AI Integration: Add AI to Your Existing App or Website

Description

Service Add AI to your existing software or website

WavX adds AI features to software you already run: a drafting or summarising button, search and answers over your own content, or AI that reads and updates live records. Each is built as a small backend service that calls OpenAI or Anthropic models, checks the result in code, and leaves decisions that matter with your staff.

Talk to a technology expert Last updated 2 October 2026

What this service is

This is the smaller commitment: one AI feature added to a web app, mobile app, website or internal system you already run, not a new product. WavX connects your software to a model through the OpenAI API or the Anthropic Claude API, and builds the checks around it.

Almost every request fits one of three patterns. They differ in what data the feature needs, and that decides both the effort and the running cost.

Three integration patterns

The effort column gives planning ranges from the cost model behind the AI cost calculator , using the "AI feature in an app" base of ₹2,50,000 and the model's data factor. The range runs from 15% below the subtotal to 25% above it. These are not quotes.

Pattern

What the user sees

Data it needs

Effort: planning range

Running cost

1. Single-call feature

A button: summarise this, draft a reply, tag this enquiry, translate this

Only what is already on the screen

₹2,50,000 × 1.0: ₹2,12,500 to ₹3,12,500, 3–6 weeks

One model call per use. Small and easy to predict

2. Search and answers over your content

"Ask the help centre", find a product by describing it

Your documents or catalogue, indexed

₹2,50,000 × 1.25: ₹2,65,625 to ₹3,90,625, 6–10 weeks

A model call per question, plus the index

3. AI connected to live records

Fields filled from an uploaded invoice, a booking made from a message, a CRM record updated

Read and write access to your systems through their APIs

₹2,50,000 × 1.5: ₹3,18,750 to ₹4,68,750, 6–10 weeks

Several model calls per task, plus staff time for review

Pattern 1: a single-call feature

The model is given a piece of text or an image and a precise instruction, and returns a result the user can accept, edit or discard. It suits support desks (summarise a long ticket thread), sales teams (draft a follow-up from the CRM notes) and operations (sort incoming enquiries by type and urgency). It is the cheapest pattern and the right first project for most businesses.

Pattern 2: search and answers over your content

The feature first finds the relevant passages in your own material and then asks the model to answer from them. This is retrieval-augmented generation, and the method, testing and costs are on the RAG development page.

Pattern 3: AI connected to live records

The model's output becomes data in your system, or an action. An invoice PDF becomes a draft entry in accounts. A customer's message becomes a booking. This pattern needs the strictest checks, because a wrong output is no longer a sentence on a screen. It is a wrong record.

When the feature is part of a new build instead of an addition, the app and software calculators carry AI features as a ₹1,50,000 line item.

How an integration is wired

Whichever pattern you choose, the path is the same. As a diagram it is seven steps from left to right:

User action in a screen you already have.

A new endpoint on your backend assembles the request: the instruction, and only the data the task needs.

The call to the model provider. The API key lives on the server and is never sent to the browser or the mobile app.

A structured response. The model is asked for a fixed set of fields, not free text. OpenAI's documentation describes its Structured Outputs feature as one that "ensures the model will always generate responses that adhere to your supplied JSON Schema".

Checks in code. Does the customer ID exist? Do the line items add up to the total? Is the date in a sensible range? A valid shape does not make the content correct.

Shown as a suggestion, or applied automatically only where you have decided the risk is acceptable.

Logged. Input, output, what the user did with it, and the cost of the call.

Two more things are built in. If the provider is slow or unavailable, the feature fails quietly and the rest of your software carries on. And the model name is a setting, because providers retire model versions and the feature must be retested and moved when they do.

What your existing software needs first

Source code and a way to deploy. If changes to the system are already slow or risky, deal with that first. See adding a feature to existing software.

A backend. The key has to live on a server. A site with no backend needs a small one added.

Data in usable form. Notes typed into one free-text field for years will limit what any model can do with them.

A decision on what may leave your systems. As of October 2026, OpenAI states that data sent to its API "is not used to train or improve OpenAI models" unless you opt in, and that abuse-monitoring logs are kept for up to 30 days by default. Anthropic states that by default it does not use inputs or outputs from its commercial products, including the API, to train its models. Send only the fields the feature needs, and leave out personal data the task does not require.

