Key Takeaways
- London-based Riplo exits stealth with a €2.6 million (about £2.3 million) pre-seed round to build an agentic operating system tailored for consulting firms.
- The platform aims to orchestrate AI agents across research, slide-building, and workflow automation, targeting higher-margin, faster project delivery for consultants.
- Funding will support product development, early customer pilots, and go-to-market efforts in the UK and Europe’s professional services market.
- Riplo enters a fast-growing agentic AI segment, competing with emerging specialist OS players rather than general-purpose foundation models.
What Happened?
Riplo, a London-based AI startup, has officially emerged from stealth with a €2.6 million funding round to build what it calls an “agentic operating system for consulting.” The announcement was made via EU-Startups’ coverage and amplified on X, where the publication highlighted Riplo’s vision of transforming how consulting teams research, analyze, and deliver client work using AI-native workflows.
Building an Agentic OS for Consultants
Riplo’s core proposition is an operating system that coordinates multiple specialized AI agents—covering tasks like research synthesis, financial modeling assistance, knowledge retrieval, and slide or deliverable drafting—within a single workspace for consulting teams. Rather than just offering a chat interface, the company is positioning its stack as infrastructure that plugs into existing tools and data sources used by consulting firms, aiming to standardize how AI agents are deployed across engagements. The €2.6 million injection gives Riplo runway to harden its platform, expand engineering and product teams, and run structured pilots with boutique and mid-market consulting firms that are under pressure to deliver more with leaner teams.
The round size places Riplo in the early, experimentation-heavy phase of the agentic OS race, where product-market fit and depth of workflow integration will likely matter more than raw model performance. By focusing on consulting-specific flows—intake, discovery, analysis, and delivery—Riplo can fine-tune agent behaviors, templates, and governance for professional services, an approach that differs from generic agent frameworks targeting any enterprise use case.
Why This Matters for the Market?
Consulting and professional services firms are in the crosshairs of AI disruption, as knowledge work becomes increasingly automatable via AI agents capable of drafting documents, summarizing data, and performing structured analysis. Riplo’s launch reflects a broader shift from standalone AI tools toward orchestration layers that manage many agents and connect them to real enterprise workflows, which is becoming a key battleground for AI startups in 2025–2026. With VC appetite now focused on clear ROI and verticalized AI platforms rather than generic chatbots, an agentic OS tightly scoped to consulting could resonate with firms seeking measurable productivity gains without rebuilding their stack from scratch.
At the same time, the agentic AI landscape in the UK and Europe is getting more crowded, with multiple companies pitching orchestration, copilots, or “AI teams” for enterprise use, including players focused on operations, software engineering, and back-office automation. Riplo’s ability to stand out will likely hinge on how deeply it embeds into consulting workflows—time tracking, knowledge management, proposal generation—and how well it can reassure firms on data security and compliance in a highly sensitive client environment.
Agentic OS Competitors in Consulting
Below is a competitive comparison between Riplo and two emerging agentic AI players with similar vertical or workflow focus: Futuria (agentic AI for enterprise operations, including professional services) and Vstorm (agentic AI engineering and consulting boutique).
Agentic AI Feature Comparison for Consulting OS
| Feature/Metric | Riplo (Consulting OS) | Futuria (Agent Teams) | Vstorm (Agentic AI Boutique) |
| Context Window | Uses underlying LLMs; optimized for case files and project docs; likely medium–large context tuned for consulting use cases. | Depends on chosen models; designed for broader enterprise process data; may support larger operational datasets. | Depends on client-chosen models; typically standard enterprise-scale context windows. |
| Pricing per 1M Tokens | Not publicly disclosed; early-stage pricing likely custom per firm and pilot-based. | Not publicly disclosed; likely enterprise licensing with usage tiers. | Project-based consulting fees rather than direct per-token pricing. |
| Multimodal Support | Likely supports text and document inputs (slides, PDFs), with roadmap for richer multimodal support as LLMs evolve. | Focused on process automation; multimodal depends on integrated models and tools. | Provides multimodal implementations as part of custom projects (e.g., documents, dashboards, APIs). |
| Agentic Capabilities | Core product: orchestrated AI agents for research, analysis, and deliverable generation in consulting workflows. | Provides configurable AI agent teams that automate operational workflows across departments. | Designs and deploys tailor-made agentic systems and RAG pipelines for clients. |
In practical terms, Riplo appears strongest where workflows are explicitly consulting-centric—research sprints, insight decks, and client deliverables—while Futuria is positioned more broadly for enterprise operations and Vstorm for bespoke, project-based builds. For firms seeking a packaged OS tuned to consulting rather than custom engineering, Riplo’s focus could be more cost-effective and faster to adopt than engaging a boutique consultancy or deploying a generic agent platform.
Bayelsawatch Takeaway
In my experience covering AI for professional services, this kind of tightly verticalized agentic OS is one of the clearest near-term ways AI can actually change how consultants work day-to-day. I think this is a big deal because a €2.6 million raise at this stage is enough to build a focused product and prove out ROI with a handful of serious consulting partners, without needing to boil the ocean. For consulting teams drowning in repetitive research and deck-building, Riplo’s approach, if executed well, could be genuinely bullish for productivity and margins rather than just adding another dashboard to their stack. At the same time, I generally prefer to see clear pricing and governance details early on, so I’ll be watching whether Riplo can translate its agentic vision into transparent, enterprise-ready offerings that risk-averse consulting leaders can actually sign off on.
