Cédric Bidet builds AI agents and practical business tools for small and midsize businesses, with a focus on simplifying workflows and reducing repetitive work.
Cédric Bidet is most useful for explaining practical AI agents and repeatable processes for small and midsize businesses.
Best for
A B2B software company adding AI agents and wanting buyers to understand practical adoption.
Not for
A mass-market consumer brand seeking high-reach sponsored content with documented audience and rate card.
Key takeaways
The value of AI-assisted development is a repeatable process, not just faster code.
A simple test produced a tool for checking email, spotting CRM opportunities and creating follow-up tasks.
Agents can file email attachments, prepare meeting follow-ups or combine e-commerce data with CRM.
Check first: Ask whether they accept sponsorships or brand collaborations and request their terms.
Main ideas
AI should remove everyday work, not add another layer of complexity · The value of AI-assisted development is a repeatable process, not just faster code · AI changes what developers contribute, but experience still matters
Cédric Bidet builds AI agents and practical business tools for small and midsize businesses.
Their posts focus on making AI useful in everyday work: reducing repetitive steps, connecting existing tools and fitting agents to an organization’s processes and constraints.
They also write about how AI is changing software development and the role of interfaces.
Follow their work for concrete examples and a pragmatic view of where AI can save effort—and where human judgment still matters.
Bidet connects technical choices to the work people need to get done.
In one post, they describe a process that turns a validated product specification into GitHub issues, a prepared development environment and an AI-assisted build.
A simple test produced a tool for checking email, spotting important CRM opportunities and creating follow-up tasks.
Their broader argument: the important change is not simply faster coding, but repeatable AI processes.
They apply the same practical lens to business operations, describing agents that file email attachments, prepare meeting follow-ups or combine e-commerce data with CRM and sales goals.
Their posts also explore how agents might cut steps between information and action, while recognizing that interfaces will not disappear overnight.
Cédric Bidet argues that AI is most useful when it removes friction from real work and fits the systems people already use.
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Rather than sell AI as magic, they emphasize practical workflows, sound technical judgment and tools that people can steer and trust.
AI should remove everyday work, not add another layer of complexity
Bidet focuses on small, concrete jobs that waste time: moving information between tools, sorting attachments and turning meetings into actions.
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The goal is to make familiar processes easier while respecting an organization’s processes, privacy needs and existing technology.
Bidet describes an agent that summarizes email and files attachments in the right place, alongside another process that checks email for important CRM opportunities, suggests actions and creates to-do list tasks.
The value of AI-assisted development is a repeatable process, not just faster code
Bidet’s account of building with Claude Code stresses the groundwork around generation: shared documentation, technical conventions, access controls, testing and deployment.
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A fast prototype matters, but the larger shift is being able to reproduce a useful process with less setup each time.
For a simple to-do tool, Bidet says they validated the business need and edge cases, then used a script to prepare the project and give Claude Code the relevant documentation. The tool was usable in under two hours; the key change, they say, is making AI workflows reproducible.
AI changes what developers contribute, but experience still matters
Bidet sees AI coding as part of software development, not a separate novelty.
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The developer’s contribution shifts toward deciding what to build, shaping its architecture and checking the result. They also acknowledge that the pace of change brings uncertainty and concern about the meaning of the work.
Bidet contrasts early struggles with PHP syntax errors with using Claude Code to produce Python, a language they say was unfamiliar a year earlier. Their technical experience still informs architecture, design patterns and best practices; the generated code may need correction or a different approach.
The posts describe both a personal shift away from writing every line of code and a broader, unsettled debate over quality, security and developers’ changing roles.
Agents can reduce interface friction, but people still need control
Bidet imagines agents retrieving information and carrying out tasks across services, sparing people from navigating a separate interface for every step.
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The aim is to reduce needless friction, not eliminate graphical interfaces: they say validation still calls for one and flag ethical questions around these capabilities.
Bidet contrasts finding a lasagna recipe on Marmiton through ads and pop-ups with asking an agent to handle a professional task, such as filtering CRM records and exporting a selection. The agent could work in the background while a person reviews or validates the result.
