Question 1

What does 'deployed to your infrastructure' actually mean?

Accepted Answer

It means all code, data processing, and AI models run entirely on YOUR cloud accounts (GCP, AWS, or Azure) or on-premise servers. We deploy the solution, but you own and control the infrastructure. Your data never leaves your environment, never goes through our servers, and never touches external APIs like OpenAI.

Question 2

Which cloud providers do you support?

Accepted Answer

We have production experience with GCP, AWS, and Azure. We work with whichever cloud provider you're already using. We adapt to your infrastructure, not the other way around. Whether you use a single cloud or multiple cloud providers, we design solutions that fit your specific environment.

Question 3

Can you deploy to our on-premise data center?

Accepted Answer

Yes, if you have Kubernetes or similar container orchestration. Most of our solutions are containerized and cloud-agnostic. We design them to run on any infrastructure that can host Docker containers.

Question 4

Do we need to give you access to our cloud accounts?

Accepted Answer

During development and initial deployment, yes. We need temporary access to set up the infrastructure, deploy code, and test. Once deployed, you can revoke our access entirely. The system runs autonomously on your infrastructure. For ongoing support, we would need limited read-only access for troubleshooting.

Question 5

How do you guarantee our data never leaves our infrastructure?

Accepted Answer

Three ways: Architecturally, all processing happens in your cloud account using your compute resources. From a network perspective, we do not create any outbound connections to external services (except if you explicitly want integrations). Contractually, we put this in writing in the Statement of Work. You can audit the deployed code and network traffic to verify.

Question 6

What about LLM models? Don't they require API calls to OpenAI?

Accepted Answer

No. We deploy LLM models directly to your infrastructure. We can use Azure OpenAI Service within your own Azure tenant, so the data never leaves your environment. Alternatively, we can deploy open-source models (Llama, Mistral, etc.) entirely on your infrastructure with no external API calls whatsoever.

Question 7

Is this compliant with GDPR, KVKK, and other data regulations?

Accepted Answer

Since all data stays on your infrastructure under your control, YOU maintain compliance, not us. This is actually the key benefit: you do not have to trust a SaaS vendor with your sensitive data. You process everything in your own environment under your own data processing agreements.

Question 8

What data do YOU (CCB) have access to?

Accepted Answer

During deployment: we see sample or test data to build and validate the solution. After deployment: zero. We do not collect, store, or process your production data. The system runs entirely on your infrastructure. If you want us to provide ongoing support, we would need limited read-only access to logs for troubleshooting, but never to your actual data.

Question 9

How much does a custom AI solution cost?

Accepted Answer

It depends entirely on scope and complexity. A comprehensive multi-cloud cost intelligence system might be a multi-month project. Simpler RAG systems or chatbots could be 4 to 8 weeks. We do not have fixed pricing because every solution is custom-built for your specific needs. After understanding your requirements, we provide a detailed estimate in the Statement of Work.

Question 10

Do we pay recurring SaaS fees?

Accepted Answer

No. You pay for development and deployment (one-time or milestone-based), and optionally for ongoing support or maintenance. But there is no recurring per-user or per-API-call SaaS fee. You own the code and infrastructure.

Question 11

What about cloud infrastructure costs?

Accepted Answer

You pay your cloud provider (GCP, AWS, or Azure) for compute, storage, and other resources the solution uses. This is separate from our development fee. We design solutions to be cost-efficient, but ultimately you control the infrastructure and the costs. We can estimate typical monthly cloud costs during the Statement of Work phase.

Question 12

How do you calculate ROI?

Accepted Answer

Honestly: we do not use generic formulas. ROI depends on YOUR specific situation. Current costs, manual processes being automated, team time freed up, and risks reduced. We work with your actual numbers to estimate realistic savings and payback period. For example, ROI might come from eliminating manual cost tracking, catching anomalies early, or automated reporting that saves hours per week.

Question 13

How long does it take to build and deploy a custom AI solution?

