Why this idea?
Since the arrival of ChatGPT, a question often arises: "Why not develop our own conversational agent, connected to our tools and data?"
The idea is appealing, especially after a stunning demonstration. But between proof of concept and a truly operational solution, the gap is enormous.
Creating an effective AI chatbot is not a simple "side project." It is a complex product to design, maintain, evolve, secure, and supervise.
Therefore, should we build or buy? Here are the key questions and concrete elements to guide you.
The costs of an internal project
Even in an optimistic scenario, an internal project is never trivial. Let's take the example of a company wishing to deploy a chatbot for its customer service:
Project team formation: product managers, engineers, IT managers, security, compliance.
Framing workshops involving business and IT departments to define scope, requirements, and use cases. After about ten meetings, the team finally has its specifications.
Initial technical tests to validate feasibility and quality of responses.
Development phase (development, tests, adjustments, internal validations).
Production launch often delayed because the project is not the absolute priority of the teams involved.
Post-launch phase: user feedback, bug fixes, constant adjustments.
Each step mobilizes resources – human, material, and organizational – and generates costs that are often underestimated. Delays and unforeseen changes can easily strain the budget and postpone ROI.
Comparison: Build vs Buy
Language models (LLMs) have changed the game by enabling the real automation of certain human tasks. However, many teams underestimate the resources needed to achieve a level of performance comparable to the best solutions on the market.
Concrete comparison: In-house Chatbot vs Industrial Chatbot
Criteria | In-house Chatbot | SaaS Solution |
|---|---|---|
Autonomous resolution rate | 35–50 % | 70–85 % |
Quality of responses (accuracy, coherence) | Variable | High |
Long conversation management (complex cases) | Limited (3-4 exchanges max) | Advanced (15-20 exchanges) |
Time to production | 8-12 months | 4-8 weeks |
Integration with IT systems (CRM…) | To develop | Native |
Analytics and performance tracking | To build | Integrated |
Maintenance and evolution | To take charge | Included |
Security and GDPR compliance | Complex, risk of breach | Automatic and certified |
*Performance measured on ViaSay clients over the last 12 months.
The business impact of this difference: An improvement of 20-30% in the autonomous resolution rate can represent the equivalent of 1 to 2 positions of agents saved. At €45,000 per agent per year (salary, charges, training, tools), the performance gap quickly amounts to tens of thousands of euros annually.
Budget simulation: even in an optimistic scenario
Let's imagine an internal project with no major unforeseen events for a customer service needing 100,000 conversations per year.
Development phase (6 months)
Team: 1 project manager, 1 product manager, 2 senior engineers
Charged cost: €75,000/year/person
Development cost: €150,000
Recurring costs (per year)
Infrastructure and LLM APIs: €36,000
Maintenance and evolutions (0.5 FTE): €37,500
Budget comparison over 4 years for 100,000 conversations per year
Option | Initial investment | Annual recurring cost | Total cost |
|---|---|---|---|
Internal development | €150,000 | €73,500 | €444,000 |
SaaS Solution (e.g., ViaSay) | €5,000 | €25,000 | €105,000 |
👉 Total gap over 4 years: -€339,000
In which cases does building make sense?
Rare specific cases can justify internal development, provided that these conditions are met:
1. High-level native technical expertise
The company must have experienced ML/AI teams for several years.
PayFit, a French unicorn in the HR sector, perfectly illustrates this profile: 841 employees including 124 engineers, and a recently developed AI assistant that successfully began simply: question/answer based on knowledge, without complex connections to systems.
2. Critical volume and substantial budget
Minimum 500,000 interactions/month and a budget exceeding €500,000/year. Below that, a specialized team is not cost-effective compared to SaaS solutions.
3. Unavoidable regulatory constraints
Highly regulated sectors where data legally cannot leave the internal infrastructure. Even in such cases, on-premise solutions often exist.
4. Strategic competitive differentiation
The chatbot must constitute a direct competitive advantage, not just a lever for profitability and operational efficiency. Example: software publisher integrating conversational AI as a core feature of its offer.
Market reality: less than 2% of French companies meet these conditions. Even well-funded tech scale-ups regularly fail due to insufficient AI expertise or massive underestimation of operational complexity.
✔ Build in-house if:
You have a senior AI team already in place
You exceed 500k conversations/month
You are in a sector subject to extreme sovereignty constraints
The chatbot is strategic for your product or offering
Key takeaways
Building your own AI chatbot may seem attractive and technically challenging, but in the vast majority of cases, it can degrade user experience, generate costs 3 to 4 times higher than initial estimates, and significantly delay reaching ROI.
Buying a proven solution allows your teams to focus on their core business, guarantees your clients an optimal experience from the start, and offers total budget predictability.
Considering a turnkey solution?
ViaSay offers high-performing AI chatbots that are easy to integrate, customizable, and immediately operational. Our clients achieve an average of 75% autonomous resolution in less than a month. Why not try it out today?
👉 Request a personalized demonstration today.
