Our AI integration services connect large language models, machine learning and AI agents to your existing websites, apps, CRMs, ERPs and data, securely and without a rebuild. Your team gets AI inside familiar workflows, and your data stays under your control.
Most businesses do not need a brand-new AI platform. They need AI working inside the tools their people already use: the CRM, the helpdesk, the ERP, the website and the mobile app. Our AI integration services make that happen through secure APIs, reliable data pipelines and careful permission design, so AI adds value without disrupting what already works.
Whether you want one AI feature inside an existing product or AI connected across your whole stack, our AI integration services cover planning, engineering, deployment and support.
System and API inventory
Data access and quality review
Security and compliance check
Prioritised integration plan
We review your applications, APIs, data sources, infrastructure and security policies to see where AI can plug in, what needs preparing first and which integration will deliver value fastest.
Target integration architecture
Model and provider selection
Permission and data boundary design
Fallback and error handling
We design how AI will connect to your systems: which model or service to use, where it runs, how data flows, how permissions are enforced, how failures are handled and how costs are controlled, before a line of integration code is written.
OpenAI / ChatGPT API integration
Claude and Gemini integration
RAG over your own data
Guardrails and usage limits
Our generative AI integration services add LLM features, such as drafting, summarising, answering questions and extracting data, to your software using OpenAI (ChatGPT/GPT), Anthropic Claude, Google Gemini or private open-weight models, with prompt management, guardrails and usage limits.
AI agent tool integration
MCP server development
Scoped permissions and approvals
Action logging and audit trails
Agentic AI integration services that let AI agents take actions in your systems, such as creating tickets, updating records or triggering workflows, through tool calling and Model Context Protocol (MCP) servers, with scoped permissions, approvals and full audit logs.
Salesforce and Einstein AI
HubSpot and Zoho AI features
SAP, Odoo and ERP automation
AI document processing into ERP
We add AI to Salesforce, HubSpot, Zoho, Microsoft Dynamics, SAP, Odoo and similar platforms, with lead scoring, email drafting, record summaries, forecasting and document processing, using native AI features such as Salesforce Einstein where they fit and custom integrations where they do not.
AI-powered search
Personalised recommendations
In-app AI assistants and chat
Image, voice and OCR features
We integrate AI into your website, web app or iOS/Android app: smart search, recommendations, AI assistants, content generation, image and voice features, built into your existing codebase and design system.
ETL and streaming pipelines
Vector database and embeddings
Data warehouse connections
Access-controlled retrieval
AI needs the right data at the right time. We build the pipelines, connectors, embeddings and vector indexes that feed AI from your databases, documents, data warehouse and SaaS tools, with governance and access control carried through.
API facades for legacy apps
Middleware and message queues
Document and screen data capture
Phased modernisation path
Older systems can still benefit from AI. We add API layers, event hooks or middleware around legacy applications and databases so AI can read from and write to them safely, as a first step toward modernisation or as a long-term solution.
Model serving and APIs
Versioning and rollbacks
Latency and quality monitoring
Cost and usage tracking
We deploy custom and open-weight models to your cloud or private infrastructure, expose them as secure APIs, and set up monitoring, versioning, evaluation and scaling so integrated AI stays fast, accurate and affordable.
Tell us which systems your team works in every day. We will show you where AI can plug in and what a first integration would take.
There is more than one way to integrate AI. We choose the pattern that fits your systems, data and risk profile, and often combine several.
AI features built directly into your web, mobile or desktop application, working with its existing users, data and interface.
Secure connections between your systems and AI APIs such as OpenAI, Anthropic, Google and cloud AI services, with retries, rate limits and logging.
Pipelines that move and transform data from source systems into AI models and vector stores, and write AI results back where they are needed.
AI steps added to business workflows in tools like n8n, Make, Zapier, Power Automate or your own backend, such as classifying, extracting, drafting and routing.
AI agents given controlled access to your systems through tool definitions and MCP servers, so they can complete tasks, not just answer questions.
Custom or open-weight models hosted in your environment and exposed as internal services for applications to call.
We work with the platforms businesses rely on most, as well as custom-built and legacy software.
Salesforce, HubSpot, Zoho CRM and Microsoft Dynamics: lead scoring, summaries, email drafting and next-best actions.
SAP, Odoo, NetSuite and QuickBooks: invoice capture, forecasting, reconciliation and reporting assistants.
Zendesk, Freshdesk, Intercom and Jira Service Management: ticket triage, reply drafting and AI chatbots.
Shopify, WooCommerce and Magento: product content, smart search, recommendations and order assistants.
WordPress, headless CMS and custom sites: AI search, content tools and on-site assistants.
Slack, Microsoft Teams and Google Workspace: internal assistants, meeting summaries and knowledge search.
Snowflake, BigQuery, Databricks, Power BI and Looker: natural-language analytics and AI-ready data.
Your own applications and older systems, integrated through APIs, middleware and event-driven hooks.
Integrating AI into existing systems is usually the fastest, lowest-risk way to get value from it. Here is what that looks like in practice.