Pragmatic AI means less hype and more attention to fit
In a crowded market of jargon and bold promises, Bidet prioritizes simplification and controlled solutions.
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They argue that an agent must fit a business’s real processes and constraints; a technically impressive demonstration is not enough if it does not help the organization do its work.
Bidet lists modest operational uses: filing email attachments, creating personalized meeting to-do lists, and combining e-commerce data with CRM information and sales targets. A client quoted in the post reported that agents helped the business fulfill orders when an employee was injured.
This post shows what Bidet builds: how an idea becomes a working agent-assisted tool, and why the repeatable process matters more than the code.
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Bidet describes a weekend test that took less than two hours from idea to a tool they used daily.
Firmin.ai is building a shared platform for deploying AI agents and business tools for small and midsize companies.
New tools inherit a standard stack, centralized documentation, authentication, rights management, workspaces, technical and graphic guidelines, architecture conventions, and deployment.
The team then industrialized initialization: once a spec is validated, a script splits the project into GitHub issues, configures environments, prepares the technical base, and launches Claude Code with the project documentation and conventions.
Bidet tested the process with a deliberately simple todo-list and could soon write: 'Check my emails, detect important CRM opportunities, propose actions to do, then create tasks in my todo.' They say the real shift is not that AI codes quickly, but that AI workflows are becoming reproducible systems.
For brands planning a campaign
For a B2B software company adding AI agents, this is concrete evidence for packaging agents as repeatable workflows around existing product data and actions, rather than as isolated chat. A campaign could position Bidet around reducing setup friction and designing agent UX that hands work into CRM and task systems.
This post explains what Bidet offers and how they distinguish Firmin.ai in a crowded market.
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It is the clearest starting point for newcomers seeking to understand the commercial side of the work.
Bidet opens with a familiar line: 'You do AI agents... a bit like everyone, no?' They agree they are neither first nor last and describe the market as a bazaar.
Executives are convinced they need to act but lost in jargon and promises, while vendors sell dreams built on LLMs, RAG, agents, and automation.
Bidet says they bring less: less jargon, less magic, fewer wow effects.
Their job is to simplify, deconstruct complexity, and build controlled solutions.
Citing 20 years in tech, they argue that an AI agent does not live in a vacuum; it must fit an organization, its processes, economic context, confidentiality constraints, and legacy systems.
Examples include an email summary that files attachments in the right place, a meeting report with a personalized todo list, and e-commerce extraction consolidated with the CRM and sales objectives.
Bidet recalls a client saying agents set up the previous month helped them honor orders during an employee's injury.
Firmin.ai deploys personalized AI agents from 150 euros per month with no commitment.
For brands planning a campaign
This post sets out Bidet's commercial positioning and offers a pricing signal.
A campaign could test an SME education format or a case-study angle around operational continuity and process fit.
Avoid generic agent hype; lead with process specifics and the promise of less friction.
This post explains Bidet's technical background and why they see AI coding as a shift in developers' work.
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It helps assess the credibility behind the product and process claims.
Bidet says code was always a brake.
They started with PHP at 16, using a printed tutorial from Site du Zero and EasyPHP.
Hours disappeared into a missing semicolon, an extra dollar sign, or a broken if statement, with no YouTube or StackOverflow at the time.
Experience eliminated those errors; they structured code, built a framework or CMS, then moved on to advanced frameworks, design patterns, architectures, scalability, and technical debt.
The point of the job, they say, is to give life to what is in your head, creating something from nothing like an artisan.
Claude Code is a revolution within the revolution: Bidet is no longer obliged to code, and the role becomes thinking about what to make and how, in PHP, Python, or JavaScript.
Claude Code produces code, challenges assumptions, asks about unclear areas, and builds according to guidelines.
Bidet is glad to have started coding nearly 25 years ago, because receiving that power at the beginning would have been difficult to use well.
For brands planning a campaign
Use this for developer-adoption or AI-coding angles, rather than as an enterprise governance voice unless supported elsewhere. The artisan and design-pattern framing can anchor a message that AI shifts value from syntax to architecture and judgment.
