Four specialists. One intelligence ecosystem.
Each agent is named after someone who made a living out of one kind of thinking, and each one is deliberately narrow: a product in its own right, not a mode of a general assistant. They share the UI4AI layer, so they share identity, permissions, memory and audit.
Marie holds the whole employee record
Marie is an AI-powered talent operating system. Six modules sit on one employee record — core HR, talent acquisition, workforce, rewards, performance and analytics — and Marie reads across all of them rather than inside one.
- →Core HR: one employee record, one org structure, one set of business processes that everything else reads from
- →Talent acquisition: branded careers, AI-drafted roles, evidence-based evaluation, offers, onboarding and offboarding as one journey
- →Workforce and rewards: time, absence, compensation, benefits and payroll held to the same record and the same approvals
- →People analytics: a discovery board that asks questions spanning every module, and answers with the evidence
Albert turns an app idea into a brief you can build from
Albert is the architect between “I have an app idea” and “here is the specification your team can build”. Describe it in plain English and he sizes it, prices it, draws it, and sends it for approval — before anyone writes a line of code.
- →App vision: four plain questions, then a proposed size and three generated screen directions
- →Plans: XS to XXL, each with its own epics, personas and journeys — move the size and every number follows
- →Review: a price with its arithmetic attached, one-time against monthly, hosting by real user band
- →Approvals: a secure expiring link, no account needed, every decision timestamped with notes
Isaac learns the brand, then runs the campaign
Isaac is an AI social catalyst. It reads your site into a Brand DNA, plans the campaign, writes every post for every channel, moves it through your approvals and publishes natively — then reads the results and comes back with the next move.
- →Brand analysis: reads every reachable page and resolves it into one Brand DNA, each field carrying its confidence and evidence
- →Plans before it writes: one sentence becomes a campaign arc, then captions, hashtags and images for every channel
- →Respects your sign-off: roles enforced at every gate, so the person who edits is never the person who approves
- →Ships and keeps watch: scheduled to the minute and published natively to fourteen destinations, with retry and rollback
Amelia works the pipeline around the clock
Amelia is an agentic AI sales agent. She combines marketing automation, multi-channel outreach and deep product knowledge to engage leads 24/7, in their own language, with conversations that convert.
- →Lead generation: AI-powered harvesting, qualification and segmentation, so only high-value prospects reach your team
- →Multi-channel outreach: email, WhatsApp, social and telemarketing run from one platform
- →Product expertise: fine-tuned to your catalogue, so it handles complex queries like an expert, in dozens of languages
- →Human in the loop: approval gates and hand-off rules keep your team in control of what the automation does
Why four narrow agents beat one that claims everything
Scoped, so testable
A narrow agent has a narrow evaluation set. You can prove it got better, which you cannot do with a general assistant.
Shared memory
One context store behind the layer, so what one agent establishes is already there when the next one opens the question.
Clean hand-offs
Structured outputs pass between agents, so nothing is re-derived and nothing is quietly lost in a summary.
Engines swap underneath
Each agent runs on whichever model suits its work today. When that changes, the agent stays and the engine goes.