An engineering company, not an AI agency.

What you can hire AIVETech to do

Build one agreed AI feature, agent or automation into an existing product, or implement the first workflow for a new one. The AI Feature Implementation Sprint defines deliverables, acceptance criteria, integrations and handover before implementation.

How we work

Kartik leads scoping, architecture and implementation. Price, milestones, dependencies and exclusions are agreed before build work. Scope changes are recorded and agreed before additional work starts. Deployment requires client approval; ongoing support is defined separately.

Founder-built evidence

Kartik Malhotra is the founder of AIVETech and builder of Soil, a SaaS product for tracking customer commitments through delivery. Explore the work for its implemented capabilities and limitations. Soil is software built and run in-house at AIVETech. Neither is presented as paid client work.

One engineer, named before you sign

AIVETech is Kartik Malhotra. There is no bench, no account manager and no team you have not met. The person who scopes the work is the person who writes the code and the person you talk to when something breaks. That bounds what can be taken on at once, which is why availability is confirmed before a proposal rather than after, and it is the reason the scope of an engagement is one defined feature rather than a whole product.

Where a project needs a specialist, that is stated in the proposal before work starts rather than resourced quietly. Nothing is subcontracted without being agreed first.

How a project is scoped and priced

Every engagement begins with a fixed-price discovery sprint at USD 2,500 over two weeks. It is separate work with its own deliverables, and the fee is deducted from the build if you continue. A build quoted before anyone has read the specification, the data and the existing code has to be renegotiated later, so the architecture is settled first and the price follows from it.

The sprint produces documents you keep whether or not you commission the build: a review of your specification and data, the architecture and technology choices, initial screen designs for your approval, acceptance criteria, a running-cost model and a fixed price for the build. The cost model matters as much as the build price, because inference, speech services and hosting are charged per use. It is built from your expected volume and the selected providers, so the cost of running the feature is estimated before the build is committed rather than discovered afterwards.

Who owns the code and the accounts

You own all code and work product on payment. Cloud, repository and AI provider accounts are created in your own name at the start of the project rather than transferred at the end, so there is no migration step, no shared credential to untangle, and nothing of yours sitting in an account controlled by a supplier. If an engagement ends early, the work that has been paid for is already in infrastructure you control.

Handover covers source, configuration, operating documentation and the evaluation results for the agreed cases, including the failure paths: what the feature does when a provider times out, when a downstream system rejects a write, and when a request falls outside the supported set. Defect handling, the warranty period, monitoring and support hours are stated in the proposal; there is no implied ongoing support subscription.

What you supply

A decision maker who can approve scope and designs, representative data you have permission to use, access to the relevant code and test systems, integration credentials through the agreed secure channel, and feedback on increments within the agreed review cadence. Hosting and model provider charges are billed to your accounts directly. Where production data cannot be shared, the work proceeds against test data and that limitation is recorded as a risk rather than ignored.

What is deliberately out of scope

Unless the proposal names them: an agent with open-ended authority over your systems, a rebuild of an existing product, integrations not listed in the scope, data cleaning at unknown scale, compliance certification, and any guarantee of model accuracy. Data protection is scoped as an engineering matter, covering where data is processed and how long it is retained; legal advice is not included.

Discovery can also conclude that the feature should be narrower than proposed, or that rules and ordinary software fit the problem better than a model does. That is a legitimate outcome of a sprint, not a failed one.

How to check us before you pay

AIVETech was founded in 2026 and has no case studies for work delivered under the company name, and no client has yet agreed to act as a named reference for an AIVETech engagement. That is a real gap, stated here rather than left for you to find. A buyer doing due diligence should weigh it.

What exists instead is a public record of paid work. Kartik has completed three client engagements through Upwork under his own freelancer account, all rated 5.0, at a 100 percent job success score. The most recent, in October 2026, was AI engineering work, and the client wrote: “Kartik is an excellent AI engineer. Delivered a high-quality solution involving AI agents, LLMs, RAG, Python, FastAPI, and API integrations.” Those were small fixed-price engagements rather than the size of build described on this site, and they were not delivered under the AIVETech name. A separate verified testimonial from a founder describes multi-year ownership of a mobile fintech product, including taking Google OAuth and CASA Tier 2 security verification through to approval.

The rest of this section is how an engagement is structured so that the remaining gap costs you less.

Start small, by design. The first commitment is the discovery sprint: USD 2,500, two weeks, fixed. It ends with architecture, screen designs, acceptance criteria, a running-cost model and a fixed price for the build, and those documents are yours whether or not you continue. There is no large advance to place before anything is delivered, and the sprint fee comes off the build if you go ahead.

Payment follows acceptance. The build is quoted at a fixed price with milestones, and payment is tied to accepted deliverables rather than elapsed time. Acceptance criteria are written before implementation starts, so what counts as done is agreed in advance by both sides rather than argued afterwards.

You hold the assets throughout. You own all code and work product on payment. Cloud, repository and AI provider accounts are created in your own name at the start, not transferred at the end, so the work sits in infrastructure you control from day one. If an engagement stops early, what you paid for is already yours and already in your accounts.

The person is identifiable. AIVETech is one engineer, Kartik Malhotra, who previously led AI at TheraNow and worked as an engineer at KredMe. The founder page carries his professional profiles. You deal with him directly at every stage; there is no account manager between you and the person writing the code.

There is work you can inspect now. Soil is a founder-built product with a public demo, and Front Desk is a demo agent whose latency work is written up with the method, the hypothesis that turned out wrong, and the measured result. Neither is client work and both say so. The engineering notes show the reasoning in more depth. Ask for a call and the code can be walked through live.

What to ask for. A written proposal naming deliverables, price, milestones and exclusions before any payment. A walkthrough of the architecture for your specific problem. The running-cost model for your expected volume. If any of that is missing, do not pay.

A note on the name

Aivetech (aivetech.in) is an independent AI engineering company and is not affiliated with similarly named companies such as Aivatech or Aivantech. If you're looking for the AI engineering firm founded by Kartik Malhotra, you're in the right place.