656. Jean-Christophe Lanoix, Turboconsultant: Running a Solo Practice on AI Agents, end-to-end

Speaker A: Hello and welcome to Unleashed. I'm your host, Will Bachmann, and I'm delighted to be here today with Jean Christophe Lenoir, who was a consultant for 17 years. He was the first employee along with the founder of a firm called Inisio. Starting with an H, H I N I C, I O, he was the first employee and they grew it to 70 employees. That firm, it was sold to a. An engineering firm. It's. It's a firm in, in Belgium and France. And then he's currently the founder of Turbo Consultant, which is, uh, builds AI systems purpose built for independent strategy and management consultants. So, John Christophe, welcome to the show.

Speaker B: Thank you, Will. Thanks for having me.

Speaker A: M. And maybe just right at the beginning here, and I know you, we can repeat it at the end, but to catch listeners that you know, just right at the beginning, I think that you have a discount code for listeners of this show for Turbo Consultant. We're going to be spending most of the show today diving into Turbo Consultant. Uh, but maybe you just share that right up at the front as well to, uh, to catch listeners as, as they're joining here.

Speaker B: Yes, indeed. Thanks for the introduction. I. I do propose a promo code for you listeners, which is UnleashedTC, like Turbo Consultant. And it gives €100 discount on the price of, uh, Turbo Consultant, which is normally €499 per month. So it comes down to €399 per month for life for your listeners with this promo code. But we'll come back to that later.

Speaker A: Very cool. All right. And, um, Umbrex and Unleashed, we do not receive a referral fee, but we're happy to make people aware of that and we'll include that, uh, discount in the show notes. So thank you for that. I know you have a couple slides and you're going to actually do a bit of a demo of the tool. So lead off, Jean Christophe, tell us about this tool that you have built.

Speaker B: Sure, let's go ahead. So I'm going to share my screen right now, please. We will confirm, um, if it works,

Speaker A: and I'll mention the listeners who are getting this on audio. We're going to put this out on itunes and Spotify and all the great place you listen to podcasts as an audio version. But we will have screenshots on the Umbrex website. And we're also going to publish the video version of this as a permalink. On the Umbrex website, we have some profiles of different AI tools for consultants. So this video, if you do want to watch and have all the visuals, you uh, can go to the Umbrex website and we'll include a link in the show notes for people to find that.

Speaker B: Okay, I'll do my best to uh, make it understandable for those who will listen only uh, without the image. But we're going to have a lot of slides and a lot of uh, images. So I'll do my best. So what is Turbo Consultant? Can you see my screen?

Speaker A: Will I can, I can see it. Great.

