'%3e%3ccircle%20cx='500'%20cy='530'%20r='315'%20fill='url(%23g)'/%3e%3cpath%20d='M707%20254c66-44%20121-103%20151-177-85%2033-147%2079-197%20141%2020%207%2035%2018%2046%2036Z'%20fill='url(%23g)'/%3e%3cpath%20d='M716%20310c73-14%20131-2%20178%2039-73%2022-133%2017-181-13%205-8%206-16%203-26Z'%20fill='url(%23g)'/%3e%3crect%20x='251'%20y='417'%20width='496'%20height='210'%20rx='105'%20fill='%23100A1F'/%3e%3ccircle%20cx='393'%20cy='522'%20r='34'%20fill='%23F9F7FF'/%3e%3ccircle%20cx='605'%20cy='522'%20r='34'%20fill='%23F9F7FF'/%3e%3ccircle%20cx='388'%20cy='520'%20r='10'%20fill='%23100A1F'%20opacity='0.35'/%3e%3ccircle%20cx='600'%20cy='520'%20r='10'%20fill='%23100A1F'%20opacity='0.35'/%3e%3cpath%20d='M265%20680c49%2088%20126%20133%20231%20136%20107%204%20190-38%20249-126'%20fill='none'%20stroke='%23100A1F'%20stroke-width='32'%20stroke-linecap='round'%20opacity='0.28'/%3e%3c/g%3e%3cg%20fill='none'%20stroke='%23A967FF'%20stroke-width='22'%20stroke-linecap='round'%20stroke-linejoin='round'%3e%3cpath%20d='M167%20338h-56v-62'/%3e%3ccircle%20cx='111'%20cy='276'%20r='22'%20fill='%230E0B1C'%20stroke='%23A967FF'/%3e%3cpath%20d='M842%20417h65v-70'/%3e%3ccircle%20cx='907'%20cy='347'%20r='22'%20fill='%230E0B1C'%20stroke='%23A967FF'/%3e%3cpath%20d='M817%20662h80v73'/%3e%3ccircle%20cx='897'%20cy='735'%20r='22'%20fill='%230E0B1C'%20stroke='%23A967FF'/%3e%3cpath%20d='M172%20649h-71v71'/%3e%3ccircle%20cx='101'%20cy='720'%20r='22'%20fill='%230E0B1C'%20stroke='%23A967FF'/%3e%3c/g%3e%3c/svg%3e)
AI Ninjasby HaloNinjasName it
A real problem we keep meeting in operations, named precisely enough to test.
Products and Labs
We build our own products too.
AI Ninjas Labs explores problems that exist because AI is changing how people discover, decide, work and buy. Ideas are tested on real cases. When one proves itself, it becomes a product.
Product 01 is live. Product 02 is in validation.
Recommendation Readiness
Report · illustrative“Which firms should we shortlist to implement a service desk platform in Scotland?”
- Discover
80Found by four of five assistants when asked about what it does. - Understand
60Described as “IT support”, not as a platform specialist. - Evidence
35No third-party proof an assistant would cite: no case studies, no reviews. - Recommend
20Named in one shortlist. Three competitors named in four.
Fix firstPublish two named case studies with outcomes. Assistants recommend what they can evidence.
Illustrative report. Scores and findings are examples.
Product 01
When a buyer asks AI who they should hire, are you in the answer?
Buyers now ask an assistant for a shortlist before they search. Recommendation Readiness measures whether AI assistants can discover your company, understand what you do, find credible evidence and confidently recommend you, and tells you what to fix first.
Not an audit. A simulation.
- DiscoverCan assistants find you at all when asked about what you do?
- UnderstandDo they describe you the way you would describe yourself?
- EvidenceCan they find credible, third-party proof to cite?
- RecommendWhen asked for a shortlist, are you on it?
How a lab idea becomes a product.
Recommendation Readiness is product 01 because it survived this process. Product 02 is in it now.
Build it fast
A working version, used on real cases, not demoed on slides.
Let outsiders in
People outside the building use it and come back. If they do not, it stops here.
Harden and sell
Supported and sold as a product with its own roadmap.
| Product | Stage | Status |
|---|---|---|
| Recommendation Readiness | Productise | Live, with its own roadmap. Explore it. |
| Product 02 | Validate | In use outside the building. Named when it survives. |
Ideas that do not survive Validate are not listed. That is the point.
Why a service company builds products.
Because the problems arrive in operations first, and we are already there.
Named precisely enough to test
Labs starts from a problem we keep meeting in live operations, not from a whiteboard. If it cannot be named precisely, it cannot be tested, and it does not get built.
Used before it is sold
Our own service desk and our own agents are the first users. A product that does not survive our operations does not reach yours.
Governed like everything else
Products run under the same authority, change and assurance discipline as the AI services we run for clients. What Labs learns goes back into the lifecycle and the engineering.
Questions we get asked about the products.
What is Recommendation Readiness?
A measure of whether AI assistants can discover your company, understand what you do, find credible evidence and confidently recommend you, with a prioritised list of what to fix first.
Who is it for?
Any firm whose buyers ask an assistant before they ask a colleague: professional services, consultancies, managed service providers and agencies.
Is it tied to Halo?
No. Recommendation Readiness is a standalone product. It has nothing to do with service management, which is rather the point of Labs.
What is product 02?
In validation. People outside the building are using it. We name products when they survive that stage, not before.
Can we bring Labs a problem?
Yes. The best ones are problems you keep meeting in operations that AI has created or could solve, named precisely enough to test.
How do you decide when to stop?
If people outside the building do not use a prototype and come back to it, it stops at Validate. Most ideas do. The ones that survive earn a roadmap.
Bring us a problem that keeps coming back.
The best Labs ideas arrive from operations, not whiteboards. If you keep meeting a problem that AI has created or could solve, tell us what it is and where it shows up.
Or go straight to Recommendation Readiness.
AI Ninjas Labs
Talk to Labs.
Bring the problem, where it appears and what you have already tried.
Start a conversation