Autonomous AI workflow agents.
We build autonomous AI agents that execute complex multi-step workflows — from data extraction and document processing to decision-making and task orchestration — reducing manual effort by up to 90%.
from langgraph.graph import StateGraph, END
def agent_node(state):
llm = ChatOpenAI(model="gpt-4o")
response = llm.invoke(state["messages"])
return {"messages": [response]}
def tool_node(state):
last_msg = state["messages"][-1]
results = execute_tools(last_msg.tool_calls)
return {"messages": results}
graph = StateGraph(AgentState)
graph.add_node("agent", agent_node)
graph.add_node("tools", tool_node)
graph.add_edge("agent", "tools")
graph.add_conditional_edges("tools", should_continue)
agent = graph.compile()Agent Orchestration Layer
Stateful agent loops with tool calling and decision branching, enabling autonomous multi-step reasoning that adapts to dynamic inputs.
Tools & Frameworks We Engineer With
Agent Frameworks
- LangGraph: Stateful multi-actor orchestration
- LangChain: LLM application framework
- CrewAI: Multi-agent collaboration
- AutoGen: Microsoft agent framework
LLM Stack
- GPT-4o: OpenAI reasoning model
- Claude 3.5: Anthropic long-context
- Gemini 1.5: Google multimodal
- Llama 3: Open-source self-hosted
Integrations
- REST APIs: External service calls
- Database Queries: SQL and NoSQL access
- File Processing: PDF, Excel, CSV parsing
- Web Scraping: Automated data extraction
Infrastructure
- Redis: State and queue management
- BullMQ: Job queue processing
- Docker: Containerised deployment
- Kubernetes: Orchestration at scale
Who We Work With
Finance
Automated compliance checks, report generation, and transaction monitoring.
Legal
Contract analysis, clause extraction, and due diligence automation.
Operations
Workflow automation, process orchestration, and system integration.
Sales
Lead qualification, CRM updates, and outreach personalisation.
HR
Candidate screening, onboarding workflows, and policy compliance.
Research
Data extraction, literature review, and competitive analysis.
Our Delivery Lifecycle
We operate under a structured, predictable lifecycle. Click any step to inspect deliverables.
Process Mapping
We identify and map the manual workflows that agents will automate.
{
"processes_mapped": 8,
"automation_candidates": 5,
"estimated_savings": "120 hrs/month",
"roi_timeline": "3 months"
}Frequently Asked
We implement multiple reliability layers: structured output validation, retry logic, human approval gates for high-stakes decisions, and comprehensive error handling. Success rates exceed 98% for well-defined workflows.
You choose the level. Agents can run fully autonomously for low-risk tasks, require approval for medium-risk actions, or operate in supervised mode where humans review every decision. We configure this per workflow.
Costs vary by complexity. Simple single-tool agents start at a few weeks of development. Complex multi-agent systems with many integrations take 6-8 weeks. The ROI typically shows within 2-3 months through time savings.
Agents excel at repetitive multi-step tasks: data extraction from documents, report generation, system monitoring, customer onboarding workflows, compliance checks, and any process involving multiple tools and decision points.
Ready to automate with AI agents?
Get a workflow automation assessment with agent architecture design within one week.