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Dataloop
Agentic AI

Agentic AI for safety and operations

AI agents built for HSE, incident investigation, risk management, and operational workflows. Deployed inside your existing cloud tenant. Your data stays within your security boundary.

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Connected AI agent network
The difference

Built in your world, not ours

Most AI tools in this space work the same way. Your data leaves your organisation and goes to a third-party platform. Their models process it on their infrastructure. You are trusting an external provider with your incident records, risk assessments, witness statements, and safety data.

We do it differently. We build AI agents that are deployed inside your existing cloud tenant. Your data is governed by your organisation’s existing security policies, permissions, and compliance settings. Nothing goes to a separate third-party AI platform.

For mining companies and government organisations, this matters. Incident investigation data, safety records, and risk assessments are sensitive. In mining, this data can be legally discoverable. In government, it is subject to sovereignty requirements. Sending it to an external platform introduces risk that many organisations are not comfortable with.

With Data Loop, there is no external platform to subscribe to. We build the agents, deploy them inside your tenant, train your team to use them, and hand them over. The agents are yours. They run within your existing enterprise security boundary, governed by your existing policies.

Typical AI platforms

Their model

  • Your data goes to their platform
  • You subscribe to their service
  • Their infrastructure, their security policies
  • Vendor dependency for ongoing access
  • Generic AI applied to your context
How we work
Data Loop

Our model

  • Your data stays within your existing cloud tenant
  • You own the agents outright
  • Your infrastructure, your security policies
  • Independent operation after handover
  • Agents built specifically for your workflows
HSE AI agents

AI agents for health, safety, and environment

Specific agents built for the workflows mining, government, and heavy industry teams run every day. None of them are generic LLM wrappers. All of them are built inside your environment.

Agent 1

Incident investigation agents

Agents that support incident investigation workflows from end to end. They ingest witness statements, photos, documents, and system records, then structure the evidence into a coherent investigation narrative.

  • Identify contributing factors and flag patterns across historical incidents
  • Draft investigation reports that meet regulatory standards
  • Human-in-the-loop at every step. The agent structures the work, the investigator signs off
  • Raises consistency across experience levels. A junior investigator produces work closer to what a senior would deliver
  • Every AI-generated suggestion links back to source material for full traceability

Agent 2

Risk and critical control agents

Agents that help safety teams maintain and verify critical controls across sites, particularly useful for organisations running multiple operations where consistency is a constant challenge.

  • Review risk assessments against current controls and flag gaps or drift
  • Standardise what "good" looks like for each critical control across sites
  • Process existing risk documentation, control descriptions, and verification records
  • Identify where standards are slipping before an audit or incident surfaces the gap

Agent 3

Safety management system agents

Agents that continuously check alignment between safety management system documents (procedures, standards, risk assessments) and on-the-ground activities. SMS and SHMS alignment without the manual audit cycle.

  • Monitor for misalignment between documented procedures and actual practice
  • Flag documents that need updating before audits or regulatory reviews
  • Particularly valuable when preparing for site inspections or regulator engagement
  • Helps procedures stay live documents instead of paperwork that drifts over time

Agent 4

Safety data analysis agents

Agents that analyse incident data, near-miss reports, hazard observations, and audit findings to surface trends, patterns, and emerging risks. Connects to existing HSE platforms like Evotix to draw on the data already in your system.

  • Natural language queries: ask "what are the most common contributing factors in vehicle incidents in the last 12 months" and get answers drawn from your own data
  • Generates insights that go beyond what built-in HSE reporting can produce
  • Complements the Power BI dashboards and data warehouses described on our analytics page
  • AI surfaces the insight, the dashboards make it visible to the whole organisation
Beyond HSE

Not just safety. Any operational workflow.

HSE is where we go deepest. The methodology is the same regardless of the domain: understand the workflow, identify where AI adds value, build and deploy inside your environment, train your team, hand over.

Other areas where we build agents: document processing and extraction, automated reporting, knowledge base Q&A from internal documents, intelligent routing and triage of incoming requests, and analysis of large unstructured datasets.

We work across mining and resources, government, financial services, utilities, and enterprise technology.

If you have a repetitive, data-heavy workflow that is eating your team’s time, there is probably an agent for it. The conversation starts with showing us the workflow, not picking a model.

Many of our AI engagements run alongside data warehouse and Power BI work. The agents surface insight from the data, the dashboards make it visible to the rest of the organisation. See our analytics page for how the two connect.

How we deliver

How we build and deploy

Five steps that hold across every AI engagement we run.

01

Map the workflow

We start by understanding the specific workflow you want to improve. What are the inputs? What decisions get made? Where are the bottlenecks? We do not start with technology. We start with your process.

02

Design the agent

We design an AI agent tailored to that workflow: what data it ingests, what it produces, where humans review and approve, and how it integrates with your existing systems.

03

Build and test

We build the agent and deploy it inside your environment. We test it with real data and edge cases. We iterate until it works reliably in production conditions, not just in a demo.

04

Train and hand over

We train your team to use the agent, manage it, and understand its outputs. We document everything. The system is yours to run independently.

05

Support and evolve

Most clients keep us involved on a retainer because the work evolves. New workflows, new data sources, new requirements. We stay as long as you need us.

Security and trust

Security is not a feature.
It is the architecture.

The most common question we hear from enterprise clients considering AI is not “what can it do?” It is “where does our data go?” That is the right question. Incident records, witness statements, risk assessments, safety observations: this is sensitive operational data. In mining, it can be legally discoverable. In government, it is subject to sovereignty requirements. It should not be leaving your organisation to reach someone else’s platform.

Deployed inside your cloud tenant

Every AI agent we build is deployed within your organisation's existing cloud tenant. Your data stays within your enterprise security boundary, governed by the same security policies, permissions, and compliance settings your organisation already has in place. Nothing is sent to a separate third-party AI platform.

Your security policies govern the data

The agents operate under your existing access controls, data governance, and compliance configurations. We do not create a separate security boundary. We work within yours. Your IT and security teams retain full visibility and control over how data is accessed and processed.

No third-party platform dependency

There is no external AI platform to subscribe to. No separate login, no separate data store, no separate vendor relationship to manage. We build the agents, deploy them inside your tenant, and hand them over. You are not renting access to our system. You own the agents.

Human-in-the-loop by design

Every AI output is reviewed and approved by your people before it becomes a record. The agent structures the work and surfaces connections. Your investigators, safety managers, and risk teams make the decisions. The AI does not make findings. People do.

Audit-ready traceability

Full chain from input to output. Every AI-generated recommendation or summary links back to the source evidence: the witness statement, the document, the data record. Built for regulatory scrutiny, internal audit, and legal review.

Enterprise compliance alignment

The agents run within your existing enterprise cloud compliance framework, including data residency, encryption at rest and in transit, and identity management. We do not require special access or admin privileges once the system is handed over.

Responsible AI practices

We follow the principles set out on our responsible AI page. We test for bias, build in graceful failure handling, and do not deploy AI systems that operate without human oversight. Read it.

We built our AI practice this way because enterprise organisations told us this is what they need. Not a platform they subscribe to. Not a system where their data leaves the building. Agents deployed inside their own tenant, governed by their own policies, owned by them.

See what an agent could do for your team

Whether it is incident investigation, risk management, or an operational workflow we have not seen yet, the conversation starts the same way.

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