What it costs to run

Cost

What drives it

How it is kept down

Model usage

Tokens sent and tokens returned, multiplied by uses

Short prompts, the smallest model that passes your tests, caching, batch processing for work that can wait

Index, for pattern 2

Size of the content and how often it changes

Index only what users ask about

Hosting and logs

Traffic and how long logs are kept

Runs on the infrastructure you already have where possible

Staff review, for pattern 3

Share of outputs that need a person

Better checks in code, so only doubtful cases reach the queue

Maintenance

Model versions being retired, changes to your own software

Model name kept as a setting; test set rerun before each switch

A worked example of the first line, using list prices read on Anthropic's pricing page in October 2026. Claude Haiku 4.5 is listed at $1 per million input tokens and $5 per million output tokens. A call that sends 1,500 tokens and receives 300 costs about $0.003, so 10,000 such calls cost about $30 before tax and currency conversion. On Claude Sonnet 5.5, listed at $2 and $10, the same call costs about twice that. Anthropic estimates a token at roughly three-quarters of an English word and notes that the count varies by language. Its Batch API is listed at a 50% discount for work that does not need an immediate answer, such as tagging yesterday's enquiries overnight. OpenAI's pricing page follows the same structure, with separate prices for input, cached input and output. Prices change, so check both pages before budgeting.

What goes wrong

A model is used where a rule would do. If a dropdown or a filter solves it, that is cheaper and never wrong.

Output is written straight to the database. With no checks in code, one bad response becomes a bad record.

The API key is in the front end. Anyone can copy it and spend on your account.

The screen freezes. A model call can take seconds. The interface has to show progress or stream the reply.

Costs creep. Prompts grow, whole histories are resent, a larger model is used for every call.

Users stop checking. A feature that is right most of the time trains people to click accept.

What it should not do without a human check

Anthropic's own guidance says the techniques for reducing wrong outputs do not eliminate them. So a person confirms before the feature:

Sends anything to a customer: an email, a message, a quotation.

Writes extracted figures into accounts, invoices, payroll or stock.

Makes or records a decision about a person, such as hiring, credit or eligibility.

Deletes or overwrites a record.

How it is evaluated

Before the build, WavX and your team assemble a set of real past inputs with the result your staff would accept: past tickets with their correct category, past invoices with the correct fields. Candidate models and prompts are scored against that set, and the cheapest combination that passes is chosen. After launch, the measure that matters is the share of suggestions accepted unchanged, edited, or rejected, which the log from step 7 provides.

When you do not need this

Your existing vendor already sells it. As of October 2026, Zoho CRM includes its Zia assistant, and HubSpot sells a Customer Agent within its Agent Hub. If your work happens inside a product like these, look at its AI features first.

The volume is small. If two people would use the feature a few times a day, a general AI assistant subscription and copy-and-paste is cheaper.

The use case is unclear. "We should have AI" is not a specification. The guide on how to use AI for business helps with choosing a first use, and how to add AI to your app or website covers budgeting, with its own cost figures.

A custom integration is worth it when the feature has to sit inside your own software, work on your own data, and follow your own rules.

AI integration is one part of AI solutions and automation at WavX.

Frequently asked questions

Can you add AI to software that another team built?

Yes, if we can get the source code and a way to deploy changes. The first step is reading the code to find where the feature attaches and where the API key can be kept safely. If the software itself is unstable, we will say that it needs repair before a new feature goes on top.

How much does it cost to add AI to an app or website?

Our cost model gives planning ranges of ₹2,12,500 to ₹3,12,500 for a feature that needs no custom data, ₹2,65,625 to ₹3,90,625 for one that works on your documents, and ₹3,18,750 to ₹4,68,750 for one connected to live systems. These come from the AI cost calculator and are not quotes.

Should we use OpenAI or Claude?

We build on both. The choice is made by running a set of your real inputs through the candidate models and comparing the results, the speed and the cost per call. The feature is written so that the model name is a setting, which lets you switch when prices or models change.

Is our data used to train the model?

As of October 2026, OpenAI states that data sent to its API is not used to train or improve its models unless you opt in, and Anthropic states that by default it does not train on inputs or outputs from its commercial products, including the API. Read the current terms yourself, and send only the fields the feature needs.

Can AI be added to a website built on WordPress or Shopify?

Yes, provided there is somewhere server-side to hold the API key. For a site on a hosted platform we add a small backend service that the site calls. The key must never sit in the page itself, where any visitor could copy it.

How long does an AI integration take?

The cost model's timeline bands are 3 to 6 weeks when the subtotal is under ₹3,00,000 and 6 to 10 weeks up to ₹6,00,000. Access to the existing code, a test environment and sample data on day one are what keep it at the short end.

Sources

Anthropic: Claude API pricing (model prices, batch discount, rate-limit tiers, token estimate) · read 2 October 2026

OpenAI: API pricing · read 2 October 2026

OpenAI: Structured Outputs guide · read 2 October 2026

OpenAI: Data controls in the OpenAI platform · read 2 October 2026

Anthropic Privacy Center: Is my data used for model training? · read 2 October 2026

Anthropic: Reduce hallucinations · read 2 October 2026

Zoho CRM: Zia · read 2 October 2026

HubSpot: AI products (Agent Hub and Customer Agent) · read 2 October 2026

Related

How to add AI to your app or website

AI development cost calculator

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