Background
The way customers discover brands is shifting from a list of blue links to a synthesised answer. ChatGPT, Perplexity, Grok and Gemini increasingly answer in place of returning sources. For B2B brands, the question is no longer where do we rank, but how are we written about by the models that now mediate the buying decision.
The brief
Build a B2B platform that gives marketing and revenue teams a clear, measurable view of how their brand surfaces inside generative engines. Track visibility across LLMs, decode why answers look the way they look, and turn that signal into a concrete content and positioning playbook.
The solution
Altriix. A generative engine optimization platform built end to end by B One. Brand identity, product strategy, UX, system architecture, LLM orchestration and front-end engineering, all designed and shipped under one roof, for a category still defining itself.
The product
Four questions, one platform.
/01
How visible is the brand?
A live measure of how often the brand appears inside generative answers, against the prompts that matter for revenue, across every model that customers actually use.
/02
What is being said?
Sentiment, framing and positioning extracted prompt by prompt, with the sources the models pulled from, so marketing teams see exactly which signals shaped the answer.
/03
Where are the gaps?
Topics where competitors are quoted and the brand is not, mapped to content and PR opportunities. The gap report becomes the editorial roadmap.
/04
What moves the needle?
Recommendations that connect a content action to a measurable lift in answer presence, validated through repeated prompt sampling, not vanity metrics.
The stack
Built on the engines that ship the answers.
A multi-model LLM layer.
ChatGPT, Claude, Gemini, Perplexity and Grok queried in parallel through a unified gateway. Same prompt, same context, every model. The platform sees the answer surface as the customer sees it, not as a single vendor frames it.
A live web signal layer.
Bright Data feeds the platform with real, current web content at scale. Models do not invent. They cite. We index what is actually cited, where it sits, and how often, so the picture is grounded in observable evidence.
A semantic retrieval layer.
Embeddings and vector search compress thousands of brand mentions into a coherent map of how the brand is talked about. Clustering surfaces the themes humans would never read line by line.
An agent orchestration layer.
Specialised agents handle scoring, drift detection, gap mining and recommendation drafting. Each agent has a defined role, an evaluation harness and a confidence score. Nothing reaches the user without passing checks.
The architecture
A multi-tenant SaaS built for the AI search era.
Altriix runs as a multi-tenant cloud platform. Each B2B account gets its own workspace, its own prompt graph, its own corpus. The runtime keeps tenants isolated, the model layer keeps cost predictable, and the data layer keeps refresh continuous.
01
Workspace isolation.
02
Streaming refresh.
03
Cost controls.
04
Evaluation harness.
05
Observability by default.
Shipped end to end
From naming to production.
Brand identity.
Product strategy.
UX research.
Product design.
System architecture.
LLM orchestration.
Data pipelines.
Front-end and back-end.
Cloud infrastructure.
Launch and GTM.
All work
Back to selected work.Two engines
Strategy meets execution.
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