AiNinjas
AI Beyond Halo

Not every AI problem lives in a service desk.

Agents, integrations, MCP tooling, retrieval systems and bespoke applications. The engineering work we do when the problem is not Halo-shaped.

Start a conversation Running AI on Halo? Start here
Not Halo-shaped

Three problems that arrive without a ticket.

Knowledge

The answer exists in three systems and nobody can find it in time.

Tooling

An agent needs to act in your systems, not just talk about them.

Integration

The data that would make AI useful sits behind an old interface.

What we build.

Six ways to engage us on work that sits outside the Halo lifecycle. Most projects start with one and grow into two or three.

Workflow AI Automation

AI applied to the operational workflows that actually run.

Automated routing and classification, enrichment before work reaches the next team, and the checks that keep a human in the loop where it matters.

Knowledge and Retrieval

Your existing knowledge, grounded and usable in context.

Retrieval over runbooks, prior tickets and internal documents, surfaced at the moment someone needs it, with sources attached so answers can be checked.

Agents and MCP Tooling

Agents that reason, use tools, act within limits and hand back to people.

Agent design, tool and MCP server development, evaluation harnesses, and the authority model that decides what an agent is allowed to do.

Enterprise AI Integration

AI connected to the systems your organisation already runs on.

Identity, ITSM, data and observability wired together so AI works with real operational data rather than sitting beside it as a separate tool.

Bespoke AI Applications

Prototypes and production applications built around a specific problem.

From a working prototype in weeks to a supported application, including the evaluation and monitoring needed to trust what it produces.

Managed AI Operations

AI run as a production discipline, governed and measured.

Oversight of behaviour, prompt and model changes, data boundaries and operational metrics. For AI services running on Halo, this is delivered as AI Service Assurance inside the lifecycle.

We use OpenAI, Anthropic, AWS Bedrock or others where the workflow has a real reason. We are not model-led.

A worked example

What AI looks like across a work lifecycle.

The detail varies by environment, but the shape of a well-applied AI workflow tends to look like this.

01
Intake

Classification, priority and intent detected as work arrives.

02
Context

Relevant knowledge, similar past cases and asset context retrieved automatically.

03
Routing

Sent to the right team or workflow based on content and operational rules.

04
Resolution

A draft response and suggested next steps surfaced for review.

AI runs alongside your people, not in place of them.Human in the loop at every stage.
Today, typically
  • Work waits in a queue until a person reads it
  • People search a knowledge base that is often out of date
  • Recurring issues are solved manually each time they appear
With AI applied operationally
  • Work arrives already classified, prioritised and contextualised
  • People see grounded answers from live knowledge, in context
  • Recurring patterns are handled by workflows that people review

Illustrative. Actual implementation is scoped to each environment.

Tell us what you are trying to build.

A briefing, not a pitch. We will tell you where AI is worth the effort, and where it is not.

Start a conversation Take the scorecard