Why do enterprise AI programs need forward deployed engineers?
Enterprise AI programs tend to stall in the first months of production, when data shifts, when an agent hits an edge case nobody modeled or when the team that built the pilot has moved on. QKTech’s AI forward deployed engineers start where your program starts, by engineering the agent itself with your team. Reliability is part of how we build it, shaping the context the agent works from, the harness that governs what it can touch, the evaluations it must pass before release and the observability that shows how it behaves once live. The same engineers stay through the first months in production and bring our own agents into your quality and operations work.
Proven in production
Results measured against each client’s own baseline
Each figure comes from a published QKTech engagement.
99%
Faster cost-analysis data processing for an automotive manufacturer, from 12 to 15 days down to about 10 minutes
50%+
Reduction in development effort through AI handling code reviews, documentation, root cause analysis, and bug fixes, increasing team output
150+
Applications migrated to the cloud for an automotive leader, with 30% lower costs and 2x faster development
93%
UI automation for UPI Android app with 432 automated test cases
Agent engineering first, with reliability built in
Most enterprises come to us with an agent they want to build, or one built quickly for a pilot that now must run in front of customers. Agent engineering is where every engagement starts, and two kinds of reliability work come with it.
Agent engineering
We design and build agents and agentic workflows for your use cases, applying context, harness and loop engineering, and connect them to the data and systems they depend on.
Reliability of AI
Every agent we build ships with evaluation suites, observability, guardrails and release discipline, applied before go-live and maintained afterwards. Release checks follow our GATES framework.
AI for reliability
We embed our own agents into your quality and operations work, covering test design, automation execution, defect triage, test data, monitoring and incident response, with agents our engineers own and tune.
AI-native platforms that scale reliability
Platforms that make automation gains repeatable
Our engineers bring techniqk, our own platform suite, into every automation program alongside the RPA and cloud platforms you already license. Reusable assets shorten setup, and the same dashboards report results from the first automated process to the last.
Teqk
Multi-cloud orchestration with self-service provisioning, compliance automation and cost visibility for every cloud account.
qRace
AI-led test generation and self-healing scripts that confirm each automated workflow still works after every change.
Nimbus AI
Scores agents and bots across 60+ dimensions, including accuracy and safety, before they take on work unsupervised.
Partners
Technology partners
We automate on the platforms our clients already run, which keeps licensing where it is and speeds up the first release.
Helps improve AI agents with continuous evaluation, observability and tracing, learning from real production signals.
Named International Agentic Testing Pioneer in the UiPath 2026 Partner Awards, recognizing our agent-led testing and automation work on the UiPath platform.
FAQs
Common questions about AI-FDE
Who is an AI forward deployed engineer?
An engineer who builds and runs AI agents from inside your team, staying through the first months in production until your own people can operate the system.
Do you build the agents, or only make ours reliable?
Both, and building is where most engagements start. Our engineers build the agent with your team and engineer reliability in as part of the build. If you’ve already built it, we make what exists reliable to run in front of customers.
Where does reliability come in when you are building the agent?
Reliability comes in with the first design decision. The context an agent works from, the tools it can call and the checks it must pass are all set during the build, and those choices shape its behavior in production more than any testing added at the end.
Is this staff augmentation?
No. Every engineer we place works to the same reliability brief and has QKTech’s platforms and review process behind them.
How is this different from AI consulting?
A consulting engagement usually ends with a recommendation. An FDE engagement ends when the system runs reliably in production and your team can operate it without us.
Tell us which process costs you most
Share the operation that runs on manual steps, or the automation you have deployed but cannot yet prove is working. A practice lead will come back with where it fits, the baseline we would measure and what a first phase would return, in the figures your finance team already tracks.