AI agent and automation services
Practical AI systems, automations, APIs, and full-stack products.
I combine full-stack product engineering with agentic AI workflows to build systems that connect tools, data, APIs, and users. Every engagement starts with a clear workflow and ships usable software built around real operational work.
AI Agent Development
A working AI agent flow for one use case, with connected tools, a simple product surface, documented architecture, tested happy paths, failure notes, and a clear next-iteration plan.
Founders, SaaS teams, agencies, and small businesses that need one practical AI agent for a real workflow.
- Turn a manual workflow into an agent-assisted process
- Connect AI to real tools, APIs, and business data
- Ship a usable workflow instead of a prompt demo
- Keep tool permissions, logs, and data boundaries explicit
Deliverables
- Workflow map and technical approach
- Agent implementation
- Tool/API integration
- Backend endpoints or service layer
- Basic frontend or admin interface when needed
LangGraph and LangChain Workflows
A maintainable LangGraph/LangChain workflow that is easier to debug, extend, test, and connect to a product, API, automation, or agent interface.
Teams moving from simple prompts to structured agentic systems that need reliability, debugging, and extension paths.
- Make complex agent behavior explicit
- Add routing, retries, and review loops
- Improve reliability over single-prompt flows
- Prepare the workflow for future tools, agents, or product integration
Deliverables
- Workflow architecture diagram or map
- LangGraph/LangChain implementation
- Typed state design
- Node and routing logic
- Example runs or test cases
MCP Server and Tool Integration
A documented MCP/tool layer with clear capabilities, inputs, outputs, validation, error handling, permission boundaries, and usage examples.
Teams building agents that need reliable access to tools, APIs, databases, files, or internal systems.
- Give agents structured access to real tools
- Reduce brittle prompt-only automation
- Control what the agent can read or change
- Improve debugging with clear inputs, outputs, and errors
Deliverables
- Tool capability map
- MCP server or tool layer implementation
- Schemas and validation logic
- Connector handlers
- Error handling and basic tests
RAG and Knowledge Assistant Build
A working knowledge assistant with content ingestion, retrieval design, answer generation, source-aware responses, a usable API/UI, and documented limitations.
Teams with documents, support content, policies, or internal knowledge that they want to operationalize.
- Make internal knowledge easier to use
- Reduce repeated manual answers
- Ground answers in source material
- Create a base for future agent workflows
Deliverables
- Content ingestion flow
- Retrieval implementation
- Assistant API
- Frontend or admin interface
- Evaluation notes
Full-Stack AI Feature Development
A shipped AI feature integrated into your product with maintainable code, persistence, clear operational behavior, and handoff documentation.
SaaS teams, founders, and product teams adding useful AI features to existing products or MVPs.
- Ship AI inside the product experience
- Connect frontend, backend, and data correctly
- Avoid isolated prototypes that cannot scale
- Keep user and data boundaries visible
Deliverables
- Feature specification
- Frontend UX implementation
- Backend API/service layer
- Database changes
- AI/model integration
Business Automation and API Integration
A reliable automation flow with connected tools, clear monitoring points, documentation, and practical day-to-day usage instructions.
Businesses and agencies that need repeatable operational workflows with custom API or backend logic.
- Reduce repetitive manual work
- Connect disconnected tools and data sources
- Create visibility through dashboards or logs
- Add AI only where it improves the workflow
Deliverables
- Workflow map
- API integration
- Automation logic
- Dashboard or admin controls
- Monitoring/logging notes
n8n Automation and AI Workflow Integration
A working n8n workflow with clear trigger logic, connected services, error handling, testing notes, and documentation so your team can run or extend it.
Small businesses, agencies, founders, and operations teams that want practical automation quickly with n8n.
- Automate workflows faster with n8n
- Connect apps, APIs, webhooks, and notifications
- Add AI steps without overbuilding custom software
- Keep workflows documented and maintainable
Deliverables
- n8n workflow design
- Workflow implementation
- API/webhook connections
- AI node or LLM integration when useful
- Error handling and test runs
Delivery process
Clear workflow, practical build, usable delivery.
Define
Clarify the workflow, business goal, existing stack, tools or APIs to connect, data boundaries, timeline, and expected result.
Build
Implement the AI agent, MCP integration, LangGraph/LangChain workflow, API connection, dashboard, or automation with focused iteration.
Deliver
Hand over the working system, usage notes, implementation details, testing notes, and the next-step improvement plan.
Real work
Systems from the field.
AI-assisted workflows connected to real business processes
My current focus is building AI systems that connect user interfaces, APIs, data, tools, and repeatable workflows instead of isolated chatbot demos.
The work connects product interfaces, backend services, data flows, tool calls, and deployment into one system designed for day-to-day use.
Focused on useful automation and complete product deliveryFAQ
Before we scope the work
What AI and full-stack services do you offer?
I design and build AI agents, MCP integrations, LangGraph and LangChain workflows, provider-agnostic LLM features, RAG systems, business automations, API integrations, dashboards, and complete full-stack applications.
Why work with a full stack developer for AI agents?
AI workflows become more useful when the developer can build the frontend, backend, database, APIs, tool calls, and deployment path around the agent.
What methodology backs the work?
Engagements combine full-stack development, LangGraph/LangChain patterns, MCP tool integration, API-first design, product thinking, testing, and iterative delivery.
How do we start?
Send the target workflow, existing stack, tools or APIs to connect, timeline, and business goal. I will reply with a clear next step, estimated scope, and the best service fit.