Question 1

What is a forward-deployed engineer, and how is that different from a contractor?

Accepted Answer

A product engineer works on one capability for many customers. A forward-deployed engineer works on many capabilities for one customer — yours. They work inside your repos, cloud accounts, SSO, and sprint ceremonies rather than taking a specification away and returning with software. The difference matters because the part of an enterprise workflow that's still worth building is the part you can't learn from a discovery call. How your team actually closes the month, what your systems actually call things, which spreadsheet everyone quietly depends on — almost none of that is written down. Our first week is spent learning it, not scoping it.

Question 2

How is this different from hiring a consultancy or a systems integrator?

Accepted Answer

A consultancy typically delivers analysis, a recommendation, and a plan for someone else to implement. We deliver running software in your repository, built by named engineers whose commits you can read. The other difference is scale and accountability. You get a named Viteus Delivery Lead and named senior engineers who stay on the engagement — not a pyramid where the people who sold the work hand it to people you've never met.

Question 3

What stops this becoming another demo that never reaches production?

Accepted Answer

An eval harness, built alongside the application rather than bolted on afterwards. We build a versioned set of real test cases, score outputs against an explicit rubric, and wire that into CI so every change to a prompt, model, retrieval step, or tool is scored before it ships. The gate checks per-case regressions, not just an average, because averages hide the failures that cost you money. We also report how closely the automated judge agrees with your own experts, and re-check it as the application changes. The accuracy target is set by how often your experts agree with each other — we won't promise a number above that ceiling, because nobody can hit it.

Question 4

What if the workflow doesn't actually need AI?

Accepted Answer

Then we'll tell you, and we'd rather tell you in week one than after a quarter of spend. Good candidates involve genuine judgment, rules too intricate to maintain as code, or heavy reliance on unstructured data. If your problem is a well-understood process with clear rules, deterministic software is cheaper to build, cheaper to run, and easier to audit. The same restraint applies to architecture. We start with a single agent and well-scoped tools, and only add orchestration where the work genuinely parallelises — multi-agent systems cost roughly fifteen times the tokens of a chat interaction, so they need to earn that.

Question 5

Who owns the code, and what happens when the engagement ends?

Accepted Answer

You own it. Code lands in your repository as we write it, with commit history attributed to named engineers — there's no black box and no handover archaeology at the end. Transfer is part of the engagement rather than an afterthought: documented architecture and decision records, a runbook, and coaching for the engineers who'll own it. After launch you can take it in-house, move to Run for SLA-backed operations, or keep the same pod on an Evolve cadence. That's your call, not a contractual trap.

Question 6

How do you get an AI application through our security and compliance review?

Accepted Answer

By treating the review as a design constraint from sprint zero rather than a gate to survive at the end. That means layered guardrails rather than a single filter, tools rated by risk with human approval required on irreversible or financial actions, immutable logging of agent actions and approvals, and coverage of the OWASP Top 10 for LLM Applications — including system prompt leakage and vector/embedding weaknesses, which most vendors skip. We'll also be straight about the limits. Prompt injection has no known fool-proof defence, which is why least privilege, segregation of untrusted content, and human checkpoints matter more than claiming the problem is solved.

Question 7

How quickly will we see something working?

Accepted Answer

Sprint zero sets up repos, environments, the eval baseline, and a CI skeleton, and we aim to have a thin vertical slice running on your real data early rather than demoing against mock data. From there delivery runs on a two-week sprint heartbeat inside your ceremonies. We'd rather set that expectation by scope than by slogan. A single workflow reaching production in weeks is realistic; enterprise-wide transformation on that timeline is not, and research on enterprise AI adoption consistently shows large organisations taking considerably longer than mid-market ones to get from pilot to full implementation.

Question 8

Why wouldn't I just hire engineers to fix this instead of bringing in an outside team?

Accepted Answer

Hiring takes 3–6 months and runs $150K–$200K per engineer per year — and that's before they've learned your codebase. AI-generated code has its own failure patterns that most traditional engineers haven't worked with before. For a full hire-vs-partner comparison, see How we engage or contact us for a scoping call. In short: a viteus.ai team is live within two weeks, specialized in AI-built code, with no recruitment fee and 30 days notice to scale up or down.

Question 9

Won't bringing in an outside team slow my roadmap down while they get up to speed?

Accepted Answer

Most outside teams would, but AI-generated codebases are all we work with, so there's no "getting up to speed" phase the way there would be with a generalist agency or a new hire. We work in your tools, your repos, and your standups from day one, on two-week sprint cycles with full visibility throughout. The more common experience is the opposite: founders who've been firefighting bugs every night find that having a dedicated team handling that layer frees them up to actually move the roadmap forward instead of being stuck in the weeds.

Question 10

How is this different from just handing it to our internal IT team to productionize?

Accepted Answer

Internal IT teams are typically backlogged 6–18 months and weren't built to work with AI-generated code — their instinct is usually to rebuild from scratch, which means months of delay and $50K–$500K in rebuild costs before anything is production-ready. viteus.ai works with what already exists. We stabilize, refactor, and harden the app you have rather than replacing it, which means your prototype gets to production in weeks, not quarters, without waiting in an internal queue or losing the momentum that got the board excited in the first place.

