AI INTEGRATIONS

Add Useful AI Capabilities to the Software You Already Use

Integrate AI into websites, web apps, mobile apps, CRM, support tools and internal systems so existing workflows can search, classify, extract, summarise, generate or assist where it creates practical value.

AI integration is not a separate AI gimmick. We start with the existing product or workflow, identify the job AI should perform, then connect the model and surrounding logic where it makes sense.

Where AI Can Be Integrated

Websites
Web applications
Mobile apps
SaaS products
CRM systems
Support platforms
Internal tools
Dashboards & portals
Custom business software

Common AI Integration Use Cases

The useful part is not the model itself. It is the specific job the model performs inside an existing product or workflow.

Search & Retrieval

Let users search approved business information using natural language instead of exact keywords.

Classification

Categorise enquiries, tickets, documents, products or records based on their content.

Extraction

Pull structured fields from emails, forms, PDFs or other unstructured text.

Summarisation

Create concise summaries of conversations, records, documents or long-form information.

Drafting & Assistance

Help staff prepare replies, descriptions, notes or internal content with human review where appropriate.

Matching & Recommendations

Use available data to suggest relevant items, routes, records or next actions where the use case supports it.

AI Can Be Added Without Rebuilding Everything

If your existing product already works, the right project may be a focused integration rather than replacing the whole system.

Websites

Add AI-powered search, product guidance, enquiry assistance or other focused capabilities to a business website.

Software & SaaS

Add AI features inside existing dashboards, workflows, user accounts or SaaS products.

Mobile Apps

Connect mobile experiences to AI services through a secure backend or API layer.

CRM & Support

Use AI to classify, summarise, draft, route or enrich customer and support workflows.

Internal Tools

Help staff work with records, documents, knowledge or repetitive information tasks.

Custom APIs

Expose approved AI functionality through a controlled service used by one or more applications.

The Model Is Only One Layer

A production AI feature usually needs more than sending user text directly to a model API.

Application interface
Authentication / permissions
Prompt or task logic
Approved context / retrieval
Model provider
Validation
Logging
Human review or fallback
Downstream system actions

Keep Sensitive Logic on the Server Side

API keys and privileged actions should not be exposed directly inside public browser or mobile interfaces. Where appropriate, SiteLumo uses a backend layer to control model calls, permissions, business rules and connected actions.

The exact architecture depends on the existing application and data sensitivity.

Common AI Integration Building Blocks

The right integration uses only the pieces the feature actually needs.

Model APIs

Connect suitable AI model providers through supported APIs and controlled backend logic.

Retrieval / Context

Provide approved business information or product data to the model where needed.

Structured Outputs

Request predictable fields or formats when downstream software needs machine-readable results.

Tool Calling

Allow the model to request approved system actions through controlled functions where appropriate.

Validation

Check or constrain AI outputs before using them in important downstream workflows.

Fallback Logic

Use rules, defaults or human review when the model cannot produce a safe or useful result.

Usage Controls

Apply limits, quotas or permissions where needed to manage access and operating cost.

Logging

Record enough request/output metadata to troubleshoot behavior without collecting more sensitive data than necessary.

Provider Flexibility

Choose a provider based on task quality, cost, availability and project requirements rather than one fixed vendor for every project.

Data Boundaries Matter

Before sending business or user information to an AI provider, the project needs a clear understanding of what data is involved and whether it should be transmitted at all.

What information the feature receives
Whether personal or confidential data is present
Which users can access the feature
What should be removed or minimised
Which provider account is used
What logs are retained

AI Is Not a Substitute for Compliance Decisions

SiteLumo can design technical controls around access, data flow and integrations, but the client remains responsible for determining legal, regulatory, contractual and industry-specific requirements that apply to its data and use case.

Higher-risk use cases may require specialist legal, security or compliance review before deployment.

What We Pay Attention To

An AI feature is only useful when quality, response time, reliability and operating cost make sense inside the actual product.

Task Fit

The chosen model should actually perform the required task well enough to justify integration.

Grounding

Where factual business information matters, the feature should use approved context rather than unsupported guesses.

