Workflow automation
Multi-step processes run by agents across your tools.
AI & Agent Engineering
AI Automation Services
AI automation uses language models and agents to complete multi-step business work that rules-based automation cannot handle.
We map the workflow first, automate the steps where AI is dependable, and keep a human approval step wherever a mistake would be expensive.
- Workflow automation
- CRM
- Support
- Document processing
- Human-in-the-loop
CAPABILITIES
What we build
CRM and sales automation
Lead qualification, enrichment, follow-ups, and quoting.
Customer support
Triage, drafting, and resolution with escalation to your team.
Document processing
Extraction, classification, and validation of documents at volume.
Internal operations
Reporting, reconciliation, and back-office tasks handled automatically.
Human-in-the-loop systems
Approve, edit, and override steps built into every critical action.
TOOLS
The AI stack we work in
Chosen per use case, not by default.
-
OpenAI
-
Claude AI
-
Gemini
-
Llama
-
Gemma
-
LangChain
-
LangGraph
-
CrewAI
-
AutoGen
-
Hugging Face
-
Pinecone
-
PgVector
-
Chroma
-
Whisper
-
Stable Diffusion
-
PyTorch
-
TensorFlow
OUR APPROACH
Our AI-native process
Seven steps, one team. Intelligence is scoped at the start and evaluated at the end.
-
01
Discovery & AI Opportunity Mapping
We start every engagement by mapping where intelligence belongs in your product: which workflows an agent can own, where an LLM adds real value, and where plain software is the right answer. You get an honest AI scope — including what not to build.
-
02
UI/UX Design
Research, flows, wireframes, and high-fidelity interfaces, validated with users before development begins. For AI features, we design the states that matter: streaming, uncertainty, and human-in-the-loop control.
-
03
Intelligence Engineering
Model selection, prompt and agent architecture, RAG pipeline design, and evaluation harnesses — built with LangChain, LangGraph, CrewAI, and the vector store that fits your data.
-
04
Web Development
Frontend and backend engineering on React, Next.js, Node, and Python, delivered in short sprints with working software at the end of each one.
-
05
Mobile Development
Native and cross-platform apps for iOS and Android, built against the same APIs and design system as the web product.
-
06
Cloud & Deployment
Infrastructure as code, CI/CD, and monitored releases on AWS, Azure, or GCP — including GPU/inference infrastructure and model-cost monitoring where AI workloads demand it.
-
07
Testing & AI Evals
Automated test suites cover every release. AI features get their own evaluation suites — regression tests for prompts, hallucination checks, and output-quality scoring — so intelligence ships with the same rigor as code.
FAQ
AI Automation — common questions
What is an AI agent?
An AI agent is software that uses a language model to plan steps and take actions — calling tools, APIs, and data sources — to reach a goal with limited human input.
What is the difference between an AI agent and a chatbot?
A chatbot answers messages. An agent completes tasks: it decides which tools to use, acts on your systems, and checks its own result. Many products need both.
How much does AI agent development cost?
It depends on workflow complexity, the number of integrations, the data and retrieval involved, security requirements, and monitoring. We price a defined first workflow after discovery.
How do you keep agents under control?
Scoped permissions, action allow-lists, human approval on high-impact steps, full tracing, and task-success evaluations in production.
Ready to build AI-native?
Tell us what you're building — we'll show you where AI belongs in it.