Question 11

What if something breaks again after you fix it, are we just dependent on you forever?

Accepted Answer

That's exactly the dynamic we're designed to avoid. Every engagement includes capability transfer: we document the architecture, set up proper monitoring and observability, and build the kind of test coverage and CI/CD pipelines that mean your team can see problems before users do, not after. If you want ongoing support, we offer SLA-backed Run retainers starting at $3,000/month. If you'd rather hand it back to your team once it's stable, we hand it back clean with a full health report, runbook, and no lock-in beyond 30 days notice. The goal is that you own the engine, not us.

Question 12

Why viteus.ai over a traditional dev agency or MSP?

Accepted Answer

Traditional dev agencies want to rebuild from scratch — it's how they bill. Traditional MSPs don't understand AI-generated code; they were built for legacy enterprise systems. Neither was designed for the specific problem of a vibe-coded app that works but isn't production-ready. viteus.ai sits in the middle: we fix and evolve what you already have, using engineers who work with Cursor, Claude Code, and Copilot daily. We understand how AI-generated code fails, where the security gaps typically live, and what it takes to make it scale.

Question 13

How do I know your team will actually understand my app? It was built with AI tools, most engineers don't know how to work with that kind of code.

Accepted Answer

You're right that most don't, that's why we built a team specifically for this. Our engineers use Cursor, Claude Code, and Copilot as their default tools. They understand the patterns AI code produces, the ways it tends to fail at scale, and how to refactor it without breaking what already works. AI-generated codebases have specific failure modes — excessive I/O operations, missing error boundaries, security gaps from skipped validation — that generalist engineers often misdiagnose. We've seen enough of them to know exactly where to look first.

Question 14

This feels expensive for something my team already half-built. Can't we just patch it ourselves?

Accepted Answer

Patching works until it doesn't. When it stops working, it usually stops at the worst possible moment: month-end close, a major customer demo, a scaling push. The average cost to rebuild a vibe-coded app from scratch runs $50K–$500K, and that's before the cost of downtime, lost trust, or the engineering time spent firefighting instead of building. An Audit starts at $2,500 and gives you a clear picture of exactly what's at risk and what it would actually take to fix it, so you're making the patch-vs-fix decision with real information rather than guessing. Most clients find the Audit pays for itself in the first avoided incident.

Question 15

What stops this from becoming another vendor I'm permanently dependent on?

Accepted Answer

Nothing about our model requires permanent engagement. One-shot services like Audit and Rescue are fixed scope with a defined end point — you get the deliverable, you own it, and you're done if that's what you want. For ongoing Run or Build & Evolve retainers, the contract is monthly with 30 days notice to scale up, down, or out. No long-term lock-in, no recruitment fees, no penalty for leaving. More practically: every engagement is built around transfer, not dependency. We set up your monitoring, document your architecture, and build the test coverage your team needs to run things independently. The measure of a good engagement is that you need us less over time, not more.

Question 16

Can't I just feed the broken code back into ChatGPT or Cursor and let it fix itself?

Accepted Answer

For small, well-scoped bugs — sometimes yes. But the problems that show up at Stage 3 of a vibe-coded app's lifecycle (load issues, auth failures, data corruption, security gaps) aren't single-file fixes. They're architectural: the way the app handles state, the way it manages I/O, the way it authenticates users across edge cases. AI tools are good at generating code in isolation; they don't have context over the whole system, and they don't know what they don't know about production behavior. That's the gap we fill — engineers who can read the whole codebase, understand how the pieces interact under real load, and make fixes that hold rather than fixes that work in a demo.

Question 17

We need this fixed now, how long does this actually take before we see results?

Accepted Answer

Depends on where you're starting. An Audit (diagnosing what's wrong) takes 2–5 business days for most apps. A Rescue (actually fixing it) ranges from 3–5 days for a targeted spot fix to 2–3 weeks for a full stabilization. A Run retainer can be live within two weeks of first contact. The full process from first call to live support typically runs about two weeks: Discovery in week 1–2, Technical Assessment in weeks 2–5, and Execution starting from there. If there's a specific incident already happening, we can scope an emergency Rescue faster than the standard process.

Question 18

Who owns the code after you fix it? Do we get full access to everything?

Accepted Answer

You own everything, always. We work in your repos, not ours. All code, documentation, architecture decisions, and test suites are in your tools from day one and stay there when we're done. There's no proprietary layer, no viteus.ai-specific infrastructure you'd need to maintain, and no handoff process to worry about because we never took ownership in the first place.

Question 19

Your team is Vietnam-based, how does that affect communication, timezone, and quality?

Accepted Answer

Vietnam puts our engineers in the same timezone band as APAC clients (Singapore, Australia, Japan) and within a reasonable overlap window for US and EU teams. Communication runs through your tools (Slack, Teams, Jira, whatever you already use) with weekly steercos, sprint standups, and a Viteus Delivery Lead as your single point of contact. On quality: our engineers aren't new to AI tools — Cursor, Claude Code, and Copilot are their daily defaults, not tools they're learning on your project. Vietnam-based delivery also means 30–50% cost savings compared to US, EU, or Singapore rates, which is part of how we can offer a full cross-functional team at a retainer price most companies would expect to pay for one mid-level hire.

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