Forward deployed engineering - Senior engineers embedded inside your workflows, turning AI into systems that actually work.
AI is not the same as software. With traditional software you ship a stable product and the customer adopts it. With AI you are deploying something that keeps evolving as models, capabilities, and best practices change every few months, and those changes often reshape the workflow itself. The old "deliver it and walk away" model simply does not fit.
Forward deployed engineering closes that gap. We put strong technical people directly alongside the operators and subject-matter experts who own the outcome. That proximity forces the work to solve the actual problem instead of a sanitized version of it, and it lets us extract the tacit knowledge, build the evaluations, and close the production feedback loop that make AI dependable at scale.
What we deliver - Practical outputs, not generic AI strategy
- An embedded engineer or small pod working next to your operators and domain experts, mapping the real workflow and where humans and agents each step in.
- Secure connection of agents to your data and internal systems, with scoped access, entitlements, monitoring, and logging designed in from the start.
- Evaluations that reflect how your work actually breaks, plus a production feedback loop so the system measurably improves from real outcomes.
- Change management, enablement, and a clear handover path so the new human-plus-agent process holds up as models and best practices keep shifting.
Best fit - Who this service is for
- Organizations moving from chat experiments to agents that participate in real, revenue- or service-critical workflows.
- Leadership teams that want a dedicated, accountable partner to bring automation to their teams instead of leaving it to every individual employee.
- Enterprises with decades of legacy systems and data that must be made safely usable by AI, with the right access controls, logging, and human-in-the-loop oversight.
Typical architecture - Designed for reliability, grounding, and operational handover
The exact stack depends on the problem, but these are the design principles we usually optimize for.
- Embedded, not at arm’s length. Engineers work inside your context, alongside the people who own the outcome, so decisions are grounded in the real problem rather than a slide-deck version of it.
- Built on tacit knowledge. We extract the undocumented know-how that lives with your subject-matter experts and encode it into the prompts, tools, retrieval, and evaluations an agent can actually use.
- A closed production feedback loop. Agents sit inside real workflows and improve from actual decisions, with evaluations that capture how things genuinely fail and a loop that feeds the learnings back in.
- We treat AI delivery as ongoing. Because models and best practices change constantly, we design for continuous improvement and keep your workflow current as upgrades change what is possible.
- Engineering depth at the front line. ETH Zurich and EPFL-trained engineers with consulting and regulated-delivery experience, comfortable embedding directly in high-accountability environments.
- Outcome ownership, not just output. The hard parts of agentic work — instructions, context, intervention, review, and integration — stay in scope, so you get reliable work product instead of impressive demos.
FAQs - Questions we hear early in the conversation
These are the kinds of questions that usually matter before a team commits to scope, architecture, and rollout.
How is forward deployed engineering different from normal consulting?
Instead of delivering recommendations from the outside, our engineers embed alongside your team and own the outcome with you. That proximity is what makes AI solve the real problem and keep improving once it is in production.
Why does AI need a different delivery model than software?
Software is relatively stable once shipped. AI keeps evolving as models and best practices change, and those changes can reshape the workflow itself. A partner embedded across many deployments moves those learnings into your system far faster than a team learning it alone.
Do we need to hire our own internal AI engineers?
Many organizations will over time, and the work is too technical and too continuous to leave as a side project. We can be that capability now and also help stand up and coach an internal forward deployed function so the know-how stays with your team.
Related proof - Case studies, articles, and next steps
If this is close to what your team needs, these pages are the best next places to look.
Agentic AI pilot case study
How we frame and ship agent pilots inside regulated, high-accountability environments.
See our delivery process
How embedding, evaluation, and operational handover are built into the way we deliver.
Discuss an embedded engagement
Start with a scoped conversation about the workflow you want to bring agents into.