Forward deployed engineers
Engineers inside your business until AI runs in production
Most AI value is lost between a working demo and daily use: systems, data, permissions and people. Our engineers work as one team with yours, inside your systems, until the solution runs in daily operation, and leave your team able to own it.
We embed engineers with your teams to connect AI to your real systems and processes, build it where it will live and take it to production. The exit is planned from day one: code, evaluations and documentation in your repositories, and a dated handover.
What our engineers do
- Sit with your teams to understand the process, the data and the constraints before writing code.
- Connect models and platforms to your systems: ERP, CRM, document repositories, identity and permissions.
- Write the integration code, workflows and evaluations a generic product does not include.
- Take the solution from pilot to production and stay through the first weeks of real use.
- Build alongside your people, so the know-how stays when we leave.
How an engagement runs
- Discovery & alignment. We frame the decision, map stakeholders and agree what success looks like before scoping anything technical.
- Design & governance. Architecture, controls, decision rights and risk approach travel together, so trade-offs are explicit before build starts.
- Build & validate. Iterations with explicit go/no-go gates: pilots ship with success criteria, fail-fast thresholds and a documented stop.
- Handover & care. Runbooks, training and escalation paths validated by someone outside the build, so continuity is not a single person's memory.
What you keep
- Code and documentation in your repositories from day one.
- Evaluation sets you own, to keep measuring quality after we leave.
- Runbooks and a dated handover your team has rehearsed.
- A named owner on your side who takes the system over.
When it fits, and when it does not
It fits when
- Several pilots worked in a demo but none reached daily use.
- The value depends on connecting AI to your own systems, data and permissions.
- Requirements will only become clear once real users try the system.
A defined project is better when
- The scope is clear and self-contained and the integration is light.
- You need a fixed price and scope for budget or procurement reasons.
- A product already solves the need: then we will say so.
Products that help, when they fit
Pulse keeps AI-assisted software development governed and traceable. Synapse gives you a governed AI layer, with agents, model routing and cost visibility, without building it from scratch. Neither is a condition: we are vendor-agnostic on models and clouds.
Frequently asked questions
Do your engineers work on site?
They work inside your teams and systems. Whether that is on site, remote or a mix depends on your organisation and security requirements; what matters is access to people, data and systems every week.
How long does an engagement last?
As long as it takes to get a first use case into daily operation and your team able to run it, with a dated handover agreed at the start.
Who owns the code?
You do. Code, prompts, evaluation sets and documentation live in your repositories from day one.
Do we have to use Thinkia products?
No. We use them when they fit the case, and we work with the models and clouds you already have.