CloudHires

AI enablement.

We take a domain problem and stand up the first working system — git, cloud, and GenAI wired end to end. You keep the account, the code, and the token bill. Then we hand off into Cursor, Claude, or OpenAI Codex so your team can keep shipping.

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What this means

Not a black-box SaaS. Not a forever staff-aug engagement. A short path from problem to a system you can own and extend.

01

Domain problem

We start from the real workflow — hiring, ops, or another vertical — not a generic chatbot demo.

02

Code + git

Initial application wired in a repo you own, with CI and review habits your team can continue.

03

Cloud pass-through

Deployed into your cloud. You cover infrastructure; you see every resource and bill in your account.

04

GenAI on your meter

Model and token cost stay on your keys and your invoice — full visibility, no hidden markup layer.

05

Agent handoff

We leave you in Cursor, Claude, or OpenAI Codex — ready to iterate the product without starting over.

Software engineering

Built to disrupt how software gets made

Classic delivery assumed humans typed most of the code, vendors kept the keys, and change slowed when the build team left. AI enablement flips that model.

What breaks

Headcount as the only throttle — large teams spent months on scaffolding that agents now draft in days
Knowledge walk-out — when consultants leave, the product freezes because nobody owns the repo or the cloud
Black-box delivery — you paid for outcomes you could not inspect, redeploy, or extend without calling the same vendor back
License fees that outlive the value — annual increases and seat taxes on software you cannot truly own or exit
Ticket-queue product change — every small feature waited on a sprint capacity you did not control

What replaces it

Direction over keystrokes — domain experts and engineers steer agents; review and judgment become the scarce skill
A living codebase — the first release lands in your git with conventions your team (and Cursor, Claude, or Codex) can keep moving
Cloud and tokens on your books — hyperscaler and model spend is pass-through; you choose the model and you pay that bill — no opaque platform tax
Continuous product loop — backlog items go into an agent session the same week, not the next quarterly change request

The shift

From “hire a team to build software” to “stand up a system your team can evolve”

Software engineering does not disappear — it concentrates. Architecture, data, security, and product taste still need humans. What compresses is the distance from a domain problem to a running vertical slice, and the cost of every change after that.

Fewer people writing boilerplate; more people specifying, reviewing, and shipping
GenAI spend replaces a large share of routine implementation hours — on your meter, with your caps
No ongoing license fee for code you already own — cloud and models are pass-through; annual vendor price hikes have nowhere to hide
Vendor value moves from “we hold your code” to “we get you to a handoff you can run without us”
Organizations that adopt this loop outpace teams still waiting on traditional delivery capacity

Why this matters: The disruption is not a better chatbot. It is a different unit of work — owned systems that keep moving after the first build, because the client controls the repo, the cloud, and the agent toolchain.

You own the stack

Enablement only works if control stays with the client. That is the point of the engagement.

Cloud

Your account, full visibility

Infrastructure runs as a pass-through on the client cloud. You see IAM, spend, logs, and environments the same way your ops team expects — because it is your account.

No CloudHires-owned production tenancy for your core system
Billing and quotas stay under your org
Access and audit trail remain yours

Source

Your repository

The initial codebase lands in git you control. We build the first vertical slice; you keep the history, the branches, and the right to change vendors.

Application source under your org
Deploy and release path you can run without us
No hostage binary or locked admin panel

GenAI

Your token cost

Model usage is metered on your provider account. You decide which models to use, how to cap spend, and when to switch — with the same dashboards your finance team already trusts.

Client-owned API keys and budgets
Transparent token and inference cost
Room to mix providers as the product evolves

Pricing honesty

No ongoing license fees that do not deliver value

Hyperscaler and cloud cost is pass-through. The model is the one you choose — and you pay that provider. The code is yours. There is no vendor lock-in and no annual price hike on software you already own.

Cloud — AWS, GCP, Azure (or similar) billed to you, visible in your console
Models — you pick the GenAI stack; token and API cost stay on your meter
Code — yours in git, so leaving a vendor does not mean rewriting the product
Transparency — spend lines map to real usage, not an opaque seat or “platform” tax

Simple by design: pay for build and enablement when you need it; after that, pay the cloud and the models you actually use. Nothing more complicated than that.

Handoff into the tools your team will use

When the first system is live, we do not leave a slide deck. We leave a working product plus an AI coding environment your engineers can drive.

What we deliver

A running slice of the product on your cloud
Source in your git remote
Docs and conventions for how the system was built
A clear next backlog your team can take into an agent

What you iterate with

Cursor — day-to-day product changes in the IDE
Claude — deep reasoning and large-context edits
OpenAI Codex — agentic coding against the same repo

Pick the tool your team prefers. The handoff is the repo and the cloud account — not a proprietary console.

Why this matters: Most “AI projects” stall when the build team leaves and the client cannot change the system. Enablement flips that — the first release is also the start of your own iteration loop.

Where this fits next to BotHires

BotHires is our product catalog for contract hiring. AI enablement is how we stand up custom domain systems when you need something built for your workflow — and then kept by your team.

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