AI that does real work inside real systems.
Specialised agents, document intelligence, process automation, knowledge systems and voice AI, engineered inside your Microsoft 365, Google Workspace or Azure environment and the platforms connected to it, with identity, permissions, approvals, audit and cost designed in from the start. Brisbane-based, working Australia-wide.
Most AI agencies understand prompt → LLM → Zapier. That works until the agent needs to read a SharePoint library or a shared drive under the right identity, respect a permission boundary, survive a tenant change or explain what it cost. We engineer that part.
The assistant is the visible part. The architecture is the work.
We don't bolt chatbots onto websites. When we build a public AI assistant, it operates against controlled knowledge, approved tools and defined business workflows. The conversation a visitor or a team member sees is only the interface.
Behind it sit identity, permissions, knowledge, models, APIs, specialist agents, workflow orchestration, approvals, audit, observability and cost controls. That is what lets a public assistant capture a lead safely, or an internal assistant file a document, without a public model ever holding unrestricted access to a CRM, a mailbox or a tenant.
Public-facing assistants are covered in Intelligent Web Platforms. Internal, operations-facing assistants are what the capabilities below describe.
Agents with a job description, a scope and a supervisor.
An agent is only useful when it can act in your systems safely. We build agents against a defined identity, a defined set of permissions and a defined approval boundary.
- Specialised business agents scoped to one process
- Internal assistants over your documents and mail
- Research agents that maintain reference information on a schedule
- Operational agents that prepare work for people
- Customer-facing agents with clear escalation to a person
- Human-in-the-loop agents with approval gates enforced in code
Agents are orchestrated rather than left to roam. A routing layer sends each request to the agent, document workflow or model best suited to it, and the console shows what each one did.
Documents understood by their content, not their filename.
Identity documents, statements, contracts, correspondence and forms arrive out of order and badly named. Document intelligence turns them into classified, extracted, validated records in the right place.
- Document classification from content
- OCR for scanned and photographed documents
- Information extraction into structured metadata
- Document validation against rules and expected values
- Routing to the correct client, matter, deal or library
- Summarisation for human review
- Duplicate detection
- Compliance workflows with an audit trail
Bulk migrations of existing libraries follow the same pipeline as new documents, so a backlog of years can be brought to one naming and filing standard and then kept there.
Remove the repetitive work between your systems.
Automation is an integration problem first. We map the process, choose the right tool for each step and build it so that it can be monitored and changed.
- Workflow discovery and process mapping
- API orchestration across vendors and internal systems
- System integration through Microsoft Graph, Google Workspace, Slack, HubSpot, Salesforce, Xero and other vendor APIs
- Approval workflows with named approvers
- Event-driven automation (arrivals, changes, deadlines)
- Power Automate where it fits the tenant
- Apps Script and Google Workspace APIs where the work lives in Workspace
- n8n for orchestration where a workflow engine is the right tool
- Custom automation in code where the logic needs to be tested and versioned
- MCP servers so agents can use tools with defined scopes
We are deliberate about tool choice. Low-code is used where it is maintainable, and code is used where it is not.
Answers grounded in your own documents and records.
Retrieval over the systems you already have, respecting the permissions that already exist.
- Microsoft 365, SharePoint and OneDrive
- Google Workspace, Drive and Gmail
- Slack, Teams, Notion and Confluence content
- Internal documentation and procedures
- CRM and line-of-business records
- Databases and structured data
- Knowledge retrieval with citations
- Enterprise search across sources
Knowledge systems are permission-aware. A person, or an agent acting for them, only retrieves what they are entitled to see.
Voice that qualifies, schedules and hands over.
Voice interfaces are useful when they are connected to the same operations layer as everything else and know when to stop and hand off.
- Inbound enquiry handling
- Qualification against your criteria
- Scheduling into real calendars
- Client servicing for routine requests
- Internal voice interaction with the operations assistant
- Recording, disclosure and hand-off rules designed in
The part most AI projects skip.