This is Bidet's clearest strategic view of why AI agents matter beyond chatbots.
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It explains how agents could reduce interface friction and integrate with websites and business tools—a useful perspective for product teams considering agent-first UX.
Bidet contrasts a grandmother who could use a computer through a graphical interface with the frustration of finding a lasagne recipe on Marmiton among ads, pop-ups, and endless scrolling.
The internet multiplied interfaces, while hidden menus, display bugs, and convoluted journeys still make tools hard to understand.
They argue that AI can break those frictions: it can fetch information directly or pilot a third-party site, and already knows enough to filter, cross-reference data, and make relevant choices.
That raises ethical questions, but Bidet says it is technically possible through APIs, MCP, and protocols that make sites function as backends.
The post cites OpenAI's Agentic Commerce Protocol, which allows a shopping list and purchase without leaving ChatGPT.
Only validation still requires a graphical interface.
In a professional context, they contrast opening a CRM, adding filters, exporting Excel, and cross-referencing it with a business tool with simply asking an agent in natural language to export a selection or launch a campaign.
The agent is invisible and works in the backend.
Graphical interfaces will not disappear tomorrow, but tools will need to integrate with agents.
For brands planning a campaign
This is Bidet's thesis for agent-first product design.
A B2B SaaS campaign could frame an AI feature as removing steps between data and action.
Treat the OpenAI protocol reference as a claim in Bidet's post, not independent verification.
The validation-step detail is useful for UX requirements.
This post shows how Bidet reads the developer debate around AI and why architecture, not language, is central to the argument.
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It offers a useful sample of their voice in technical community conversations.
Bidet calls vibe coding one of the hottest topics in the developer community, then says people should stop saying 'vibe coding' because it is now integral to the profession.
The debate spans concerns about quality and security, claims that all front-end code in the US is AI-generated, that apps can be built in two days, and that developer jobs will disappear.
Bidet says everyone has a bad viewpoint, including them, and everyone is advancing in fog because what is true today may not be true tomorrow.
They point to anxiety about jobs changing radically and about delegating intellectual reflection.
The developer role, once at the top of the hype pyramid, is being disrupted, and developers with ego are being overtaken by those who master AI.
Bidet still sees a colossal lever for value production and a chance to start an activity by testing, restarting, iterating, and gathering feedback; work once planned for six months may now happen in weeks.
They are amazed that Claude can code Python, a language they did not know a year earlier, while they know architecture, design patterns, and best practices; Claude translates that into Python.
Practices remain empirical, and Bidet asks readers where they are and whether they have documented their methods.
For brands planning a campaign
This fits developer-community content on AI adoption, not the direct SME agent offer.
A campaign could feature Bidet discussing architecture and judgment over syntax, or gather practitioner process examples.
The open question is a natural engagement hook, but should not be presented as completed research.
Practical AI agents for small and midsize businesses3
Industrializing AI-assisted development with Claude Code3
Simplifying AI adoption and cutting through jargon2
The changing developer role and AI as a sparring partner2
Reducing interface friction and the shift to agentic tools2
Practical AI agents for small and midsize businesses
Bidet builds lightweight AI agents and business tools for SMEs, mainly through Firmin.ai.
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Their examples include an email summary that files attachments, a meeting report with a personalized to-do list, and an e-commerce extraction consolidated with CRM and sales targets.
They describe a common platform for deploying agents and business tools, with personalized agents starting at €150 per month without commitment.
They stress the need to understand an organization's processes, confidentiality constraints, and technical legacy so AI becomes a lever rather than a risk.
Uses everyday examples and client stories to show how small automations remove repetitive work and free up time.
Bidet openly calls themself 'one more' AI agent builder in a messy market; their differentiator is simplification, not novelty.
For brands planning a campaign
Brands can consider Bidet for campaigns that need grounded, SME-focused AI use cases rather than abstract LLM promises. Their examples can be repurposed for product marketing or user education about AI agents.
Industrializing AI-assisted development with Claude Code
Bidet describes a process that uses Claude Code and a common platform to turn an idea into a usable tool in under two hours.
