B2B SaaS and tech companies
Adding AI features to existing products, building internal tools, or prototyping new ideas with a clear path to production.
Tech companies and B2B SaaS firms need to move fast on AI - either adding it to existing products or building new AI-powered offerings. I help with both.
For product teams: adding AI features (search, recommendations, assistive writing, automation) to existing products with a clear path to production. For internal tools: building AI-powered systems that make your team more productive. For prototyping: validating new ideas quickly with working software, then hardening for scale. I've worked with startups and established tech companies; the approach is the same - ship something useful, learn from usage, iterate.
Example AI integrations
AI services and tools I've integrated for businesses include:
Vercel AI SDK
React AI components for embedding chat and completions in SaaS. In B2B SaaS, it ships chat and AI features quickly in React apps.
LangChain
Framework for building AI features into B2B products. In B2B SaaS, it builds RAG, agents, and AI workflows into products.
Anthropic
Claude API for long-context and tool-use in product AI. In B2B SaaS, it powers long-context and tool-calling in product AI.
OpenAI
GPT APIs for embedding AI in B2B products. In B2B SaaS, it adds chat, embeddings, and completions to products.
Vectara
Conversational search API for SaaS applications. In B2B SaaS, it adds conversational search to applications.
Jina AI
AI reader and embeddings for RAG and search pipelines. In B2B SaaS, it powers RAG and semantic search in products.
Try these free tools
AI product integrations
- Search and retrieval - Semantic search over your documents, products, or knowledge base so users find what they need without exact keywords.
- Workflow automation - AI agents that triage tickets, draft replies, route requests, and trigger follow-ups. Integrate with your existing tools.
- Predictive analytics and forecasting - AI for demand forecasting, anomaly detection, risk scoring, and time-series prediction.
Frequently asked questions
- I integrate via your existing API layer - adding AI endpoints for search, recommendations, or content generation that your frontend calls. The AI layer sits alongside your codebase, not inside it. I work with your engineering team to ensure clean integration and maintainability.
- For your first AI features, external help gets you to production faster and avoids the learning curve. Once the patterns are established, your team can maintain and extend them. I aim to leave codebases your engineers can own, not black boxes they depend on me for.
- I design for cost-efficiency from the start - caching common responses, choosing the right model size for each task, and using cheaper models for simple operations. I set up monitoring so you can track costs per feature and per user, and optimise as usage grows.
- Absolutely. Common internal AI tools include: code review assistants, documentation generators, customer data enrichment, support ticket triage, and internal search over your knowledge base. These pay back quickly by saving engineering and support time.
How do you add AI features to an existing SaaS product?
Should we build AI features in-house or hire externally?
How do you handle AI model costs at scale?
Can AI help with internal tools for our tech team?
Want to discuss AI for your business?
I help businesses integrate AI into their workflows. Get in touch to talk through your specific situation.