How the system is allowed to behave, who it acts as, what it can reach, what it costs and how you would know if it went wrong.
- Model selection per task and the ability to swap
- Identity: scoped application registrations, not shared accounts
- Access control and least-privilege Graph permissions
- Data boundaries, documented data flows and third-party processing
- Human approvals enforced in workflow code
- Monitoring and observability of tasks, queues and errors
- Cost tracking per task and per day
- Audit of every decision and approval
- AI governance policies your board can read
See the ten principles we build to on the guardrails section of the homepage.
Your AI. Designed around your business.
Mia is one client's assistant, with a name, voice and interfaces chosen by that practice. Every Sigma Labs deployment is shaped around the organisation it serves. What stays constant is the engineering underneath.
The name is yours.
- Name and identity
- Voice and tone
- Interfaces: portal, messaging, voice
- Permissions and data boundaries
- Specialist agents and workflows
- Connected business systems
The architecture is Sigma Labs.
- AI architecture and model selection
- Agent orchestration
- Integrations, APIs and MCP connectivity
- Identity and security boundaries
- Human approval controls
- Audit, cost and observability
Give it an identity. We engineer what sits behind it.
Works with the platforms you already run.
Microsoft is where we go deepest. The same identity-first discipline applies to Google Workspace, Slack, your CRM, finance, support and practice systems, and anything that exposes an API, webhooks or an MCP server. If your team already runs on it, we build around it rather than asking you to move.
- Microsoft 365
- Google Workspace
- Slack
- Microsoft Teams
- Google Meet
- Zoom
- Notion
- Miro
- SharePoint
- OneDrive
- Google Drive
- Google Docs & Sheets
- Dropbox
- Box
- DocuSign
- Adobe Acrobat Sign
- PandaDoc
- Exchange / Outlook
- Gmail & Google Calendar
- Microsoft Bookings
- Calendly
- Twilio
- WhatsApp Business
- Telegram
- Teams Phone
- HubSpot
- Salesforce
- Dynamics 365
- Pipedrive
- Zoho CRM
- Xero
- MYOB
- QuickBooks
- NetSuite
- Business Central
- Stripe
- Square
- Jira
- Confluence
- monday.com
- Asana
- ClickUp
- Trello
- Airtable
- Smartsheet
- Zendesk
- Freshdesk
- Intercom
- Jira Service Management
- Mailchimp
- ActiveCampaign
- Klaviyo
- Typeform
- Jotform
- Microsoft Forms
- Google Forms
- Employment Hero
- BambooHR
- Deputy
- Tanda
- Shopify
- WooCommerce
- BigCommerce
- Power Automate
- n8n
- Zapier
- Make
- Microsoft Graph
- Google APIs & Apps Script
- Slack API
- MCP servers
- REST & GraphQL APIs
- Webhooks
- GitHub
- GitLab
- Azure DevOps
- Azure
- Google Cloud
- Cloudflare
- Entra ID
- Google Cloud Identity
- Okta
- Azure OpenAI
- Anthropic Claude
- OpenAI
- Google Gemini
- Azure SQL
- PostgreSQL
- BigQuery
- Snowflake
- Power BI
- Looker Studio
- Broker & lending CRMs
- Practice management
- Legal matter systems
- Clinical & patient systems
- via their APIs
Not listed? If it has an API, a webhook or an MCP server, we can architect around it. Every integration runs under a scoped identity with the least permission the job needs, and data flows to any third-party platform are designed, documented and agreed with you first.
Straight answers about AI in your business.
The questions owners and operations managers ask before they let an AI system near their documents.
Does the AI send emails or file documents on its own?
Where does our data go?
Which AI models do you use?
Will this replace our staff?
How do we know what it is doing and costing?
Tell us what is consuming your team's time.
We will tell you honestly whether it is an AI problem, an automation problem or an architecture problem, and what it would take to fix.