Outsourcing artificial intelligence (AI) development means hiring an external team to build or maintain AI features for your business. A team in Nepal can do that work, but a successful project needs more than a competitive hourly rate. You need a clear task, a way to test the result, and agreement on who owns and supports the software.

At Codse, we work from Kathmandu with companies in the US and Australia. This guide explains how to assess a partner, compare costs, and organize delivery. It is written for founders and business teams, so you do not need an engineering background to use it.

Evaluate the team and its working process

Location tells you where a team works. It does not establish whether the team can deliver your project. Ask for evidence of relevant experience and meet the people who will do the work.

For an AI project, look for three capabilities:

• Product delivery: The team can connect the feature to your existing software and release it to users.
• Quality testing: The team can measure whether the AI produces useful, accurate results and detect problems after changes.
• Ongoing support: The team can track errors, running costs, and response times after launch.

For example, a support assistant needs more than a convincing answer in a demo. It needs access to the right help articles, limits on customer data access, and a clear way to involve a support agent when it cannot answer.

Plan how you will work across time zones

Agree on meeting times using the actual cities and working hours of both teams. Nepal uses UTC+5:45 throughout the year. Some US and Australian locations change their clocks for daylight saving, which can change your meeting overlap.

These are useful starting points for planning:

When the teams work at different times, written handoffs become especially important. Each handoff should state what changed, what needs review, and which questions are blocking progress. Assign someone on your side to answer those questions.

Do not assume that an overnight handoff gives you round-the-clock support. Agree separately on who responds to urgent incidents and at what times.

Choose a project with a clear result

Start with work you can describe and evaluate. Suitable examples include:

A system that searches documents before generating an answer is often called retrieval-augmented generation (RAG). If a vendor proposes RAG, ask which documents it will search and how the team will check the answers.

Keep ownership of product priorities, sensitive-data decisions, and success criteria within your business. An external team can help shape those decisions, but someone on your side must be accountable for them.

If the problem is still unclear, start with a short discovery project. Its result should be a defined use case, a data-access plan, and an estimate for the next stage. Our AI integration services can help with that preparation.

Compare the full cost of delivery

Use written quotes for the same scope. Broad country-level price ranges cannot tell you what your project will cost, and an hourly rate does not show how much work remains after delivery.

Ask each partner to separate these costs:

Request the currency, taxes, payment schedule, exclusions, and process for approving extra work. If the quote includes AI service usage, ask what usage assumptions it makes.

A cheaper project can cost more overall if your team must redo the work. Compare the cost of reaching an agreed, tested milestone, including your own review time and likely support needs. Ask vendors to explain any claimed savings rather than treating them as guaranteed.

Choose a delivery model that matches the work

There are three common ways to organize an engagement:

A fixed-scope project

Agree on a specific result, a price, and acceptance criteria: the checks the work must pass before you accept it. This works best when the task is understood and you can provide the required data and system access.

For example, the scope might be “extract these five fields from this set of invoice formats.” Agree in advance on how the team will handle additional formats or changed requirements.

A dedicated team

Reserve a small team for ongoing product work. The team learns your software and business over time, which helps when priorities change regularly.

This model needs a steady supply of useful work and an available decision-maker on your side. Confirm the team’s roles, availability, and responsibilities before committing to a monthly fee.

A pilot followed by ongoing work

Start with a limited paid project, then decide whether to continue with the same team. A pilot lets you assess delivery, communication, and handover before making a larger commitment.

Define the decision at the end of the pilot: continue, revise the approach, or stop. Payment for a pilot should buy a useful result even if you choose not to proceed.

Agree on quality before development starts

“Production-ready” should mean ready for the intended users under agreed conditions. Put those conditions in writing.

For a support assistant, acceptance criteria could cover:

1. Which questions the assistant is allowed to answer.
2. Which approved sources it must use.
3. How the team will test answer quality using representative examples.
4. When a person must review or take over a request.
5. What response times and running costs are acceptable.
6. How users and operators will report a problem.

During development, schedule regular demonstrations of working software. Use a staging environment, a separate test version of the system, so your team can review changes before customers receive them.

Ask the partner to document important design choices and automate checks for critical user actions. AI quality tests should run again when the model, instructions, or source documents change.

Define access, ownership, and support

The contract and technical setup should make responsibilities clear. Agree on the following before granting access:

• Access: Give each person only the permissions needed for their work. Keep development, testing, and live customer systems separate.
• Credentials: Use individual accounts and managed secrets instead of sharing passwords or service keys in messages.
• Records: Log enough information to investigate actions and errors, with access controls and limits on sensitive data in logs.
• Data handling: Specify where data may be stored, who may process it, and when it must be deleted.
• Ownership: Define intellectual property (IP) rights for source code, prompts, configuration, and other project assets.
• Handover: Keep company-controlled access to the code repository and infrastructure. Require operating instructions and an exit process.
• Support: Name the people responsible for incidents, response times, and fixes after launch.

These agreements should cover the AI providers and other third-party services the project uses, as well as the development partner.

Review data requirements for your market

Start by identifying the information the system will handle: public documents, confidential business records, personal information, or health information. Map where that data travels, including model services and logs. Your privacy or legal lead can then review the requirements that apply to the project.

For US healthcare work covered by the Health Insurance Portability and Accountability Act (HIPAA), cloud providers handling protected health information may require business associate agreements and other safeguards. See the US Department of Health and Human Services guidance on cloud computing. Health-related software does not all have the same obligations.

For Australian organizations subject to the Privacy Act, overseas data handling needs careful review. Australian Privacy Principle 8 addresses cross-border disclosure of personal information; responsibilities depend on the arrangement. The OAIC guide to sending personal information overseas explains the key considerations.

Prepare a diagram of the data flow, a list of service providers, and named incident-response owners. These give reviewers concrete information to assess before development begins.

Questions to ask a potential partner

Use these questions to compare teams:

1. Can you demonstrate a similar feature used by real customers?
2. Who will work on our project, and who reviews their work?
3. How will you test AI output and detect a drop in quality?
4. How will you report running costs and errors?
5. What access will you need, and how will you protect our data?
6. Which meeting and support hours can you commit to?
7. What will we receive at handover, and how can we move to another provider?

Resolve unclear answers about data access or ownership before sharing sensitive information. A small pilot can help assess delivery quality, but it does not replace those agreements.

An example first-month plan

For a narrow pilot with data and approvals ready, the first month might look like this. Treat it as a planning example, not a promise of a production launch:

If the quality checks fail or approvals are incomplete, extend the pilot. Expand only after the first use case provides enough evidence to justify the next investment.

Frequently asked questions