagement
Rarely on its own: an engagement usually comes with team upskilling or a technical foundation.
The CIO path: governance, roadmap, security and the move from POC to production without depending on a single vendor.
Our CIO offerTrain the teams who will use or take over the solution, on your own use cases, from the executive committee to the technical teams.
See the training offerDocument RAG, agents, connectors and EU hosting, when a ready-made foundation beats a full custom build.
Discover the platformA demonstration built on your own data, to settle an internal debate with observable results rather than opinions.
Request a demoResources
Selected articles and videos to better understand this solution and see how it works in practice.
GuideAn enterprise RAG is relatively easy to prototype. Making it a useful, stable product is a much bigger challenge. Here are the five most common production pitfalls—and how to get ahead of them.
GuideWhy one provider is not enough for an enterprise AI SLA. Compare commitments, multi-provider gateways and fallbacks for RAG and AI assistants.
GuideWhy knowledge management initiatives fail, what AI really changes about managing what a company knows, and what it will never change.
GuideWhat is AI automation? Benefits, examples, tasks worth automating, and key steps to do it right while using as little AI as possible in production.
GuideDiscover 6 concrete productivity gains that contextual AI with RAG can bring to a company by structuring and sharing its knowledge more effectively.
GuideEnterprise AI agents: the definition, what an agent can really do, the method that gets it into production, and what it actually costs.
An engineer who works directly at the customer site, inside their business context, rather than from a delivery centre. They analyse the need, formalise the use cases and build the solution themselves. Forward deployed engineering removes the round trips between the people who understand the business and the people who write the code: they are the same person.
A consulting firm produces recommendations and a staffing company supplies profiles. We do both ends: business scoping and production code, with the same people. Our commitment is to the outcome users get, not to a number of days sold.
Yes. Some clients already have strong technical teams and mainly need the need formalised, the use cases ranked by value and the deliverables steered. We then act as business analysis lead, with the advantage of being able to challenge proposals on technical grounds.
That is the most common case. We take over what exists, identify what is really blocking (answer quality, data, access rights, costs, adoption) and put the project back on a path to production. Sometimes the conclusion is to stop a use case, and we say so.
The criteria are set when the use cases are formalised, before development starts: time saved on a task, share of useful answers, volume of requests handled without human intervention, real adoption by the teams. Once live, those measures drive continuous improvement.
Both. We work from your offices in Paris and the Paris region, especially during the analysis and formalisation phases where being on the ground matters most, and remotely for development and follow-up.
No. We work on your stack, with the models and tools that suit your context. Our platform is an option when it saves time, not a prerequisite for the engagement.
Thirty minutes to understand your context, what you have already tried and what is blocking today.