Speaker B: Okay. So it's basically the execution layer, uh, the AI execution layer for solo consultants. The main limiting factors for solo consultants is they do not have the pyramid, they do not have the army of uh, junior consultants to do all the research and all the nitty gritty details and uh, all the slide building and so on. And the unfortunate reality for Solow is that he is his own pyramid. He builds his own slides, he does his own research. And the reality is that he's spending most of his time on lower value tasks and uh, less of his time on high value tasks. So Turbo Consultant is uh, a solution to that dilemma. And um, it's basically a virtual team of AI agents under the consultant's direction. So the consultant basically sits on top and directs a team of AI agents that cover uh, the entire spectrum of activities of a solo consultant going from upstream business development, lead generation, proposal building, research and monitoring. It has deep research capabilities, uh, it can build methodologies, can do the marketing, build uh, marketing material, Write contents except etc. Build the deliverables. That's a big big part of it. Quality control. We'll come back to that. It's very important for consultants. It has various layers of quality assurance that are built in. It can help you prepare your meetings, do the follow up and many management and administrative tasks such as uh, writing your contracts, etc. Etc. Can manage your emails, etc. Etc. So from a technical point of view, Turbo Consultant is a layer sitting on top of Entropic co work. So Cowork is uh, agentic environment built on Top of Entropic LLMs. Fable 5 Opus 5 Sonnet 5 Opus 4.8 Etc. All the LLMs from Entropic are basically very powerful models but they are pretty much useless. To actually do things you need a uh, harness, you need an orchestration layer. Claude Cowork is such an orchestration layer. So it allows to go from question and answers. This is what you get from a uh, standard LLM to instructions and actions. So with Cowork you can actually do things within the um, entropic environment. Or outside, you can connect to many other tools. Your emails, your file systems, uh, your canva. If you use canva or thousands of different tools. Turbo Consultant is a plugin within Claude Cowork, and it basically transforms Cowork into a consulting team. Okay? It adds the right method, the right guardrails. So Cowork behaves like a perfect team of consultants. Not only that, it behaves like your perfect team of consultants because it's highly personalizable. Okay, we'll come back to that in a second. So that's very uh, nice on paper, but in reality we all know that AI has a uh, big limitation for uh, consultants. First of all, it is very generic, okay? It gives very generic output. There is no differentiation, okay? If you start using ChatGPT or Claude out of the box for your consulting work, it's gonna sound like any other consultant using ChatGPT or Claude, okay? Turbo Consultants, um, is not like that. It is very personalized, um, because when you first use it, uh, you go through uh, a, a step of onboarding where for two or three hours the system is going to ask you all the possible questions about your business. Who you are as a consultant, who are your clients, what kind of services you provide, what are your references, who are your competitors. You're going to provide examples of deliverables, examples of proposals. You're going to provide your template so the um, system can extract all your patterns. Your semantic patterns is going to start speaking like you. It's going to extract your visual pattern, it's going to build slides like you. It's going to extract your methodological patterns. It's going to use your own methodologies, so highly personalized environment. Secondly, AI is a yes man. Okay, yes, with uh, AI you can go faster, but, but the uh, reality is you go faster into the world, uh, because you have constantly uh, this reinforcing uh, loop. Even if you're wrong, the system will never say that you're wrong. Here I added critical thinking. So the system is going to push back. If you're wrong, it's going to let you know. Third, hallucination made up facts, errors. AI basically is great 80% of the time, but 20% of the time it's not great at all. It gives you hallucinated numbers, uh, fake sources, et cetera. And basically the time you gain during the 80% of the time you waste it, uh, by reviewing everything, uh, by hand to make sure all of these errors never reach your clients. Here we have a triple level quality assurance. Everything is triple checked. The sources uh, you have an internal audit by cowork itself, external audit by external LLMs. So what you get is really something that is 99% client ready. Then you have an issue with normal, well, uh, consumer AI. You have an issue with client sensitive data, especially if you use your cowork session or if you use your uh, ChatGPT session with the memory. Memory is very useful so the system remembers you. But it's going to start mixing up uh, information between clients. So you're going to have a leakage of information from client A to client B. Deliverable. That's a big, big problem for consultants here. It's fixed by design, everything is siloed and it's just physically impossible to have such a leakage. I'll show you why. And finally with uh, AI as a consultant, what you end up having is a uh, patchwork of disconnected tools, uh, with various subscriptions and you do not capitalize on anything because you have every time to start again re explain what you do re explain who you are and you have to do copy and paste from one tool to perplexity to chatgpt, etc. Here it's unified, it's a personalized system and it gets better every day. It knows you more and more. Okay, very quickly it includes a whole library of consulting grade methodologies in many different areas from strategic consulting to market analysis, innovation and product data management, problem structuring, Messi, Pyramid principle M and A due diligence. You have various uh, valuation methodologies that are built in that you can use if you want to. And the system will add to that library all the methodology that you use that are going to be extracted from you during the onboarding stage at the very beginning. So you have a lot of. The system is going to use your methodology in priority, but it can also use methodologies consulting grade from McKinsey, from BCG, etc. Off the shelf. Okay, let's start the demo wheel. So I'll switch to the Cowork environment. So can you see it now?

Speaker A: Yes.