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The platform standardizes stack, documentation, authentication, rights, workspaces, technical and graphic guidelines, architecture conventions, and deployment, so each new tool inherits them.
A script then splits the project into GitHub issues, configures environments, prepares the technical base, and launches Claude Code with the project documentation.
After testing it with a simple to-do list, they could ask an agent to check emails, detect CRM opportunities, propose actions, and create tasks.
They frame the real breakthrough as turning AI processes into reproducible systems.
Step-by-step narration with specific timeframes and tool names.
For brands planning a campaign
Brands building AI features can assess Bidet's practical experience making AI-assisted development repeatable and their focus on reducing setup friction. This is especially relevant for developer-tool or platform campaigns.
Simplifying AI adoption and cutting through jargon
Bidet positions themself as bringing less jargon, less magic, and fewer wow effects to AI projects.
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They say their job is to simplify, deconstruct complexity, and build controlled solutions for problems that are simple but time-consuming or complex but solvable with AI.
Drawing on 20 years of tech experience, they prefer entering an SME's daily life to understand how to improve its internal mechanics.
They note that an AI agent does not live in a vacuum and must fit an organization's processes, economic context, confidentiality constraints, and technical legacy.
Contrasts Bidet's approach with market hype; uses plain language and client outcomes.
Bidet explicitly says they bring 'less' rather than more—a counter-position to hype-driven AI marketing.
For brands planning a campaign
Brands seeking a credible voice on pragmatic AI adoption can use Bidet's anti-hype framing. Their focus on integration constraints is useful for content that addresses buyer skepticism.
The changing developer role and AI as a sparring partner
Bidet writes that AI is reshaping software development, calling 'vibe coding' an integral part of the job rather than a separate trend.
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They observe anxiety among developers about losing passion or meaning, and say experienced developers who master AI now surpass those who relied on ego.
They describe Claude as able to code Python—a language they did not know a year ago—while they provide architecture, design patterns, and best practices.
They note that practices are emerging empirically and no one uses the same method.
Candid reflection on professional identity; acknowledges uncertainty and Bidet's own changing role.
Bidet says everyone advances in a fog and no single method dominates, resisting a tidy conclusion.
For brands planning a campaign
Brands targeting developers or technical audiences can consider Bidet for content about AI's impact on the profession, skill transitions, and practical AI-assisted coding. Their personal account of learning new languages through AI offers a concrete angle.
Reducing interface friction and the shift to agentic tools
Bidet argues that AI is reducing the friction of navigating multiple interfaces.
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They use the example of searching for a lasagna recipe on Marmiton amid ads and pop-ups, then suggest that instead of opening a CRM, adding filters, and exporting an Excel file, a professional can ask an agent in natural language to export a selection or launch a campaign.
They point to APIs, MCP, and protocols like OpenAI's Agentic Commerce Protocol that let sites become backends, with the agent working invisibly in the background.
They expect graphical interfaces to remain for validation and note ethical questions.
Everyday consumer anecdote (grandmother, Marmiton) to frame a professional argument.
Bidet notes graphical interfaces will not disappear tomorrow; validation still needs one.
For brands planning a campaign
Brands developing conversational or agentic experiences can use Bidet's framing of friction reduction and natural-language processes. Their examples—checking email and detecting CRM opportunities—show what users might expect.
The posts document one business relationship: Firmin.ai, the AI-agent platform Bidet writes about as the company, sold to small and midsize businesses from €150 per month.
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The other recurring commercial name is Claude Code, the coding assistant they describe using and recommend to other developers. No sponsorship is disclosed; ask whether the recommendation involves a commercial arrangement.
Firmin.ai
Owned business
Bidet writes as the person behind Firmin.ai, an AI-agent platform for small and midsize businesses, and describes building it.
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They say they hesitated a long time before launching Firmin.ai, explain what the company adds ("moins de jargon", fewer copy-paste tasks), and describe its internal build process: a shared platform for deploying AI agents and business tools, where each new tool inherits the standard stack, authentication, rights management, guidelines and deployment.
