Every agent listed here is real, delivered at a Malaysian F&B manufacturer and a large Malaysian enterprise across finance, e-Invoice, and reporting workflows. All of them run on AutoGo, powered by our Aurix multi-agent engine, on Microsoft Azure.
We do not sell an "AI agent platform" and hope. We build specific agents, scoped to specific workflows, running on our own Aurix engine. Each one below has a name, a job, an ERP, and a real client engagement behind it. Some are running in production. Others were validated in a POC. We label each one honestly. If your finance function has any of these workflows, we can walk you through what it took to make it work.
See how agents meet enterprise security, compliance and SLA needsDelivered at one of Malaysia's largest F&B manufacturers. Three agents cover recurring finance work that used to eat analyst days.
AR Invoice Consolidator
Merges dozens of AR line items into consolidated invoices, ready for SAP posting.
Daily Cash Flow Agent
Produces a daily cash flow and financial planning report from the SAP feed.
SAP Journal Poster
Prepares and posts recurring SAP journal batches with reviewer approval.
Delivered for a large Malaysian enterprise across e-Invoicing, invoice OCR, payment advice distribution, advance settlement, and P&L compilation.
e-Invoice Converter
Extracts RAMCO invoice data and converts it into the Malaysian e-Invoice template.
Smart Invoice OCR
Reads supplier invoices and posts them straight into RAMCO.
Payment Advice Distributor
Sends each vendor their own payment advice email automatically.
Advance Settlement Agent
Runs advance settlement calculations and notifies each employee.
P&L Compiler
Builds the Profit & Loss statement from the trial balance.
Four healthcare AI agent patterns we deliver in six to eight weeks for Malaysian private hospital groups, TPAs, and healthcare providers. Same Aurix engine as our live agents, adapted for revenue cycle and administrative workflows. Reviewer-in-the-loop from day one, full audit trail, ready for PDPA and MOH scrutiny. If your team has the workflow, we can build the agent.
Eligibility Verification Agent
Queries corporate insurance, TPA, and GLC medical panel portals through bounded MCP tools for eligibility and benefits confirmation before care.
Patient Intake Automation
Extracts patient demographics, insurance card data, and referral notes into a structured intake record ready for reception staff to confirm.
Claim Denial Classifier
Reads denied claims, classifies denial reasons against a taxonomy, cross-references clinical documentation, and proposes appeals for a claims officer to sign off.
Prior Authorisation Preparation
Assembles clinical documentation, payer-specific PA forms, and medical necessity narratives for scheduled procedures, ready for clinician review and submission.
Five asset integrity AI agent patterns we deliver in six to eight weeks for oil and gas operators, refineries, gas processing plants, and heavy industry in Malaysia and ASEAN. Same Aurix engine as our live agents, aligned to the frameworks integrity engineers already trust: API 580 / 581 and 579, DNV, ASME, and PETRONAS Technical Standards. Delivered jointly with Optitech, our engineering partner with live integrity project experience in Malaysia and Vietnam. Reviewer-in-the-loop on every AI decision, full audit trail, ready for PTS and DOSH-JKKP evidence requirements.
Inspection Report Extractor
Reads scanned or PDF inspection reports, extracts findings and thickness data, and posts them into the integrity record.
AI-Assisted RBI Ranking
Proposes RBI ranking adjustments against your chosen framework (API 580 / 581, DNV, or ASME). Every suggestion carries its reasoning. Engineer signs off on every rank change.
Corrosion Anomaly Detector
Classical ML on inspection thickness readings and sensor telemetry. Flags outliers before they become failures, sends to engineer review queue.
Turnaround Scope Builder
Compiles proposed turnaround inspection scope from RBI outputs, inspection history, and Integrity Operating Window breaches. Planners review and refine before commit.
Regulatory Evidence Compiler
Assembles integrity evidence packages for PETRONAS SI/IE audits and DOSH-JKKP inspection reviews from your underlying integrity data.
Orchestrator plans. Small agents do one job each. Tools are bounded and authenticated through the AI API layer. Evaluator scores every output before it acts. Human escape hatch on every decision.
Putting an AI agent into production is mostly about what surrounds the model: who can access what, where data lives, how quality is proven, and who answers for the result. This is how every AutoGo agent is set up.
Security and access
Every agent runs under its own service identity in Microsoft Entra ID, never a shared or personal login. Who can configure an agent, approve its work, or view its logs is set by role, so a reviewer can approve results without being able to change how the agent works.
Agents reach your data only through scoped tools in the AI API layer, not raw database access. Each tool can be limited to the records a process needs, such as one subsidiary, cost centre or vendor group, rather than whole tables.
Models run on Azure OpenAI in a Malaysian or Singapore region, and documents, outputs and logs stay in the same region. On Enterprise, AutoGo can run inside your own Azure tenant or on-premise, so data never leaves your environment.
Compliance and privacy
Agents are designed around the rules Malaysian enterprises answer to: BNM RMiT for financial institutions, LHDN e-Invoice, PDPA, and PETRONAS Technical Standards and DOSH-JKKP for oil and gas. Controls and evidence are mapped to the framework that applies to you.
Under the PDPA amendments in force since 1 June 2025, organisations above the prescribed thresholds must appoint a Data Protection Officer and report qualifying data breaches to the Commissioner within 72 hours. AutoGo gives your DPO a record of what personal data each agent processes, where, why, and who accessed it.
Every tool call, retrieval, output and approval is logged in Azure Log Analytics and Microsoft Purview inside your tenant. When an auditor asks how a result was produced, the answer is a query, not a spreadsheet exercise.
Reliability and quality
We agree service level objectives (SLOs) for each process, such as processing time and success rate, and track the service level indicators (SLIs) behind them. Enterprise clients can add a contractual service level agreement (SLA) on top.
Before go-live, every agent is tested against your real documents and edge cases, not sample data. Changes to prompts, rules or connectors are re-tested against the same set before they reach production.
The Aurix Evaluator scores every output before the agent acts. Low-confidence results go to a person, and the share handled automatically only rises as evaluation scores prove stable on your data.
Commercial and control
Pricing is per business process with unlimited users, not per seat or per AI call. Monthly processing allowances keep spend predictable, and usage is visible so there are no surprise AI bills as volume grows.
Your data remains yours and is not used to train AI models. Data handling, confidentiality and responsibilities are set out in your agreement, with NDAs available from the first conversation.
Single sign-on, multi-step approval routing, policy-based approval thresholds, a register of every agent with its owner and permissions, private tenant or on-premise deployment, and SLA-backed support.
Tell us the process, the ERP, and the pain point. We will scope whether an agent earns its complexity and, if it does, what a six to eight week pilot looks like.