Agentic AI
& Automation

Autonomous AI agents that observe, reason, and act, transforming your business processes from manual to intelligent.

Overview

Software that thinks, not just executes.

Traditional automation follows rigid rules. Agentic AI goes further: it observes your data, reasons about what to do, plans a sequence of actions, and executes them autonomously. When something unexpected happens, it adapts.

We build custom AI agents tailored to your business workflows. Not generic chatbots, not simple RPA scripts, but intelligent agents that handle complex, multi-step tasks with the judgment and reliability your operations demand.

Fully Autonomous Agents

Integrates With Your Systems

Human-in-the-Loop Safety

invoice-agent — Running ILLUSTRATIVE

# New file detected in /inbox

$ invoice-agent process incoming_invoice.pdf

[ocr] Extracting text from PDF... done

[llm] Classifying document → Invoice

[llm] Vendor: Sun Hing Supply Co.

[llm] Amount: HK$142,500 | Due: 2026-04-15

# Cross-referencing with ERP

[tool:erp_query] Searching purchase orders...

[tool:erp_query] Match: PO-2024-0891 | HK$142,500

[llm] PO amount matches. Line items verified (3/3).

[policy] Amount under auto-approval threshold

# Executing actions

[tool:erp_update] Posting to Accounts Payable...

✓ Invoice matched and approved

✓ Payment scheduled for 2026-04-12

✓ Slack notification sent to #finance

Processed in 4.2s. Waiting for next task...

Capabilities

What Our Agents Can Do

Autonomous Task Execution

Agents that work independently on multi-step workflows (from data extraction to report generation) without human intervention at each step.

CORE ENGINE

Intelligent Document Processing

Extract, classify, and process invoices, contracts, receipts, and forms with high accuracy. Understands context, not just OCR text.

DOCUMENT AI

Process Automation

Replace fragile rule-based automations with AI that handles edge cases, exceptions, and variations in your business processes gracefully.

WORKFLOW

Decision Support Systems

AI that analyses data patterns, surfaces insights, and recommends actions, giving your team the intelligence to make faster, better decisions.

ANALYTICS

Multi-Agent Orchestration

Coordinate multiple specialised agents working together on complex operations: research, analysis, validation, and reporting in parallel.

ORCHESTRATION

Continuous Learning

Agents that improve through a human feedback loop: corrections and reviews feed into refined prompts, tools, and playbooks, so they get more reliable with use.

FEEDBACK LOOP

How It Works

The Agent Lifecycle

Every agent follows the same intelligent loop: continuously observing, reasoning, acting, and learning from outcomes.

Perceive

Ingest data from documents, APIs, databases, emails, and user inputs

Reason

Understand context, identify patterns, and evaluate options using LLM intelligence

Plan

Break complex tasks into ordered steps with fallback strategies for failures

Act

Execute actions via APIs, tools, and system integrations with validation checks

Learn

Fold outcomes and human feedback back into prompts and playbooks, then the cycle restarts

Continuous Loop

Each round of feedback makes the agent more reliable

Use Cases

Where Agents Excel

Financial Document Processing

Automated extraction and reconciliation of invoices, receipts, purchase orders, and contracts. Match line items to POs, flag discrepancies, and route for approval, all without human review for standard cases.

Hands-Free Processing

Customer Service Automation

AI agents that handle tier-1 support requests end-to-end: understanding the issue, checking account status, applying fixes, and escalating complex cases to human agents with full context and recommended actions.

Auto-Resolution

Supply Chain Optimisation

Agents that continuously monitor inventory levels, predict demand patterns, and automatically generate purchase orders when restocking thresholds are hit. Factor in lead times, seasonal trends, and supplier reliability.

Smart Restocking

Quality Assurance Automation

AI-powered test generation and defect detection. Agents review code changes, generate test scenarios, run automated tests, and produce detailed reports, catching issues before they reach production.