Structured Validation

Machine-readable outputs should be checked before they update important systems.

Human Review

Generated or uncertain results may require approval before customer-facing or consequential use.

Latency & UX

AI calls can take time, so loading states, streaming or asynchronous flows need to fit the user experience.

Operating Cost

Model choice, context size, call frequency and user volume should make economic sense.

What Determines the AI Integration Quote?

The quote depends on the existing system, the AI task, data flow, backend requirements, integration points and production safeguards.

Existing Product

The current codebase, platform, architecture and available integration points affect the work.

AI Task

Search, extraction, generation, classification, recommendations and tool use have different requirements.

Data & Context

Knowledge sources, document volume, structured data and permissions can add complexity.

Backend Changes

Secure model access, APIs, queues, storage or new services may be required.

User Volume

Expected calls, concurrency and response-time requirements affect architecture and cost.

Validation & Review

Output checks, fallback logic, moderation and human approval can be important parts of the integration.

Define the Feature Before Choosing the Model

The written scope should clarify the user flow, AI task, data sources, provider/account responsibilities, integrations, validation, fallback behavior, operating assumptions and quoted cost.

Adding AI to an Existing Product?

We can review the current workflow and identify where a focused AI feature could improve the product without forcing a full rebuild.

Already Have an AI Integration?

Existing features may need better prompts, retrieval, structured outputs, cost controls, fallback logic, model changes or stronger backend integration.

A Written-First AI Integration Workflow

Existing-product details, screenshots, sample inputs, test cases, progress updates and approvals can all be handled asynchronously in writing. Meetings are optional.

Show Us the Existing Product

Explain the current system, user flow and where you want AI to help.

Define the AI Task

We clarify inputs, context, outputs, provider needs, permissions, validation and fallback behavior.

Integrate & Connect

The model, backend logic and required product integrations are implemented around the agreed scope.

Test Real Inputs

We test expected inputs, edge cases, bad outputs, latency and important failure scenarios.

Release & Improve

The feature goes live, with later tuning, model changes and new capabilities handled as new scope or support.

Provider Accounts & Ownership Are Defined

Model provider accounts, API keys, backend services, deployment, storage, logging, third-party costs and handover responsibilities are confirmed according to the project.

AI Integrations Keep Changing

Providers, APIs, pricing and model behavior change over time. SiteLumo can continue maintaining, tuning and updating the integration after launch.

AI Integration Questions

What is an AI integration?

It means adding a focused AI capability to an existing website, app, SaaS product, CRM, support system or internal tool rather than building a completely separate AI product.

Can you add AI to my existing website or software?

Potentially. We first review the existing platform, codebase or APIs to confirm the safest and most practical integration path.

Which AI provider do you use?

The provider depends on the task, quality, cost, availability, privacy requirements and technical fit. We do not force every project onto one model provider.

Can the AI use my business data?

Potentially, where the data is appropriate and access is properly designed. The project should define exactly what information can be sent, retrieved or exposed.

Can AI update records or perform actions?

Yes, in some projects. Tool calls or downstream actions should be controlled through backend logic, permissions, validation and clear business rules.

Can you switch an existing feature to a different model?

Potentially. Provider changes may require prompt, API, output-format, pricing or behavior adjustments, so the current implementation needs to be reviewed first.

Will there be ongoing costs?

Usually yes. AI model usage, storage, retrieval services, hosting or third-party APIs may have ongoing fees separate from SiteLumo project fees.

Can you guarantee AI outputs are always correct?

No. AI outputs can be wrong or uncertain. Production integrations should use grounding, validation, fallbacks and human review where appropriate.

Do we need meetings?

No. SiteLumo uses a written-first workflow. Existing-system details, sample inputs and test cases can handle most project communication.

What should I send for a quote?

Describe the current website or software, where you want AI added, the exact task it should perform, what data it needs, expected users or volume, and any existing provider or API setup.

Want to Add AI to an Existing Product?

Show us what the current system does and the exact job you want AI to perform. We’ll reply in writing with the questions needed to define the integration properly.