Adding AI to tools people already use avoids a long rebuild and gets results in front of users sooner.
Teams use AI more when it appears inside their usual workflow instead of in a separate app.
AI handles reading, sorting, drafting and data entry between systems, freeing people for higher-value tasks.
AI surfaces summaries, predictions and insights at the point of decision, within existing screens.
Your existing access controls, audit trails and data residency rules extend to the AI features.
A clean integration layer lets you switch or add AI models later without reworking every application.
AI integration is where AI meets real software, with its authentication, edge cases, performance limits and legacy code. As a software development company with AI engineers in-house, we handle both sides properly.
Each industry runs on different systems and rules. We bring AI into the platforms and workflows specific to your sector. Explore our industry software services:
The models, frameworks and integration tools we use to connect AI to business systems reliably and securely.
OpenAI API (GPT)
Anthropic Claude API
Google Gemini API
Llama / Mistral (self-hosted)
AWS Bedrock
Azure OpenAI
LangChain / LangGraph
pgvector / Pinecone / Qdrant
REST & GraphQL APIs
RabbitMQ / Kafka / SQS
n8n / Make / Zapier
Python / FastAPI
Node.js / TypeScript
Laravel / PHP
Java / .NET
Redis caching
Docker / Kubernetes
Langfuse / OpenTelemetry
A step-by-step AI integration process that protects the systems you rely on while getting AI into users' hands quickly.
We map the workflows, systems, data and security requirements involved, and agree what the integration should achieve.
We define the architecture, model choice, data flows, permissions, fallbacks and cost controls.
We set up connectors, pipelines, embeddings and service accounts with least-privilege access.
We implement the integration behind feature flags and test accuracy, security, performance and failure scenarios.
We release to a pilot group first, gather feedback, then expand, with no big-bang switchover.
We track quality, latency, usage and cost, and update prompts, models and integrations as needs change.
Tell us which systems you use and where you want AI to help. We will respond with an integration approach and next steps.
The applications, data and workflows you want AI to work with.
Architecture, scope, timeline and cost estimate for the first integration.
Pilot, measure and expand, with support after launch.
ConverseIQ is an all-in-one WhatsApp SaaS platform that helps businesses manage customer conversations, automate messaging, capture leads, and empower teams to engage with customers efficiently through WhatsApp.
AI integration means connecting artificial intelligence capabilities, such as large language models, machine learning models or AI agents, to the software, data and workflows a business already uses. Instead of working in a separate AI tool, people get AI features inside their CRM, ERP, website, app or internal systems.
An AI integration usually has four parts: a connection to an AI model or service (often via API), a data layer that gives the model the right information with the right permissions, the application changes that show AI results to users or trigger actions, and monitoring for quality, security and cost. We design, build and test each part, then roll the integration out in stages.
The cost of AI integration depends on the number of systems involved, how accessible your data and APIs are, whether you use a hosted model API or a self-hosted model, security and compliance requirements, and expected usage volume, which drives ongoing API and hosting costs. A single AI feature in one application costs far less than AI connected across several platforms. We provide a written estimate after reviewing your systems.
A focused integration, such as adding an AI assistant or document extraction to one application, can often be delivered in a few weeks. Integrations spanning several systems, legacy software or strict compliance reviews typically take a few months. API availability and data readiness have the biggest impact.
Yes. We add AI features such as smart search, recommendations, chat assistants, content generation and image or voice capabilities to existing websites, web apps and iOS/Android apps, working within your current codebase and design. For customer-facing chat specifically, see our AI chatbot development services; for assistants that help users inside your product, our AI copilot development services.
We integrate OpenAI (ChatGPT/GPT models), Anthropic Claude, Google Gemini, cloud AI platforms such as AWS Bedrock, Azure OpenAI and Vertex AI, and open-weight models such as Llama and Mistral that can be self-hosted. We also integrate your own custom machine learning models. We recommend the option that fits your accuracy, cost, latency and data-residency needs.
Usually not. Most AI integration projects add AI alongside existing systems through APIs and middleware. For older software without APIs, we can build an integration layer around it, and, if it makes sense, plan a gradual legacy application modernisation.
We give AI only the minimum data it needs, enforce your existing user permissions on retrieved content, encrypt data in transit and at rest, mask personal data where possible, use provider settings that do not train on your data, defend against prompt injection and log every AI action. Private or regional deployments are available when data must not leave your environment.
Agentic AI integration connects AI agents to your systems so they can take actions, not just generate text: creating tickets, updating records, sending notifications or running multi-step workflows. We expose carefully scoped tools, often through Model Context Protocol (MCP) servers, add approval steps for sensitive actions and keep an audit log of everything an agent does. To have the agents themselves designed and built, see our AI agent development services.
CodeBase Coders provides end-to-end AI integration services: readiness assessment, integration architecture, LLM and agentic AI integration, CRM/ERP and app integration, data pipelines, model deployment and ongoing support. We also offer broader software integration services, AI development services for new AI builds and AI consulting if you are still deciding where to start. Contact us to plan your integration.
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