AI Integration & Workflow Automation
Practical AI features embedded in your product and automation systems that replace manual work — not demos, production deployments.
AI systems in production — prompt-engineered, monitored, and cost-optimized
AI integration is an engineering discipline. We do not add chatbots to websites to appear modern, and we do not prototype AI features not intended for production. Our AI work addresses specific problems: reducing manual classification, extraction, or generation tasks that consume hours of human time per week; enabling decisions that previously required manual research; or building product capabilities that were impractical without language models — such as real-time document analysis or dynamic content generation at scale.
Our AI work falls into two categories. Direct model integration — embedding OpenAI (GPT-4o, o1), Anthropic (Claude), or Google Gemini into your product — for features requiring natural language understanding, generation, or reasoning. And workflow automation using n8n or custom-built agents for connecting existing tools and eliminating repetitive operational work. Both approaches are available independently or combined.
Prompt engineering is a first-class engineering discipline in our process, not an afterthought. A poorly structured prompt in a production feature costs money at every API call and produces inconsistent output. We version prompts, test against adversarial inputs and edge cases, monitor output quality continuously, and rebuild prompts when model updates change behavior. For high-volume use, we implement structured outputs — JSON mode and function calling — to ensure reliable, parseable responses.
Automation systems built with n8n connect disparate tools — CRMs, databases, notification systems, payment processors, and third-party APIs — into workflows that execute without human intervention. We have built automations that reduced manual lead qualification review by 80% for one client, automated invoice generation from project management events, and built inventory alerting workflows connected to purchasing systems.
Custom AI agents handle tasks requiring multi-step reasoning, tool use, or context that persists across interactions. We use LangChain, the OpenAI Assistants API, and Anthropic's tool use for agent architectures, with explicit attention to failure modes — what happens when the agent cannot complete a task, produces a low-confidence answer, or encounters an input it was not designed for. An agent that fails silently is worse than no agent.
Every AI system we deploy includes monitoring for quality degradation and cost tracking. Model outputs change as providers update their models. We build evaluation pipelines that sample outputs against defined quality criteria and flag when performance drops — before your users notice. Token usage is tracked per feature and optimized continuously. Unchecked AI API costs in production are a real operational risk that we design controls for from the start.
What's Included
- [✓]AI feature integrated directly into your product or workflow
- [✓]Prompt engineering with versioning and edge case testing
- [✓]n8n automation workflows connecting your existing tools
- [✓]Custom AI agent with defined tool use and failure handling
- [✓]Cost monitoring and token usage optimization
- [✓]Output quality evaluation pipeline
- [✓]API integration with any third-party service
- [✓]Documentation and operational runbook
Tech Stack
Common Questions
Which AI models do you work with?
OpenAI (GPT-4o, o1), Anthropic (Claude 3.5 Sonnet and Haiku), and Google Gemini. Model selection is driven by task requirements, latency constraints, and cost per token. For most production text features, Claude 3.5 Haiku or GPT-4o mini provide the best cost-to-performance ratio.
How much does AI integration cost?
A focused AI feature integration (single model endpoint, prompt engineering, output validation, and monitoring) ranges from $5,000 to $15,000. Full automation systems with n8n workflows, custom agents, and evaluation pipelines range from $15,000 to $50,000 depending on complexity.
Can you automate workflows across tools we already use?
Yes. n8n has native integrations for over 400 services. For tools without native integrations, we build custom webhook handlers or API bridges. Common integrations include HubSpot, Salesforce, Slack, Notion, Airtable, Stripe, and all major cloud databases.
How do you manage AI costs in production?
We implement token budgets per request, caching for repeated identical queries, model tier selection (smaller models where quality permits), and usage dashboards for ongoing monitoring. Runaway AI API costs are a real production risk — we design cost controls from the start.
Do you build RAG (Retrieval-Augmented Generation) systems?
Yes. RAG systems — combining vector search with language models to answer questions from proprietary documents — are a common request. We implement with Pinecone, Supabase pgvector, or Weaviate depending on your data volume and latency requirements.
Ready to start a ai integration & automation systems project?
Scope defined before any code is written.