AI Integration Services That Ship Into Your Live Website, Web App or CRM
We build custom AI integrations directly into the systems you already run: vector search over your own content, LLM features powered by OpenAI, Gemini or Claude, and automated workflows that connect to your CRM, helpdesk or storefront. No platform migration, no strategy-deck retainer. Working software in your production environment.
The Problem With the AI Integration Market
Most businesses looking to integrate AI into their website or application are offered one of two extremes:
The Enterprise Consultancy Route
Months of discovery workshops, a strategy deck, and a six-figure retainer before a single feature reaches a user. The output is a roadmap, not a working system.
The Wrapper-Plugin Route
A chatbot widget dropped onto your site, connected to a general-purpose model with no access to your actual data. It sounds confident, invents answers, and quietly erodes customer trust the first time it gets a price or a policy wrong.
ONS TECH Takes the Practical Middle Path
We are a senior engineering team that has been building production software since 2014 (18+ years of PHP and Laravel experience, 8+ years of React Native). AI is one more integration we build properly: connected to your real data through direct API integration, handled privately, and measured against an operational outcome you define up front, such as fewer support tickets, faster lead response, or higher booking conversion.
Core AI Integration Capabilities
We engineer four specialized AI integration practices directly into your production codebase:
Custom Vector Search & RAG Architecture
Keyword search fails when customers describe what they want in their own words. Vector search integration matches on meaning, so "somewhere quiet for a family with young kids" finds the right listing even if those words never appear on the page.
We connect your proprietary documents, product catalogue, help centre or knowledge base to a private vector index, then use Retrieval-Augmented Generation (RAG) so the model answers only from retrieved, verified content.
CRM & Helpdesk AI Automation
Qualify, route and respond to inbound enquiries without adding headcount. We build AI-assisted lead qualification and ticket triage that syncs two ways with HubSpot, Salesforce or Zendesk, so records, notes and statuses stay consistent between the AI layer and your team's tools.
Every automated flow includes a clear human fallback. When confidence is low or the request is sensitive, the conversation hands off to a person with full context attached.
Multi-Model LLM Orchestration
Different tasks suit different models. We wire up production API connections to OpenAI, Google Gemini and Anthropic Claude, then route each task to the model that fits its quality, speed and cost profile.
The integration includes the engineering that separates a demo from a dependable feature: token and context optimisation, streaming responses so users see answers immediately, retries and timeouts, usage logging, and provider fallback so one outage doesn't take your feature down.
Website & E-Commerce Conversational Assistants
A context-aware assistant that knows your catalogue, availability and policies, and can guide a visitor from a question to a booking, basket or sign-up. It works across custom web apps, WordPress and Shopify storefronts.
We ground the assistant strictly in your live database, style it seamlessly to your brand typography, and wire it directly into your Stripe or shopping cart checkout flow.
Case Study: Tours Grenada — Vector Search & Travel Assistant
Review how practical vector search and RAG architecture transformed an excursion booking platform.
Tours Grenada (toursgrenada.com)
Caribbean tour operator and excursion booking platform
The Challenge
Visitors planning a trip want specific answers quickly: what an excursion involves, what's feasible in which season, how to combine activities into an itinerary. Waiting hours for an email reply meant many of those high-intent visitors went elsewhere.
The Solution
We built a vector search-powered AI assistant using embeddings and a RAG architecture. It retrieves from verified excursion data to give contextual recommendations, then guides the visitor directly into the booking flow.
Technical and Business Outcomes
Responses generated from retrieved excursion data rather than general knowledge, keeping recommendations tied strictly to real offerings.
Retrieval and token streaming response tuned for travellers on cellular mobile connections with zero UI freezes.
The assistant hands interested visitors straight to the reservation pipeline, capturing high-intent booking leads automatically.
Data Security, Privacy & Compliance
Security hesitation is the most common reason AI projects stall, so here are our commitments in plain terms.
No Training on Your Data
We integrate through business and enterprise API tiers whose terms exclude submitted data from model training, and we confirm the current terms for each provider during scoping.
Private Vector Storage
Your content is indexed in a private, access-controlled vector database with encryption in transit and at rest. It is not shared with other clients or used to build anything outside your project.
Role-Based Access Control (RBAC)
The AI layer only retrieves what the current user is permitted to see. A customer-facing assistant never surfaces staff-only documents or confidential organizational records.
GDPR-Aligned Data Handling
We minimise the personal data sent to models, support deletion and retention requirements, and document where data flows across your data pipelines.
Full Client IP & Code Ownership
All custom code, system prompts, configuration schemas, evaluation sets, and private indexes are transferred to you at handover. Zero proprietary vendor lock-in.
Our 4-Week Implementation Framework
Timelines depend on data volume and the number of systems involved. We confirm scope and schedule in writing before kickoff.
Architecture & Data Pipeline Discovery
We map your systems, data sources and access rules, define the use case and its success metric, and design the pipeline from source data to retrieval.
Vector Embedding & Model Configuration
We clean and chunk your content, generate embeddings, stand up the private vector index, and configure model selection, prompts and grounding rules. We also build an evaluation set of real questions to test against.
API Integration & Frontend UI Hookup
We connect the AI layer to your application, CRM or storefront through secure APIs, and build the user interface, including streaming responses, source display and human handoff.
Stress Testing, Latency & Handover
We load-test, tune latency and token usage, run the evaluation set, and deploy to production. You receive documentation, monitoring and a handover session.
Frequently Asked Questions About AI Integration
Direct, practical answers about vector search, data privacy, tech stack compatibility, and token costs.
Ready to Put AI to Work in Your Existing Systems?
Bring us your architecture, your data and the problem you want solved. In a technical discovery call, we'll review how your system is built, what data you have, and what a practical first integration looks like, with a realistic scope and timeline. You'll talk directly with the engineers who would build it.