Speaker B: Great. So this is Cowork. So it looks like a uh, standard chat standard LLM. But for those who don't know cowork, as I said, it's not question and answers. Well obviously you can ask questions and get answers, but it's more instructions and action. The system do things for you. So basically uh, Cowork sits into the settings, it's a plugin. So uh, in the settings you have this section here, plugin and it's very easy when you subscribe, uh you basically download a uh, connector which is here. It's a one click install and the connector basically connects cowork to uh, the actual plugin which is on the server. Okay. So it's very easy to install. There are zero technical uh, hurdle here. Okay, so what we're going to do now is a uh, couple of tasks which we're going to start in parallel including building a proposal, preparing a kickoff meeting and executing a uh, work package in a typical uh, client engagement. So what I mean by a typical client engagement is here I built, it's totally made up as the AI to do it for me. I built an hypothetical request for proposal uh from an hypothetical client which is named Kestrel Rich Partners. A lower middle market private equity firm investing in health care and consumer service in the U.S. okay so the topic is as follows. We are in exclusivity on a group of 42 veterinary clinics across the U.S. southeast. Approximately 120 million revenue. The business is founder owned and has been built by acquiring independent practices one at a time over eight years. So the objective is an independent commercial view to support our investment committee on August 21. So what is specifically requested? The scope is market uh, analysis. Is pet care spending still growing or was the pandemic surge a one off? Ah, that's the first question. Second question is retention. How loyal are ah pet owners? Okay. When the practice changes hand then competition analysis Runway and the investment thesis at ah, the end. Okay, what we're going to execute now will be the first work package on market. So what's requested is information available to the advisor Financials by clinic 3 years Anonymized client list set as information memorandum. So the deliverable expected is a 20 to 25 page deck IC ready reporting model, the timeline and the indicative budget 55 to 75k. What the proposal should contain approach and work plan team and relevant experience. What you need from us, assumptions and risk fees and terms. Voila. Uh, that's totally hypothetical. This does not exist. So what we're going to ask Turbo Consultants is to help us build the proposal. So I'm going to ask. I just received the attached rfp. I want you to help me build the proposal. It's this one here. Voila. Uh, okay, we can start. All right, so in parallel we're gonna launch another task. By the way, what I'm doing is basically I'm using Turbo Consultant as any user would on a normal day. I didn't show you the actual onboarding. Okay. Which is just a one off task which lasts two to Three hours, as I said, because I did it myself before the demo. What I can show you just to illustrate an important point is here. This is an onboarded folder. Okay? When you start with Turbo, uh, Consultant, you start with an empty folder, you give coworker a working folder, it's totally empty. And then you go through the onboarding and then the system asks you questions and at the end it builds a whole set of folders and files which are the infrastructure of files that will be used later on by the system to work uh, like a consultant and to work like you, basically. Okay, and one important thing here is the client subfolder here. Every time you have a new assignment, a ah, new engagement, you create a new client folder. Then when you work on the engagement, you open cowork on this particular client folder. This is quite crucial for two reasons. One, it's crucial for data management and data, uh, hygiene, let's say because when cowork is working on that particular client, he only is only seeing what's inside the folder, he's not seeing what's outside, he's not seeing the other clients. So that's the architectural reason why the data leakage between clients is impossible, physically impossible. The second reason is when you create, and when you ask Turbo Consultant to create uh, client folder, it creates the same kind of folder structure, file structure and folder structure entirely focused on the engagement. So when you work on this folder, the system behaves like a uh, war machine aimed at executing this particular engagement. It's not polluted by all the context outside of it by all your other work. It's only focused on this, executing this work. It has its own cloud MD file, its own identity, just to execute this engagement. Okay? It is hyper focused. All right, so here, so we ask to build the proposal. So very interesting here. The first question the system is asking you here is how do you want to work on this proposal? You can work autonomously. Well, the system can work autonomously. So you give a couple of inputs at the beginning and then it goes straight to uh, delivering you with a document. You have the exact opposite, which is co construction where you're going to be consulting every step of the way. The system is going to ask you to validate everything. You're going to be brainstorming for every slide, every task, uh, everything. Okay, it takes longer, it takes more of your time, but when you get there it's almost ready because you already gave everything you have. And this is what I would use if I was still a consultant using Turbo Consultant. And you have the intermediate. Uh, so it's pretty much in the middle. You validate the big picture, and then boom, you get your document. So here, for the sake of the demo, we go for autonomous. Where do you want to price inside this? Uh, 50 to 75k. Uh, let's say top of the range. Who is on the team? Me only. What, uh, is your relationship with the client? Let's say it's called. But if you have a history, you can actually mention it and he will use it in the proposal. Uh, what's your track record? I can cite. Yeah, we could give as much information as we want if we have a track record. Who is likely bidding? Let's say mbb. No, let's say CDD Boutique and other independents. What do I want? Slides. And let's have a synthetic presentation. So it does not take too much time. Give me synthetic proposal. Let's say around eight slides. Obviously, if we want a 20, 30 slides, it can do it also. All right, so now we're going to launch a second task in parallel. So let's say we won the proposal. All right, so we are starting the project. So we have to give it the client folder now. Here. So it's this one here. All right, we are starting the project. And, uh, well, first thing to do is a kickoff meeting. So I am starting the project. I want you to help me prepare for the kickoff meeting.

Speaker A: And.