The same post carries a sales line for Firmin.ai agents priced from €150 per month with no commitment.
Their exact title is unstated; ask whether they are a founder, director or employee.
Original disclosure
Firmin.ai déploie des agents IA personnalisés dès 150€/mois – sans engagement. On en parle ?
Claude Code is the tool Bidet credits with changing how the work gets done, and the posts read as an endorsement rather than a disclosed partnership.
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They describe Claude Code as the turning point that removed code as a barrier, note that it produces code but also challenges project requirements and asks about unclear areas, and report that a full tool went from idea to daily use in under two hours using an industrialised Claude Code process. No payment, sponsorship or affiliate arrangement is disclosed; ask whether one exists before treating the endorsement as independent.
Cédric Bidet writes about putting AI agents to work on everyday business tasks, especially for small and midsize companies.
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Their focus is on simplifying: less jargon, fewer impressive demos and more attention to whether a tool fits the way a team already works.
They also document how AI-assisted development changes the building process, using Claude Code and shared project conventions to turn an idea into a usable tool quickly and make the process repeatable.
Start with posts that show a specific job—filing email attachments, creating meeting to-dos or checking CRM opportunities—then move to pieces on AI-assisted coding and interface friction.
For related reading, compare with creators who cover AI-native marketing operations, agency models and sales/CRM protocols.
Both write about using AI to run practical B2B operations, not as a showpiece.
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Benoliel describes building an ABM campaign with AI tools and knowledge bases, and points to emerging GTM engineering roles that turn marketing into repeatable systems.
Benoliel focuses on marketing and go-to-market operations—ABM assets, segmentation, CRM and sales handoffs—while Bidet focuses on building lightweight agents for SME tasks and AI-assisted software development.
Both examine where AI fits in B2B services and workflows.
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Robin analyzes agency growth, prospecting, client experience and new agency models, including the limits of automated outreach and the value of systems over volume.
Robin focuses on agency positioning, business models and market strategy, while Bidet focuses on building agents, repeatable AI-assisted development and reducing interface friction.
Both discuss AI agents and protocols that let people operate business tools through natural language.
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Goillot describes an MCP for a phone system and shows how to use Claude to analyze calls, prioritize follow-ups and trigger sales actions.
Goillot's examples come mainly from sales, telecom and CRM workflows, and from growing a SaaS company through partnerships; Bidet's come from SME operations and AI-assisted development.
The posts point to a small, focused professional audience rather than broad reach, which shapes the placement decision.
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Cédric Bidet's LinkedIn profile showed about 4,800 followers when observed on 8 October 2026.
The five posts were published between 4 December 2025 and 12 May 2026 — roughly one a month, making this an active rather than high-volume account.
Written in French for people weighing AI adoption in business, the posts address the same professional conversation a B2B software company joins as it adds AI agents to its product.
The posts provide no data on who is reading: geography, age, gender, employer, job title, seniority, impressions, reactions or comments.
Before committing budget, ask for recent results on comparable topics and formats — impressions, reactions, comments and clicks — plus any audience survey covering employer type, job role and company size.
LinkedIn followers
4,806
Follower count shown on Cédric Bidet's LinkedIn profile · 2026-10-08
Cédric Bidet's posts contain no sponsorship or brand-partnership offers.
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Their commercial activity here is selling AI agent deployment to small and midsize businesses through Firmin.ai, described as starting at €150 per month with no commitment and an open invitation to get in touch.
This is a service offer for companies buying implementation work, not a rate card for sponsored posts, reviews, or brand placements.
For campaign planning, ask whether they accept sponsorships or brand collaborations and request a sponsor policy, rate card, media kit, or official partnership contact; none appears in these posts.
A campaign aimed at buyers at a B2B software company adding AI agents to its product, who want a creator to help buyers understand what those features should do and how to introduce them without creating new friction.
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Bidet fits this campaign closely, and the case rests on what they publish rather than on reach.
They run Firmin.ai, described as a shared platform for deploying AI agents and business tools for small and midsize companies, with personalized agents starting at €150 a month and no commitment.