Accepted Answer

Typical timeline: 2 to 4 months for a complete enterprise system with multiple integrations. Simpler solutions (chatbot, RAG system) might be 4 to 8 weeks. Complex integrations or multiple clouds can take longer. We provide a detailed timeline in the Statement of Work after understanding your requirements.

Question 14

What's the development process?

Accepted Answer

Our development process has six phases:

• Discovery & requirements gathering (1 to 2 weeks)
• Architecture design & Statement of Work (1 week)
• Development in phases with regular demos (6 to 12 weeks)
• Deployment to your infrastructure (1 to 2 weeks)
• Testing & validation (1 to 2 weeks)
• Handoff & training (1 week)

We work iteratively with regular check-ins, typically every 2 weeks.

Question 15

Can we see progress during development?

Accepted Answer

Yes. We work in phases with regular demos (usually every 2 weeks). You will see working functionality throughout the project, not just at the end. We deploy to a staging or development environment in your infrastructure so you can test as we build.

Question 16

What happens after deployment?

Accepted Answer

We provide training for your team, comprehensive documentation, and a handoff period. Optionally, we offer ongoing support or maintenance contracts. But the system is yours. You can maintain it yourself, hire someone else, or keep working with us. No lock-in.

Question 17

What technologies do you use?

Accepted Answer

We use cloud-native services: Cloud Functions, Lambda, Azure Functions, and similar services from each cloud provider. For portability, we use Docker and Kubernetes containers. Our primary language is Python for data and AI work. For databases, we use PostgreSQL, Cloud SQL, BigQuery, or whatever fits your infrastructure. We adapt to your existing tech stack.

Question 18

Can you integrate with our existing systems?

Accepted Answer

Yes, that is the point of custom development. We have experience integrating with collaboration tools like Microsoft Teams and Slack, business intelligence platforms like Power BI and Tableau, various databases (SQL and NoSQL), cloud APIs, and internal REST or GraphQL APIs. Integration requirements are defined during the discovery phase.

Question 19

Do you use open-source or proprietary tools?

Accepted Answer

Primarily open-source: Python libraries, open-source LLMs (if you prefer), standard databases, and similar tools. This avoids vendor lock-in and licensing costs. For cloud services, we use whatever your cloud provider offers. We do not introduce proprietary tools that require ongoing licensing from third parties.

Question 20

What if we want to modify the solution later?

Accepted Answer

You own the code. We provide full documentation and can train your team. You can modify it yourself, hire another developer, or continue working with us. We design solutions to be maintainable and extensible, not black boxes.

Question 21

How do we get started?

Accepted Answer

The process typically follows these steps:

• Schedule a free consultation where we discuss your needs, infrastructure, and requirements
• We show you relevant case studies and explain how a similar approach would work for your situation
• If it makes sense, we conduct a paid discovery phase (1 to 2 weeks) to deeply understand your specific needs
• We provide a detailed Statement of Work with timeline, costs, and deliverables
• If you approve, we start development

The entire sales process is transparent and focused on finding the right fit.

Question 22

Do you work with small companies or just enterprises?

Accepted Answer

Our solutions are best suited for organizations with: cloud infrastructure already in place, sensitive data that cannot go to external APIs, budget for custom development (typically $50K+ projects), and internal teams who will maintain or own the solution long-term. If that describes you, company size does not matter.

Question 23

What if custom AI isn't right for us?

Accepted Answer

We will tell you. Not every situation needs custom development. If a SaaS tool solves your problem, we will say so. If your data is not sensitive enough to justify on-premise deployment, we will say so. We would rather have an honest conversation than take on a project that does not make sense.

Question 24

Can we see references or talk to past clients?

Accepted Answer

We have a detailed case study on our website (client name anonymized for confidentiality). For specific reference calls, we would need to check with the client first, but we are happy to facilitate that conversation during the sales process if it helps your decision.

Your Questions About Custom AI Answered

Deployment & Infrastructure

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