Mibba vs Quillio

Mibba

AI coworker for French notarial offices, automating workflows while teams stay in control.

Mibba is an AI coworker built specifically for French notarial offices. It helps notaries, clerks and support teams reduce repetitive administrative work without replacing their existing practice-management or CRM systems. Mibba connects the information already spread across matters, emails and documents, then prepares useful work for the team: summarising a matter, identifying missing information, tracking deadlines and pending actions, preparing client follow-ups, organising document-related tasks and surfacing clients who need attention. Every proposed action remains subject to human review and validation. The product is designed for the day-to-day reality of notarial practices, particularly teams of 10 to 30 people handling many active matters in parallel. Instead of acting as a generic chatbot, Mibba maintains the context of each matter and shows the sources behind the information it uses. This helps collaborators resume a file faster, coordinate consistently and spend more time on legal analysis, client advice and relationship-building. Mibba is delivered as a web-based paid subscription with pricing tailored to the roles equipped and the scope selected by each office. Customer data is hosted in France, isolated between customers and never used to train AI models. The platform is built to complement the software already used by the office, so teams can introduce AI-supported workflows without disrupting established processes.

Quillio

AI legal assistant built for Australian lawyers

Quillio is an AI legal assistant built specifically for Australian law firms, handling document review, chronologies, legal research, first-draft correspondence, and file notes so that lawyers spend their hours on analysis and advice rather than administrative work. The problem it targets is a capacity problem rather than a technology one. A mid-level lawyer is expensive and slow to hire, and much of what firms pay that salary for is not legal thinking - it is reading discovery, assembling chronologies, writing file notes, and producing first drafts that a partner will rewrite anyway. Lawyers routinely work far more hours than they can bill, and the gap is filled by exactly this kind of work. Quillio's pitch is that a firm can add senior-level output across a practice area without a recruiter, a six-month search, or a counter-offer war. Two things distinguish it from generic AI writing tools. The first is jurisdiction: it is built for Australian practice and Australian law, with data sovereignty, which matters because legal work is jurisdiction-bound and because client confidentiality obligations make where data is processed a professional issue rather than a preference. The second is practice-area depth - the product is organised around specific areas including family, criminal, commercial, employment, property, civil litigation, wills and estates, immigration, personal injury, and conveyancing, rather than offering one general-purpose assistant and leaving the specialisation to the prompt. It runs on a free trial, and the company is built by lawyers rather than by a general AI vendor moving into legal, which shows in how the workflows are shaped around the artefacts a matter actually produces. The comparison set splits in two. Against the incumbent legal research platforms, Quillio is narrower and workflow-focused rather than a citator and case database. Against general-purpose AI assistants, the trade is obvious: a general model is cheaper and more flexible but has no jurisdictional grounding, no matter structure, and no answer to the data-residency question a professional indemnity insurer will eventually ask. Against simply hiring, it is a capacity decision made at a fraction of the cost and none of the lead time. It is a good fit for small and mid-sized Australian firms where administrative load, not demand, is the constraint on how many matters the practice can take. It is a poor fit for firms outside Australia, and for work where the bottleneck is genuinely judgement rather than throughput.