Richard Propst

The #CPQ game is about to be elevated. It was about time ... A week ago, Thoma Bravo announced the acquisition of PROS, My immediate reaction was that there are good synergies, for example with Conga https://lnkd.in/gEBpSKgV And here we are. Conga announces the acquisition of the #B2B part of the PROS business. This changes the #CPQ game drastically. It will morph from Configure - Price - Quote to Configure - OPTiMIZE Price - Quote. Can we call the emerging capability Revenue Lifecycle Management? At least, one can say that Conga + PROS offers a broader and deeper toolkit across the revenue lifecycle, from configuration and pricing to quoting, contracting, up to post-quote execution. MyPoV 👉 Great move that will show benefits for both PROS and Conga B2B customers. For their customers? Let's see. 👉 This merger will surely spur some activity from enterprise #CRM vendors, #ERP vendors as well as pricing specialists. Especially the latter should feel some more heat. 👉 It will be interesting to see where the other piece of the fillet that PROS is will move. On the other hand, it is a bit sad to see a great company like PROS being filleted like this. Stay tuned for more. https://lnkd.in/gSJNFTke https://lnkd.in/gHyXM-vv

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What's the MACH Alliance for? It's a question I've been asked a great deal and to take it back to first principals it was to promote architectures (unashamedly technical) to support customer choice, excellent user experience and interoperability between best of breed vendors. Has that worked? That's a debate that can last hours - the good MACH projects, the ones on stage at the various MACH events show that it absolutely can work. Have there been horror stories - of course, plus ça change in IT. However, one thing that has been announced and now released is the MACH Common Data Model. Search my LinkedIn and you'll find a link to a (now unavailable) blog I wrote in 2019 (I think; Stacey Trimnell-Ritchard remembers it I hope as she said she used it in a marketing conversation back then so it did happen 🤣) calling for this and so it's brilliant to see it now exists: https://lnkd.in/eTFVgYKX What is it? In their own words: "The MACH Alliance Open Data Model (ODM) initiative is the natural next step in enabling interoperability across composable ecosystems. It serves as a semantic interoperability layer: a "Rosetta Stone" for understanding how to translate data between systems in hybrid commerce architectures. It provides canonical reference models and implementation guidance that enable architects to design effective data integration across commerce platforms while preparing organizations for AI-powered ecosystems." I'm interested and signed up and I know we're looking at how we can incorporate the ideas into Patchworks (the platform already supports this of course and it will help our AI mapping tools no end). Find out more by attending MACH X in October: https://lnkd.in/epHx2JVp

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Today marks Six months in as SOCPAC CTO, and it somehow feels like both yesterday and six years ago. When I stepped into this role, we were behind the curve. Legacy systems, siloed data, and slow processes held us back. In just half a year, we’ve laid a Foundational Technical Architecture that gave us a modern backbone for data, AI, and autonomy. Since then, we’ve: 1. Unified our digital foundation for secure, scalable operations 2. Embedded AI and LLMs into daily workflows to speed decisions 3. Deployed containerized AI agents across all networks 4. Integrated autonomy at the edge, moving toward true man–machine teaming 5. Shifted our culture from cautious adoption to championing innovation The result? We’re no longer catching up, we’re setting the pace. What once felt like a distant goal is now part of our daily reality. All of this happened in six months, which is why it feels so surreal. It took countless late nights, cross-functional sprints, and the relentless dedication of an amazing team. I'm humbled by the trust the organization placed in me to drive this transformation, and I'm grateful to every person who rolled up their sleeves to make it real. This was truly a team effort, from the junior analysts using our new AI tools to the senior leaders who empowered us to take bold risks. And we’re just getting started. The foundation is in place, but now we get to build on it. In the next chapter, we'll be refining and scaling these capabilities, drilling deeper into AI-enhanced decision-making, and pushing the envelope on autonomous operations. The technology will keep evolving, and so will we. Our goal remains the same: give our operators a decisive edge, making it an extension of the warfighter and keep innovating responsibly. I’m humbled by what the team accomplished in such a short time, and excited for what’s next. The foundation is in place. Now we build on it. Six months down, and the journey is just beginning.