Their most concrete post covers going from an idea to a tool they used daily in under two hours.
The point: coding was not the difficult part.
The platform already carried the stack, documentation, authentication, permissions, workspaces, technical and design conventions, and deployment, so each new tool inherited them.
What still cost time was environment setup, databases, domains and CI/CD, so they industrialized that too: after a spec is agreed, a script splits the project into GitHub issues, configures environments, prepares the technical base, then launches Claude Code with the project documentation.
That is precisely the argument a software company needs to make to buyers who expect a long, bespoke integration.
Bidet also writes about interface decisions, where most agent features succeed or fail.
The frame is a task, not a technology: rather than opening a CRM, adding filters, exporting an Excel file and cross-referencing it against another tool, they argue, users should be able to ask an agent in plain language to export the selection or start a campaign.
In the two-hour test, they wrote a prompt that checks email, detects important CRM opportunities, proposes actions and creates tasks in the to-do list — a compact, reusable illustration of what an agent feature should hand off to existing systems.
They note that graphical interfaces will not disappear tomorrow, that a validation step still needs one, and that the capability raises ethical questions.
A vendor seeking a credible, non-inflated account of agent UX will find that restraint useful.
Their differentiation is a third reason.
They open a post with a line heard often — "You do AI agents... a bit like everyone, no?" — agree they are neither first nor last, call the market a bazaar, and say what they bring is less: less jargon, less magic, fewer wow effects.
They describe the job as simplifying, deconstructing complexity and building controlled solutions, and insist an agent does not live in a vacuum but has to fit an organization's processes, economic context, confidentiality constraints and legacy systems.
For a software company competing in a noisy AI category, that is a usable position supported by their own published words.
The fit is about topic and demonstrated practice, not audience scale.
Their LinkedIn profile showed about 4,806 followers on 8 October 2026, and no impressions, reactions, comments or click figures are published for any post.
Treat this as a content-credibility placement and set the campaign around deliverables and a tracked link, not reach targets.
Ask for recent performance on comparable topics and formats, plus any audience survey covering employer type, job role and company size.
When to choose a specialist
A campaign for a mass-market consumer brand seeking a high-reach sponsored content push, with a documented creator package, audience demographics and rate card, and reporting against impressions and engagement.
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This is a poor fit for specific reasons.
No sponsorship formats appear in Bidet's posts.
Their commercial activity runs in the other direction: they sell AI agent deployment to small and midsize businesses through Firmin.ai, described as starting at €150 a month with no commitment, and invite people to get in touch about that service.
This is an offer to companies buying implementation work, not a media kit.
The posts include no sponsor policy, rate card, sponsored-content example, disclosure practice or booking route, so a campaign cannot be bought or scoped from what's published.
Ask Bidet directly whether they accept sponsorships or brand collaborations and request their terms.
The audience information also does not support a reach-led campaign.
The only audience figure available is roughly 4,806 LinkedIn followers on 8 October 2026, with no geography, age, gender, employer, job title, seniority, impressions, reactions, comments or clicks.
A consumer campaign that needs scale, demographic targeting or engagement benchmarks has nothing to plan against here.
The subject matter points the wrong way, too.
The posts are written in French for people weighing AI adoption in a business setting — SME executives lost in jargon, developers dealing with AI-assisted coding, product teams thinking about agent-first interfaces.
A consumer brand looking for lifestyle or mass-appeal content would be buying a professional audience whose size and composition are undocumented.
A related campaign need fails for a different reason: enterprise-scale AI governance or regulated-industry compliance credibility.
Bidet's examples involve small and midsize businesses, an email summary that files attachments, a meeting report with a personalized to-do list, and an e-commerce extraction consolidated with CRM and sales targets.
Those are operational, not enterprise governance, cases, and their posts say nothing about large-organization compliance or procurement.
Ask for evidence of relevant experience and audience and engagement reporting before considering either campaign.
A worked campaign example
Worked angle for a campaign aimed at a B2B software company adding AI agents to its product — "Remove the step, not the interface".