Speaker B: Yeah, we good. Let's go. Let's go ahead. All right. And also we're gonna ask, uh, Turbo Consultant to execute Work Package one. I want you to execute Work Package one. So as a reminder, Work package one is, uh, market. Is pet care spending still growing or was the pandemic surge a one off? Okay, so we're gonna go from one task to the other to see it's gonna take time. So what you're gonna see, Will, is that it's not the typical, uh, question and answer type of LLM experience. It's gonna take quite some time. Sometimes it takes 15 minutes, 30 minutes, 45 minutes sometimes. Because the system does actually a lot of work in the background, it is following a very strict process and methodology. Okay? Uh, the methodology of the engagement has been defined and is following it step by step. And it takes a bit of time. So here w. So one question on the work package 1. What form do you want now? So what I want is a, uh, PowerPoint and an Excel. And question number two, uh, research depth. A kickoff meeting. Who sits across the table at the kickoff meeting? So, Dana Whitfield. Well, that's the person who sent the email and the uh, deal team. Yeah. Okay, let's go. What should I produce for the kickoff meeting? Briefing note. Briefing note and short kickoff deck. Data room. So let's go for briefing note and short kickoff deck. Yeah, let's go. How much external research before the kickoff meeting?

Speaker A: Uh,

Speaker B: known structured only. Okay. Just for the sake of uh, going faster. All right, so the system is working right now on the free task. What I'm going to show you now will is examples that I run before the demo so you can actually see examples of deliverables. So it's on another rfp, hypothetical rfp, uh on a completely different topic which is EV charging, Electric vehicles charging. It comes from a fake made up company, Verdor Group which operates UH 1100 supermarkets across six countries in Europe. Most sites have large customer car parks and basically they're considering uh the opportunity to have charging points for EVs on their parking lot. Okay, so today we lease parking space to two charging operators under long term agreement and earn a nominal fee. The board believes we are leaving value on the table and has uh asked management to assess bringing charging in house. A uh board paper circulated in June states that the European EV charging market will be worth uh 25 billion by 2030. We would like this figure to be tested before any commitment is made. So the objective is an independent defensible view on whether the company should operate charging itself and if so where and how cope we have market, uh WP1 bug, package one, then segmentation, then geography, then competition, analysis, entry mode and plan. What we're going to uh execute is WP1 market. So what the deliverable expected is a board ready deck, Excel model and one batch recommendation. So I run exactly the same exercise with this rfp. I built a proposal which is uh, let me see here. All right, so this is exactly the same experience than what you're seeing right now. I said I have just received the attached rfp. I want you to help me build the offer. Okay. Then the system asked a couple of questions just like it did. How do you want to work? Uh, I said intermediate. What fee are ah you targeting for the eight weeks? I want to follow a realistic bottom much approach with a detailed budget breakdown at the task and subtext level. You can come up with various scenarios and options. Who deliver me, what format PowerPoint relationship with uh existing relationship with customer called Whos is likely bidding MBB, energy specialist, boutique and other independent. Okay. Then the system went and delivered in a matter of maybe 10 minutes a proposal Of a structure, a proposed structure for the proposal. So 70 slides and well, obviously you listeners only listening to the podcast cannot see it, but it's basically an overview, uh, an outline of the presentation, the 17, uh, proposed slides in different sections. So at the beginning, uh, pretty much the context, um, what we already know before the engagement starts. So the starting assumptions, let's say, and then m more of the objectives, the work packages, so the methodologies, 1, 2, 3, 4, 5, 6, and the organization and the budget. Okay, so the system gives you the overview of the proposal and ask you to validate. So you don't see it here, but I had uh, a question box asking is it okay or not? So I said yes. And maybe half an hour later the system has been working, uh, pretty hard and it delivered a, uh, proposal in my template. So it's natively within Cowork. This is Turbo Consultant template, which you saw before. Okay. And I get a, uh, fully fledged proposal. So here we have uh, the content. Well actually I will open PowerPoint so we can see it better here. So the overview with the three, uh, sections, okay, the introduction, the work packages methodology, and then the budget and organization. So. Well, here, for example, on this slide in the introduction, Cowork outlines the missing data. So most of the available data from available scenarios on EV charging in Europe focus on um, everything but public charging. Okay, so right from the beginning, the system identified a missing element in the key data needed to execute. So we have here another piece of, uh, the presentation, another piece of missing data is, uh, the system looked at all the, well, the selection of eight competitors and basically they disclose information on the charging point and the tariff, but nothing on the actual usage and their economics. Okay, so this is right from the beginning we see that this is something that's going to be pretty key in executing the assignment, how we get the data. And