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🚀 CPQ. Reimagined. AI Agents that understand context, talk to each other, and engineer revenue transformation. The best engineering problems aren’t just technical — they’re systemic. We built Everstage to fix incentive compensation because we saw how broken it was. Manual spreadsheets. Calculation errors. Zero visibility into what actually drives behavior. But incentives are just one part of the revenue engine. We realized that when your CPQ is disconnected from comp, you’re optimizing one lever while the other pulls in a different direction. So we reimagined CPQ from the ground up — not as another quoting tool, but as an AI Agent that understands your business context. Everstage CPQ automatically creates quotes for reps by understanding context from call transcripts, Slack and Teams conversations, CRM data, and every connected system. It’s the missing link in the revenue loop — connecting pricing decisions at the point of quote with the incentive structures that drive rep behavior. Behind the scenes, there are two intelligent agents at work: 🧠 The CPQ Agent, optimizing quotes within pricing guardrails. 💡 The Incentive Agent, aligning those quotes with commission outcomes. Together, they talk, negotiate, and find balance — creating a seamless, intelligent loop that drives both revenue efficiency and rep motivation. This synergy was the missing piece. Read more here - https://lnkd.in/gJ5T4-Cb

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In 2025, you don't need to be paying $5 Million to MuleSoft (I'd recommend saving it for a "Temporary Spokesperson," if you catch my drift 😉) What's in an Integration Platform? 1) An event bus 2) An API Gateway for ingress, auth, and ratelimiting 3) Data transformations 4) A runtime for services and code 5) A workflow engine 6) Connectors to get data in and out of other systems. With MuleSoft, you get all of that, but it's Proprietary, heavyweight, expensive, hard to learn, and did I say expensive? There's a much better stack: ** Event Bus ** Apache Kafka (or Confluent!) with a schema registry such as ApiCurio. If you really need to lock it down, most advanced cases can be solved with Kroxylicious. ** API Gateway ** Most of this can be solved easily with the K8s Gateway API, Keycloak, and Quarkus / Spring / Golang Gin / equivalent. LittleHorse also has an upcoming "LittleHorse Gateway" product which receives webhooks and turns them into actionable streams in Apache Kafka, complete with lineage and schemas. ** Service Runtime ** Kubernetes has solved the problem of "I have 10 physical servers, please run these 43 applications on them." Why would you code against MuleSoft's proprietary runtime? LittleHorse has an **Open-Source** framework for running apps (LH Runtime) as well as an enterprise-ready management plane through their Kubernetes Operator that gives equivalent functionality at a more lightweight footprint, learning curve, and price. ** Workflow Engine ** LittleHorse's core DNA is a workflow company. It's the most developer-friendly workflow engine which also gives you access to fresh data to be used in analytics. ** Connectors ** Apache Kafka Connectors and LittleHorse Task Workers can do everything that MuleSoft's proprietary connectors can do. If you're ready to move to a more open platform with drastically lower price tags, higher performance, and a more modern architecture, give my team a shout. We'd be happy to help.

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Talking to a large enterprise that is deploying Rovo in production and they have a precise and thoughtful graduation metric set they used to promote pilots to production: 1. Reduce cycle time by 50% for specific use cases defined : examples were bug triage, ticket resolution for faqs etc 2. 80% accuracy of documents and objects returned in first shot with search & chat 3. Daily active usage by 100% of trial cohort members Enterprise AI deployment is complex with sophisticated security gates and the ROI needs to be clear and tangible while capturing user attention. Very gratifying to see Rovo continue to meet and beat the high bar.

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I’ve been asked a few times lately: “𝐇𝐨𝐰 𝐝𝐨 𝐀2𝐀 𝐨𝐫 𝐌𝐂𝐏 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐭𝐢𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐋𝐋𝐌? 𝐃𝐨𝐞𝐬 𝐭𝐡𝐞 𝐋𝐋𝐌 𝐢𝐭𝐬𝐞𝐥𝐟 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞 𝐜𝐚𝐥𝐥?” Here’s the quick breakdown 👇 ➡️ 𝐇𝐨𝐰 𝐋𝐋𝐌𝐬 𝐬𝐞𝐞 𝐭𝐨𝐨𝐥𝐬: When you call the model’s API (e.g. OpenAI), you send: * the system prompt * the user prompt * a list of tools (𝘨𝘦𝘯𝘦𝘳𝘪𝘤 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴 with descriptions + parameters). The LLM doesn’t know about MCP/A2A/APIs or services, it just sees a set of 𝘨𝘦𝘯𝘦𝘳𝘪𝘤 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴 it can call. ➡️ 𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐜𝐨𝐦𝐞𝐬 𝐢𝐧: Your agent code (or framework) decides what those functions really represent. That is, they decide what to "bind to". In practice, they’re just API calls. MCP provides a thin, standard way to define these tools. That makes it easy for your agent to map MCP-defined tools into the LLM’s function call API. ➡️ 𝐖𝐡𝐚𝐭 𝐚𝐛𝐨𝐮𝐭 𝐀2𝐀? It’s the same idea. You take an agent-card.json (A2A’s format) and map it into function calls that the LLM understands. The LLM only knows how to call 𝐠𝐞𝐧𝐞𝐫𝐢𝐜 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬. It’s your agent (or agent framework) that decides how those map to MCP or A2A. These specs just give us a clean, standard way to describe the bindings. HTH! 

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