How is this different from contractors or staff augmentation?

A contractor fills a seat and takes direction. Our engineers own the build with your team, from the first change through to production, and the engagement ends on a handover date agreed at the start. You keep the code, the decision log and staff who know how it works.

Do the engineers need access to production?

They need access to the systems the build touches, granted through your own access controls and reviewed by your security team. Most of the work happens in development and staging, with read-only access where that is enough. Production access covers deployment and the stabilisation period, and every action is logged under your existing controls.

Do you bring your own tools?

We work in the stack you already run. Where a piece is missing, we use our default tools, such as LangGraph for agents, Docker and Kubernetes for deployment and GitHub Actions for releases. Everything we add sits in your repositories and your cloud account.

What happens when the engagement ends?

Your staff own the system. Before we leave, the people who will run it deploy a change, run the evaluation set and handle a support call with our engineers beside them. We can stay on as an advisory retainer for a second opinion on later changes.

Where do the engineers work?

We work on site with your team in the cities we cover. We work over calls with clients in the rest of Australia. In both cases the engineers work inside your repositories, your cloud account and your ticket queue.

Forward deployed AI engineers

Your team carries the work by handover

Our engineers carry the build early. Your staff take more of it at each step.

01Discovery02Build03Run together04Handover

Zinto engineersYour team

You leave with the build live and your staff running it

01
The build live in your production environment
02
Your staff running and changing it
03
Code, decisions and runbooks in your own systems

What it is

We are not the advisory firm that leaves you with an expensive PowerPoint. We sit with your business, get our hands dirty and implement the change. A forward deployed engineer is an engineer who works inside your team and your systems rather than from our office. This engagement puts senior AI engineers in that seat for a fixed term. They build alongside your staff and hand the work over before they leave.

Members of the team worked at Goldman Sachs, Deutsche Bank, EY, Deloitte and Uber before Zinto.

The handover problem

Many AI builds are delivered from outside. The build works in the demo, and then it meets production: the access request, the data that arrives in a different format, the security review, the first incident. By then the vendor is gone, and your team inherits a system it did not build and cannot change.

Our engineers stay through that part. They work inside your environment from the start. The build meets your access controls, your data and your release process while it is still cheap to change.

How it runs

The engagement starts with discovery. The engineers sit with the people who do the work and learn the systems it runs through, and the build plan comes out of that. They join your standups and planning, take tickets from your queue and put every change through your code review.

They build against real data in your development and staging environments. Each service runs in a Docker container. Terraform defines the infrastructure, and Kubernetes runs it in your cloud account or on your own servers. Where the build needs a model inside your environment, vLLM serves it as described under on-prem AI deployment.

Every change goes through your CI pipeline, or through GitHub Actions where you have none. The pipeline runs unit tests in pytest. It also runs an evaluation harness, a test program that scores the build against a set of real cases with agreed answers. A change that scores below the current version does not go to production. Each release keeps the previous version ready for a rollback.

After go live the engineers stay for a stabilisation period. They watch traces and error rates under real load, fix what breaks and move review points where the error rate says to.

What they build

The work is the same as our other build services, done from inside your team:

Agents and workflow automation, with LangGraph for agents that run several steps with review points
LLM integration and RAG over your documents, with LlamaIndex for ingestion and retrieval
Data engineering for the layer those builds read from
Model fine-tuning where a narrow task needs it

Security and access

The engineers work under your security policies. They use accounts that your IT team issues, on devices that meet your rules. Access is scoped to what the build touches, and every change goes through your own approval steps. Their access ends on the handover date. Your team removes it the same way it removes access for any departing staff member.

Who it suits

It suits a team that knows what it wants to build and has the domain knowledge, but lacks the engineers to ship it. One example is an insurer with a claims triage build approved and no AI engineers on staff. Another is a manufacturer whose IT team runs the ERP and has no time for a new system. It also suits a bank or a wealth manager whose security rules keep outside vendors from running systems on their behalf.

It does not suit a business that does not yet know what to build. That starts with an AI strategy engagement.

Handover

The handover date is set at the start. Before it, the staff who will own the system pair with our engineers on real changes. Before the handover date they deploy a change, run the evaluation set and handle a support call on their own. The code, the decision log, the architecture notes and the runbooks live in your systems.

forward-deployed-ai-engineers.stack.yaml

# Tools on this service. Same stack across every Zinto build.
service: Forward deployed engineers
runs_on: your servers, on premises
stack:
  - LangGraph
  - LlamaIndex
  - vLLM
  - Docker
  - Kubernetes
  - Terraform
  - GitHub Actions
  - pytest

Other services

All seven services →

U1Strategy and readiness auditA ranked list of what to build, what to buy and what to leave, checked against your data, systems and security rules U2On-prem AI deploymentCustom GPU servers in your building or data centre, running open-weight models on bare metal, with every request logged in your environment U4Agents and workflow automationAgents that read, check and post work inside your systems of record, with a person where an error costs money U5Data engineeringThe data layer your agents and reports read from U6LLM integration and RAGAnswers from your own documents, each with its source U7Fine-tuningAn open model trained on your data for one narrow task