What is a forward deployed engineer?+
A forward deployed engineer (FDE) works inside a customer's own product, code and team to make a specific outcome real in production. The role was made well known by Palantir. At Datics it means our engineers join your repositories and rituals to ship one outcome you agreed with us, then hand it over.
Who is forward deployed engineering for?+
Software companies with a live product and their own engineering team, who know the outcome they need and want it built inside that team rather than handed over from outside. If the outcome is not clear yet, the Planning Sprint is the better start.
How is this different from staff augmentation or hiring contractors?+
With staff augmentation you rent people and direct their hours. A forward deployed engagement is priced and planned around one outcome: we agree what ships and what done means, own the delivery with your team, and finish when it is live and handed over, not when a contract runs out.
Why not just hire our own AI engineer?+
For work that never ends, you should. For one outcome you need now, a hire takes time to find and still has to learn production AI on your product. Our engineers start within a week, have shipped AI in production before, and leave your team able to run what they built.
What does a forward deployed engagement cost?+
It starts with the Outcome Sprint: one week, $2,500, with our engineer working inside your team to define the outcome. You then get a fixed quote for that outcome, and the sprint fee counts toward it. There is no monthly rate per engineer.
Who are the engineers?+
Senior Datics engineers who have shipped AI in production inside live products, in stacks such as AWS and Azure, React and React Native, and agent frameworks such as LangGraph. Most engagements start with one dedicated engineer. If the outcome needs more, or you want to move faster, we add engineers, always inside a fixed quote. They are backed by the wider Datics team on architecture and applied AI.
How soon can an engineer start?+
Within a week of booking the Outcome Sprint.
Can we meet the engineer before we start?+
Yes. You meet the engineer who will join your team before the Outcome Sprint starts, and you can ask them anything about how they would approach your outcome.
What if an engineer is not the right fit for our team?+
We replace them. The outcome and the fixed quote stay the same, and the work already done stays in your repositories.
What if the work takes longer than planned?+
Within the agreed scope, that is on us: the fixed quote does not change. If you add to the scope, we agree the change and its price with you before any work on it starts.
Can we stop after the Outcome Sprint?+
Yes. The outcome brief, with what ships, what done means and how it is tested, is yours to keep whether or not you continue with us.
Who owns the code and the work?+
You do. The work lives in your repositories and cloud accounts from the first day, and everything we build is yours when the outcome is handed over.
How do you keep our code and data secure?+
Our engineers work only inside your accounts, with the access you grant and can revoke at any time. Every change goes through your own code review, the work stays in your repositories and cloud from day one, and we work under your NDA, or a business associate agreement where health data is involved, as we do for Datascape.
What happens after handover?+
Every outcome comes with a two-week warranty after handover: anything that does not meet the agreed done criteria is fixed at no extra cost. Further work after that is quoted as a new outcome, only if you want it.
Who do we talk to if something is not right?+
Our engineer first, every day. Above every engagement is Umair Majeed, our founder and CEO, based in Austin, Texas, and you can reach him directly.
How do your engineers work with our team?+
Inside your tools: your repositories, tickets, reviews and planning. They demo every two-week sprint to your team and make product and architecture calls together with your people, so your team knows the work well enough to run it after handover.
What kinds of outcomes fit this model?+
Outcomes inside a live product that need engineers close to the code and the team: an AI feature or agent, a data foundation the AI depends on, a module rebuilt without stopping the product, or an integration with systems you already run.