Deliverable: a three-post sequence in French over four to six weeks, each 400–700 words, plus one short case-study note the sales team can send to prospects. The through-line: an agent feature earns its place by deleting a step in an existing process, and the hard part of shipping one is repeatability, not the model.
Post 1 — "The task a user actually wants to skip".
Open with Bidet's comparison: opening a CRM, adding filters, exporting a spreadsheet and cross-checking it against a business tool versus asking an agent in plain language to export the selection or launch a campaign.
Include the working prompt as a copyable example — check email, detect important CRM opportunities, propose actions, create tasks in the to-do list.
Close by naming what the feature must hand off to: the CRM, the task list, the sales targets.
Product angle: position the agent as the layer between data and action.
Post 2 — "Two hours from idea to a tool people use".
Walk through the repeatable setup: the shared platform includes the stack, documentation, authentication, permissions, workspaces, conventions and deployment; the script splits the project into GitHub issues, configures environments and prepares the technical base before launching the coding assistant.
State Bidet's conclusion: the shift is not that AI codes fast, but that AI processes are becoming reproducible systems.
This post answers the integration objection and shows how a small team can standardize agent delivery.
Post 3 — "Where the validation step stays".
Use the limits Bidet describes: graphical interfaces will not disappear tomorrow, one validation step still needs a screen, and the capability raises ethical questions.
This gives the product team a credible talk track for launch.
Case-study note: a one-page summary built from the same material, aimed at SME buyers, leading with the client line Bidet quotes — that agents set up the month before helped honor orders during a staff injury — and ending with the platform's starting price and no-commitment terms as the commercial frame.
Success measure: because no engagement or impression data is published for these posts, set acceptance criteria on deliverables and one tracked link per post rather than on reach. Confirm directly whether Bidet offers a sponsored or paid-content arrangement; they describe a service business selling implementation, not a published sponsorship offer.
Next step for the brand: ask Bidet for recent performance on comparable topics and formats — impressions, reactions, comments and clicks — plus any audience survey covering employer type, job role and company size, and confirm the terms of any sponsored or paid-content arrangement.
Choose Jeremy Goillot if the software company’s agent feature is specifically for sales or business telephony.
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Their posts give concrete examples: analyzing call objections to revise a sales script, prioritizing follow-ups, and sending personalized appointment reminders from call transcripts. That makes the product use case easy to show through familiar sales tasks.
The examples focus on Allo’s telephone system and sales workflows. They provide less evidence for the broader agent-in-product approach Cédric Bidet describes, such as simplifying interfaces across different business tools or making agent deployment repeatable.
Choose Emmanuelle Benoliel if the campaign is about AI-supported marketing operations rather than software product design.
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Their posts describe an ABM campaign workflow linking customer and sales information to tailored messages and creative assets, then handing work to the technical team. That is a stronger fit when the buyer needs to see how AI can speed up campaign production and leave more time for improving the message.
The example is a marketing campaign workflow inside a company, not a demonstration of how a software product should integrate agents into its user experience. It is less directly useful for a product team deciding what an agent feature should do.
Choose Marie Robin if the software company wants to address agency growth and the systems behind lead generation.
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Their posts connect audience engagement to CRM data, lead qualification and sales follow-up, and discuss how agencies can use AI while improving their offer and client experience. This is a better route when the campaign’s buyer is an agency owner or marketing leader evaluating operational growth.
Emmanuelle Benoliel's examples target agency business models and marketing systems, not agent features in a B2B software product. They offer less direct material on product interaction, technical setup or validation.
This page draws on five LinkedIn posts by Cédric Bidet covering AI agents for small and midsize businesses, AI-assisted development, agent-first interfaces and the changing developer role.
It also covers work, ideas, business relationships and comparable creators.
The posts present statements about Cédric Bidet's business, tools and methods; claims such as Firmin.ai pricing and the client story are reported as published in the posts, not independently verified.
The posts shown here have no publication dates, so the page does not claim a timeline for when these views appeared or changed, and no engagement figures, audience demographics or sponsorship information appear in the posts here.
No signature book, show, or earlier career details appear in these posts, leaving an open gap for name searches.
