AIS
An automated intelligence layer deployed into your business in two weeks. Your AI agents, dashboards, and automation run on it from day one.
AIS is the Automated Intelligence System Beyond Data deploys into your business. One governed place for your ERP, CRM, finance, and operational data. A business ontology that knows what your data actually means. Automated data quality, real-time alerting, an AI assistant, dashboards, and a runtime to build and host your own AI apps. Production-ready in two weeks, built on AgileData technology, deployed and supported by Beyond Data.
What usually breaks
Most AI projects fail before the model is written. The reason is almost always the same. The data is scattered across an ERP, a CRM, a finance system, and a long tail of spreadsheets nobody has mapped.
Teams then spend six to twelve months, and a budget north of $400k, building a data platform to pull it together. Then another six to twelve months building the semantic layer, the dashboards, the alerting, and the AI integration on top. Half the time the program stalls. The other half ships something that works, but by then the AI agenda has moved on. The board is asking why nothing is in production yet.
The honest answer is that the foundations were never the headline problem. They were the whole problem.
The capabilities behind this
AIS
The operating layer your AI, automation, and analytics run on. Deployed in two weeks.
Learn moreForward Deployed Engineers
A senior AI engineer embedded inside your organisation for a fixed engagement. They scope, build, and ship production-grade AI.
Learn moreAI and Data Strategy
A prioritised AI agenda anchored in business outcomes, with the data foundations assessment to make it real.
Learn moreWhat changes once this is in
Production intelligence in weeks, not years
AIS is live in your business in two weeks from kickoff, with your first source systems connected and the AI assistant answering questions. Bringing every system in is a separate, scoped piece of work. AIS is ready to read from on day one. Every downstream AI, automation, and analytics engagement ships faster because the layer is already there.
AI, automation, and analytics on one foundation
Every AI agent, automated workflow, and decision dashboard reads from the same governed layer. No bespoke integration work. No three-month delay between use case and usable data.
Enterprise-grade governance by default
Role-based access, encryption at rest and in transit, and audit trails arrive switched on. Australian Privacy Principles are covered from the first load.
Visible cost, no runaway bills
Cost observability is built in. You see what storage, compute, and pipelines are spending, by workload, before it becomes a surprise invoice.
The infrastructure beneath AIS
Six layers, all pre-configured. Data sources, ingestion and exchange, the data platform, the consumption layer, the application and control layer, and the operations layer underneath it all. Every layer arrives standing up. Nothing for your team to architect, deploy, or run.
The modelling layer
The modelling layer is where AIS understands your business. The data map, catalog, change rules, data tiles, context layer, and trust rules turn raw source data into a governed, queryable model. The AI chat assistant, notifications, monitoring, and machine learning all read from the same model, so every output is aligned to the same definitions.
What's built in and what you'd otherwise have to wire yourself
| Capability | What that means |
|---|---|
| Pre-configured storage and compute | AIS is provisioned and tuned before you arrive. No architecture debate, no month of Terraform. |
| Automated pipelines with monitoring | Pipelines run themselves. When something breaks AIS tells you, with enough context to act. |
| Business ontology | Customers, accounts, products, and transactions defined once so every AI, automation, and analytics layer above reads the same truth. |
| AI assistant out of the box | Your team queries the business in plain English from day one. |
| AI app runtime | Build and host your own AI apps on top of AIS, no separate infrastructure to stand up. |
| Dashboards and real-time alerting | Decision dashboards and alerts when something needs attention, both built in. |
| Automated data quality | Quality rules run automatically. Issues surface before someone notices in a dashboard. |
| Role-based access built in | Access mirrors your org chart from day one. Finance sees finance, ops sees ops, the board sees what the board should. |
| Encryption at rest and in transit | Data is encrypted on the way in, on the way out, and while it sits. No configuration required from your team. |
| Australian Privacy Principles ready | Controls, retention, and audit trails are aligned to APP obligations before any customer record lands. |
| MCP connector for AI agents | A standard endpoint your AI agents, copilots, and internal tools all read from. One integration, not ten. |
| Cost observability by default | Spend is attributed to workloads and visible in a dashboard. No mystery cloud bills at the end of the quarter. |
| Senior-led support included | The same engineers who deployed AIS support it. No ticket queue, no tier-one hand-offs. |
If you built this yourself
Six to twelve months and $400k to $800k for the data platform. Another six to twelve months for the ontology, the AI assistant, the dashboards, the alerting, and the AI app runtime on top. An ops burden that does not go away. That is the honest number most mid-market CIOs get when they price building this end-to-end. AIS is the alternative. The infrastructure, the ontology, the assistant, the dashboards, and the alerting all arrive pre-configured, governed, and supported. You are buying a finished system, not funding a build.
How this lands inside your operation
Your finance data lives in Xero or SAP. Sales lives in HubSpot or Dynamics. Operations runs on an ERP your team half-trusts, and the numbers that actually get to the board come out of a shared drive full of workbooks. Every AI pilot you have tried has stalled on integration.
AIS replaces that wait. We connect the source systems, land the data into a governed layer, configure the ontology against your business, switch on the AI assistant, stand up the dashboards and alerts, and expose a single MCP endpoint that AI agents, BI tools, and analysts all read from. Your internal team keeps doing their job. The foundations stop being the blocker.
This is for you if…
- CIOs, CTOs, COOs, and Heads of Data at mid-market Australian businesses, typically $20M to $500M revenue
- Teams with data spread across an ERP, CRM, finance system, and spreadsheets
- Organisations that have been quoted six to twelve months for a platform build and want production in weeks
- Businesses where AI, automation, and analytics keep stalling on data integration, not the model
- Leaders who want enterprise-grade governance without an enterprise-grade build
This probably isn't the right fit if…
- Organisations with a mature data and AI platform already running and staffed
- Businesses wanting a fully custom, greenfield architecture. AIS is standardised by design
Frequently asked
You might also care about
Embedded Build Team
A senior AI engineer inside your business in weeks, shipping production-grade from day one.
10 weeks
from scope call to production
AI Readiness & Strategy
A funded AI plan in 6 to 10 weeks, not 6 to 10 months, for leaders who want direction before delivery.
6-10 weeks
board question answered with a funded plan
Let's talk about AIS.
30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.