basically the data will come from the client because he has uh, already two parking operating with uh, uh, charging points, with lease agreement. So this is what it is explained here. So the system, the key, the idea here is not to go in too much into the details, but just to show you that the system has perfectly understood the context and what is at stake, uh, and what's missing and what we need to do to actually find that missing data so we can keep going. So you see, this is really consulting grade slides. Okay, yeah, it's not only in your template, it's actually consulting grade, which is quite difficult to find on the marketplace. Uh, to be honest, either you have something in your template, but not consulting grade or consulting grade, but not in your template. Here you have both natively inside of Cowork. It's a huge time saver because you can actually rework everything right out of the box. Okay, so here you have the methodology with the different work packages and yeah, the detail of each work packages. So here you can see in the RFP they proposed the, uh, build by your partner, and the system actually proposed a fourth option which is repricing their existing leasing agreement, which is, again, a smart idea. It's. It shows that really understand what is talking about. And then we have the last section with the timeline. And yeah, something I didn't like is the way it presents the budget. So I basically said it here. Oh, yeah, I asked for a detailed budget in an Excel, so that's what I had here. Yeah, the effort for each subtask. And then I say, I don't want to disclose the number of man days. This was only for internal use. I want you to rework the slides. Certain slides, including how we present the budget. I don't want to show my Mondays. Okay. If necessary, only give me two, three sites separately. No need to rebuild the entire deck. And then it gives me, yeah, maybe five minutes later. So one on work package, one presented differently without the mandates. And the actual offer with the price, the value, but not the cost, not the mandates. It's very easy to rework. You can do it manually or you can ask the system to do it for you. Let's go and see where. Okay, so context. All right, so now kickoff preparation. So I did exactly the same. The project is starting. I want you to help me prepare for the kickoff meeting. Ten minutes later, I get my deck again in my template. It's not a very difficult work to do as a consultant. It's just you take your offer and you transform it into a deck for the kickoff meeting. But it just takes time. What we need from the client. So that's, uh, the data request. Okay. The timeline. Speaking of data request. So he built me the table, the detail Excel table of the data I need to request from the client. So let me zoom in here. So every piece of data, uh, item requested, why is it needed? What work package, how critical, etc. Etc. For all the data points that we need. This takes honestly, two, three hours if you're a consultant. It gives me my briefing notes that you can send to the client. Again, it's not rocket science. It's just, again, it's the proposal reformatted, uh, differently, but it just takes time. And it gives me my own internal briefing. So who is in the room? Uh, the one thing to get right. So the key objective of my meeting, who is in the room, what uh, they want, what to give them. Yeah, this 25 billion number that we need to double check from the board how to handle it and then the questions to ask in priority order, the objections and what we can answer to the objections. So we already know the market is 25 billions. What need need you to, we need to tell us what to do about it. So what you, what you should answer to that the agenda here. So now let's have a look where we're at. So it's still working. So you see it's, you see it's doing work here. So it's the preparation of the kickoff meeting from the, for the other engagement. So it read the rfp, it wrote the uh, kickoff briefing note and now it's building the kickoff deck. Okay, so you can see the to do list here. So let's go back to the ev charging engagement. And I said I want you to help me execute work package one on market, same as you already saw. So it worked. It took a long time. It took maybe 45 minutes. And again this is an extreme case. I do not recommend to go for such a extreme uh, level of automatization. I do recommend to go co construction and to interact as much as possible with the system. But this is just for the demo. And what we got is another very nice PowerPoint in my template. It could be in anyone's template where during the onboarding you provide your template and the system will just ingest it and reuse it. So you get here a um, very nice executive summary slide with key conclusions. Okay. And then the whole analysis and one of the key part of the analysis is double checking the 25 billion of the from the board. And basically this is the, the system is not a uh, yes man because he actually pushed back on this number quite a lot and it turned out this number is completely false. It's just a capex. Total capex. But it's um, not the actual revenue the company could get. And that was part of the analysis here. Okay, so what, yeah, that's, that's what you see here. You know, you have the total market and then different, you peel the onion and in the end the real scope is much smaller than anticipated. So here you see again a lot of uh, analytical consulting grade slides. But the next question is how do I know that it's actually correct and it's not Full of mistakes and hallucinations. And I'm glad you asked because I run the audit of this. And it's here. Let me see. Yeah, it's here. It's in another thread. I want you to do the three audits for WP1, market