Automated Testing

HR & Recruitment Pipeline

Agents that screen resumes against job requirements, schedule interviews, generate personalised outreach, and manage the onboarding document flow, freeing your HR team for high-value interactions.

Streamlined Hiring

Case Studies

Agents In Action

Finance

Accounting Agent

Our own Profex Accounting System is built to be run by an AI agent: it exposes its entire back office through the Model Context Protocol (MCP), so an assistant like Claude can operate the books in plain language.

Challenge

Accounting software is powerful but slow to drive: every invoice, bill, payment, journal and reconciliation means clicking through forms and knowing where each feature lives. Teams without a dedicated bookkeeper lose hours to manual entry and month-end busywork, and the data stays locked behind the UI — out of reach of the AI assistants people now work with daily.

Solution

We made the platform MCP-native. An AI agent connects over the Model Context Protocol and drives the full finance workflow through 90+ tools: draft and approve invoices and quotations, record bills and payments, post and reverse journal entries, reconcile bank accounts, and generate reports. Seven built-in playbooks walk the agent through real jobs: month-end close, bank reconciliation, bill-payment runs, aged-receivables follow-up and quarter-end tax review. Every action is scoped to an API key, logged, and reversible. A companion Telegram bot lets staff capture invoices, expenses and receipts on the go.

90+ Agent tools: create to reconcile

7 Guided playbooks (close, bank rec, tax)

MCP-native Runs in Claude Desktop or over HTTP

MCPClaudePythonDjango RESTPostgreSQLDocker

Property

Tenant Concierge

An AI assistant built into a tenant-management platform: landlords and butlers handle daily operations by chat on WhatsApp, Telegram or Feishu.

Challenge

Landlords managing multiple units juggle repetitive inquiries (room details, contract terms, vacancy status) across phone calls, messages and spreadsheets. New-lease onboarding means tedious manual data entry from signed contracts, and properties without smart meters need someone to record every utility reading by hand each month.

Solution

Built a LangGraph tool-calling agent into the tenant-management system, running through a pluggable platform layer on WhatsApp, Telegram or Feishu. Landlords and butlers ask about rooms, tenancies, vacancies, contracts and utility readings in plain language (English or Chinese) and the agent answers by querying the live system. For a new lease they send a photo of the signed contract and a vision-LLM extracts the tenant, rent, deposit and dates to verify before saving; for utilities they photograph the meter and the model reads the value, logs it and calculates usage — no manual typing.

Vision LLM Reads contracts & meters from a photo

3 channels WhatsApp · Telegram · Feishu

LangGraph Tool-calling agent · EN + 中文

LangGraphClaudeGeminiQwenDjangoPostgreSQL

Green Wiser diagnostics agent investigating a charger fault

EV Charging

Charger Diagnostics Agent

An AI diagnostics agent inside an EV-charging platform: operators ask what's wrong with any charger or session in plain English, and it investigates the live system to find out.

Challenge

An EV-charging network runs on distributed hardware from different vendors, each speaking the OCPP protocol a little differently. When a charge fails, ops staff had to dig through OCPP status codes, database records and log stores across several systems to work out why. It was slow work that needed deep platform knowledge, so problems sat unresolved.

Solution

Built an agentic investigator into the admin console. An operator describes a symptom and anchors it to a charger, session, transaction or user; the agent runs a diagnostic loop (a relevance gate, then playbook-guided reasoning over 12 tools that query the live database and the OCPP log store) and streams its progress live before producing a plain-English diagnosis and a handoff report. Seven root-cause playbooks (hardware fault, firmware, config, network, user error, software bug) steer it, all personal data is redacted before it reaches the model, and it's multi-LLM with side-by-side model comparison and per-investigation cost tracking.

Plain English "Why did session X fail?" → answer

12 tools Live database + OCPP log investigation

Multi-LLM Model comparison + cost tracking

TypeScriptOpenAI / GeminiCloud DatabaseElasticsearchOCPPSSE

Technology