Forward-deployed engineers embed with your team, learn how your business actually works, and ship AI applications to production — with evals, security gates, and named senior ownership. Then we operate and evolve them, whether we built them or your team did.
20 minutes, no deck, no pitch. If an agent isn't the right answer for your workflow, we'll tell you that — in writing.
What we do
Most clients start with a build and stay for the run. You can also join at either end — bring us a workflow and we'll build it, or bring us something already live and we'll take it on.
Forward-deployed engineers embed with your team and ship a new enterprise AI application end to end — embedded discovery, production build on your real data, an eval harness wired into CI, and security hardening before real users arrive.
Embed · Discover · Build · Prove · Harden · Transfer
See how we buildOnce an application is live it needs owners. We assess, harden, operate under SLA, and keep evolving it — for the applications we built, and for the ones your team shipped without us.
Audit · Rescue · Run · Evolve
See managed supportIs this you?
The use case is clear, the appetite is there, and the hiring plan is nine months behind it. Whichever seat you're in — CIO, CTO, Head of Operations, or Chief Risk — find the situation that sounds like yours. That's where a forward-deployed pod starts. Already have an app in production instead? That's the managed-support half of the offer.
“We know what to build. Hiring the team to build it takes three quarters.”
A forward-deployed pod embeds in your repos and ships the application — while your team learns the patterns alongside them.
“Six people spend most of their week on work a system should handle.”
We sit with the team doing the work, learn how it actually runs, and build the application around the real process — not the documented one.
“It demos beautifully. It has no evals, no audit trail, and no guardrails.”
We rebuild it to a production bar: eval harness, tool risk ratings, human checkpoints on irreversible actions, and OWASP LLM coverage.
“Every function has an experiment. None of them survived contact with real data.”
We pick the one workflow worth industrialising, build it properly, and hand your team the pattern for the rest.
Recognise your situation? We'll scope it with you and tell you honestly whether it's worth building.
Book a 20-min call →How we deliver
A product engineer works on one capability for many customers. A forward-deployed engineer works on many capabilities for one customer — yours. Our engineers work inside your repos, your cloud accounts, and your sprint cadence, because the part of an enterprise workflow that resists a discovery call is exactly the part that's left to build.
You cannot infer from a discovery call how a specific company closes its books. So we don't try — we sit with the people doing the work and learn what your business actually calls things and how those things actually move. Almost none of that is written down.
Engineers join your repos, cloud accounts, SSO, and ceremonies. Week one is spent learning your systems and the real process — not the documented one.
We find the workflow worth automating and pressure-test whether it needs AI at all. If deterministic software is the better answer, that's the recommendation you get.
Working software on your real data inside a short time box. A single agent with well-scoped tools first; more architecture only where it earns its keep.
A versioned eval set and a calibrated judge, wired into CI so every prompt, model, and retrieval change is scored before it ships.
Layered guardrails, tool risk ratings, human checkpoints on irreversible actions, audit logging, and OWASP LLM Top 10 coverage before real users arrive.
Source code in your repo with named commit attribution, documented architecture, and your engineers coached to own it. The exit is part of the engagement.
Where enterprise AI stalls
2×
AI pilots built with an external partner were twice as likely to reach full deployment as those built in-house — and employee usage of the resulting tools was nearly double. Two thirds of organisations now partner rather than build alone.
Source: MIT NANDA, The GenAI Divide: State of AI in Business, 2025
The same research names the reason pilots stall, and it isn't infrastructure, regulation, or talent — it's that most systems never learn. They don't retain feedback, adapt to context, or improve once they're live. We build the opposite: applications with an eval harness from the first sprint, so you can prove the thing works today and know the moment it stops.
What production-grade means
Evals are the deliverable that separates an enterprise AI application from a science project. Here is what we commit to, in terms your own engineers and risk team can check.
01
A versioned golden set built from real traces, a rubric-scored judge, and a gate on every change to prompts, models, retrieval, or tools. The gate asserts per-case regressions — not just an average, because averages hide the expensive failures.
02
We report how closely the automated judge agrees with your experts, using a chance-adjusted score, and re-calibrate as the application changes. The accuracy target is set by how often your own experts agree with each other — we won't promise a number above that ceiling.
03
Single agent with tools first. Multi-agent architectures cost roughly fifteen times the tokens of a chat interaction, so we use them only where the work genuinely parallelises and the value justifies it. Sometimes the honest answer is that you don't need an agent.
04
Guardrails layered rather than single-point, tools rated by risk, human approval on irreversible or financial actions, and immutable logging of agent actions and approvals. Prompt injection is treated as a design constraint, not a patch.
Source: Multi-agent token comparison: Anthropic, How we built our multi-agent research system.
Proof
50%
of escalations resolved at Tier 1
1 week
from kickoff to a prioritized risk register
92–95%
event-detection accuracy
65–70%
faster launch vs. traditional build
Representative results from individual engagements.
Why teams trust us
Every engagement is led by named senior engineers who own the architecture, security gates, and merge review — paired with sharp, AI-native engineers who build to the same standard. You get production-grade delivery from people who are accountable for it, working inside your business rather than reporting on it.
01
Senior tech and execution leaders stay on the engagement end to end — architecture, security gates, merge review, and QA. Nothing ships without accountable senior sign-off.
02
A versioned eval set and the AI Vibe Test Framework run alongside development, not as a post-hoc audit — so correctness is measured on every change, and proven rather than assumed.
03
Auth gaps, data handling, and compliance risks are found and fixed as part of delivery — not bolted on after a breach or a failed audit.
04
We build so your team can own it — documented architecture, a clean handover, and knowledge transfer. No lock-in.
Senior-led, eval-driven, and built to hand over — production-grade quality at a lower fully-loaded cost than onshore hiring, with no corners cut on security or review.
Capability transfer
A forward-deployed team that can't leave isn't a partner, it's a dependency. Every engagement pairs delivery with structured shadowing, pairing, and coaching, so your engineers can run what we built. Engineering runs through Viteus with APAC-friendly timezones for Australia, Singapore, and the region, plus workable overlap for the UK, Europe, and US — on your tools and in your repos.
Our engineering bench
Vietnam is one of the world's strongest and fastest-growing engineering markets. Our developers are sharp, AI-native, and work under named senior oversight — with APAC-friendly hours for Australia, Singapore, and the region, plus workable overlap for the UK, Europe, and US.
Top 20
globally for software-engineering talent
~550,000
professional IT engineers in the market
50–75k
new tech graduates every year
~2.3%
IT unemployment — a competitive, in-demand pool
Source: Second Talent / CMC Global / InCorp, 2025.