deliverables, both the PowerPoint and the Excel, and give me the three full audit reports. So the first layer of audit is full source traceability for each claim, identify the sources, evaluate the quality of the sources and the level of confidence. Level two is internal audit. It's cowork, creating a sub agent who's going to audit the main agent work. And L, uh, three. The third layer is external audit. So it's two external models, two external LLMs via, uh, API. So you have Fable 5 and you have GPT 5.6. So it's a very powerful model that are going to audit the deliverable. Do not modify the deliverable just yet. Just provide me with your recommendation as well as the key point where my decision are needed. So he went on and maybe 30 minutes later delivered a report for the sources. Sources, verification. So you see the summary here. So factual claims 47, verified 14, probable 9, hypothesis 13 contradicted 6, unverifiable 5, confidence label that needs downgrading 5 and confidence label that need upgrading. 1. Okay, and then you have all the details and one second, so you see all the details here. You don't have to read it yourself. You just ask the system to iterate, improve, audit again and in five minutes or in 15 minutes you get a new version that is way better. The second audit, internal audit, again here, it's really detailed. And the third layer of audit here, what you see here is very interesting. You have the two models and you have all the criteria, okay? Internal coherence, conformity with the firm standard, voice and tone consistency, source quality, analytical rigor. You have, I don't know, 20 different criteria and two models that assess the, um, deliverable according to these criteria. Okay? So what I did is to ask for a summary because it's too much information. So it gave me, uh, a quick summary here and it's actually a very interesting one. The model is the strongest artifact in the engagement. The deck is writing checks. It does not catch, okay? So it has mistakes in it. And that's why the audit is so valuable, okay? Because the documents looks very nice, but in reality they are mistakes. And then it gives me the eight decisions I need to make on the perimeter on WP1, headline number, etc. His recommendation. And then I said I want you to rework the deliverables by integrating all the relevant findings, including your eight recommendations. And then, yeah, maybe 30 minutes later I get a, ah, new version here that integrates everything. I think it added additional slides, if I remember correctly. Yeah, this one is new, so it's actually super useful. And it's uh, a key safeguard against a huge limitation of AI for consulting. The fact that it makes so many mistakes is, for me it's, uh, an absolute blocker for the use of AI in consulting. This layer of quality assurance is, in my opinion, a game changer because again, in very short amount of time, you can iterate and get to something that is ready for final review. So let's see where we're at. It's still, uh, working. Yeah, it's still working on the kickoff preparation, uh, it's still working on the exhibit, uh, on the work package one, and it's still working on the proposal as well. All right, so let me show you a couple of other features. This, uh, one here, writing of service contracts, that's another small task for a consultant. That takes time and it's always the same. You have your template just like here, service agreement. So that's a typical service agreement, a template that any consultant has. And yeah, you have all those placeholders and for everything, for your name, for the client name, the address, the scope, the engagement dates, et cetera. And you have your proposal and you have to put your proposal inside your template. We've all done that in the past. It takes time. It's very annoying. So here you just go. I have won the attached proposal. I want you to prepare the service contract using my own template available in your workspace. So I gave it to him. So he asked me a couple of questions. I gave my own company's information. I say leave blank for the client, and then five minutes later I get my service, uh, contract here. So it has filled in my company's information, it left blank for, for the client. And then we have the actual scope, the deliverables, the fees here, the milestone for payments. Well, it took the proposal, put it in the contract. No rocket science. But again, you, uh, save two hours here.

Speaker A: That's very cool. So powerful tool that you've built for listeners that want to go check it out and learn more. What's the website? Where do they go online to check it out?

Speaker B: Sure. Let me go back to the PowerPoint. You, uh, can go to www.turboconsultant.com. yeah, again, there is a promo code for your listeners, uh, unleashedtc they can also go to my LinkedIn account. I try to post every day on, uh, AI in consulting Jean Christophe Lanois. I guess you will give the link.

Speaker A: Fantastic. All right. Well, Jean Christophe, congrats on building Turbo Consultant. Looks like a pretty powerful tool. Uh, the PowerPoints look beautiful. Really nice, nicely constructed pages. Thanks for joining and thanks for extending the discount, uh, offer to listeners of the show.

Speaker B: Thank you very much. I'm glad you, you liked it. According to my own measurement and according to my, actually, my client feedback, uh, you get a 25x on tasks like we saw today, proposal building or, uh, deliverable, uh, writing 25X. You can do several in parallel, so you get possibly a 50, uh, x 100x. And, uh, the quality is preserved if not increased. So, uh, yeah, I'm very, very excited and, um, very glad that you invited me. Uh, will, thank you very much.

Speaker A: All right, thank you for joining.