For teams that already have software and want AI added into it without rebuilding the product.
Connect AI with the systems your team already uses.
I help integrate AI capabilities into existing applications, CRMs and internal tools, with attention to data access, human review and what happens when a workflow fails.
Describe the system, the data involved and the outcome you need.
- OpenAI
- Claude
- RAG
- Next.js
- Node.js
- AWS
Who this is for
AI integration fits teams that already have a working product and want AI features added on top of it.
SaaS companies extending their product with AI
Internal-tools teams adding AI to existing workflows
WordPress / WooCommerce stores adding AI assistance
API products adding AI endpoints or agents
Teams that want AI added without a full rewrite
When your product needs AI
If any of this matches what your team is planning, AI integration is usually the simplest path.
Your users keep asking the same questions
An assistant grounded in your product or company data can answer them reliably.
Your team repeats the same structured work
LLMs can draft, summarise, qualify, and route without changing your existing systems.
Your product is missing smart features competitors have
AI assistants, recommendations, summaries, and semantic search are usually one integration away.
Your data is valuable but inaccessible
Retrieval over your own documents turns documents, tickets, and notes into answers.
What I integrate
AI capabilities that plug into an existing application.
- OpenAI / Claude / Gemini APIs
- AI assistants and chat interfaces
- RAG over your existing data
- Structured outputs and tool calling
- AI scoring, qualification, and enrichment
- AI-powered search and recommendations
- Application-specific context retrieval
- Backend services for AI features
Existing-stack compatibility
AI integration works with the stack you already ship. None of the items below require a rewrite.
Frontend
- Next.js
- React
- TypeScript
- Tailwind
Backend
- Node.js
- Express
- FastAPI
- Python
- REST APIs
Data
- PostgreSQL
- MongoDB
- Redis
- Vector databases
Cloud
- AWS
- Docker
- GitHub Actions
AI integration architecture
How AI features plug into an existing product without forcing a rebuild.
Service-layer boundary
AI features live behind a service that the existing app already calls so changes stay contained.
Data access through your own APIs
Retrieval uses your existing data sources no parallel databases or duplicate pipelines.
Authentication inherited
The AI features inherit the same auth model as the rest of the product no new edge cases.
Structured outputs where it matters
Tool calls and JSON schemas replace free-form text in places the rest of the system has to parse.
Application context
User, tenant, and product context are passed into prompts so answers stay relevant.
Observability from day one
Latency, cost, and quality are logged in the same observability stack as the rest of the app.
How integration projects run
A small engagement model short enough to add AI without slowing the rest of the roadmap.
Where AI fits
Identify the highest-value place to add AI in the existing product and the smallest change to ship it.
Architecture sketch
Decide the model, retrieval, service boundary, and how the existing app talks to the new layer.
Integration
Add the service layer, expose its APIs, and connect it to the existing UI or APIs.
Evaluation and rollout
Evaluate outputs, add guardrails, and roll out behind a feature flag or to a small cohort first.
Hardening
Improve prompts, retrieval, error handling, and observability based on real usage.
Relevant work
AI work that lives inside a real product.
Skannr conversational AI booking
A conversational entry point to a healthcare booking platform, grounded in provider data.
Next.js · FastAPI · OpenAI · PostgreSQL · AWS
View ProjectHexify AI CRM automated lead workflow
AI-driven lead discovery, enrichment, and scoring wired into a working CRM.
React · Next.js · Node.js · OpenAI · Playwright · PostgreSQL
View ProjectCommon integration scenarios
Frequent AI integration shapes I've shipped or scoped.
AI assistant inside an existing SaaS
A bounded assistant that uses your existing data to answer product questions.
AI-assisted drafting and summarisation
Drafts, summaries, or rewrites inside the product's existing workflow.
AI-enriched records
Existing records leads, tickets, opportunities enriched or scored by an LLM.
AI semantic search
Replace keyword search with retrieval over your documents, tickets, or content.
RAG over company documents
Answers grounded in your documentation, knowledge base, or content library.
Frequently asked questions
Answers to common questions about integrating AI into an existing product.
No. Most AI work is integration work wiring models, retrieval, and tool use into the systems you already ship. The aim is scope, not rewrites.
Primarily OpenAI, Claude, and Gemini. The right model depends on capability, latency, cost, and data-handling requirements.
Yes. I work alongside your existing engineers, in the languages and frameworks the codebase already uses.
Integration focuses on adding AI to software that already exists; development focuses on building software around AI. See the AI Development page for the other side.
Yes see the RAG Development page for the architecture and the integrations I typically build.
Depends on the surface area. Many focused integrations land inside a few weeks; broader rollouts are scoped project-by-project.
Tell me which system needs an AI connection.
I start with the tools you already use, then design input, action, output and approval points around the real workflow.
You don't need to know the technical solution first. Describe the problem I'll help identify the right path.