Fayora AI Frequently Asked Questions

Question 1

What does Fayora AI do?

Accepted Answer

Fayora AI helps businesses leverage artificial intelligence to solve real problems and drive measurable results. We design, develop, and deploy custom AI solutions including business automation, AI-powered CRM systems, intelligent agents, custom mobile and web applications, data engineering pipelines, and AI strategy consulting. We focus on practical, production-ready solutions that integrate seamlessly with your existing systems.

Question 2

What types of companies do you work with?

Accepted Answer

We work with businesses of all sizes — from growing startups to established enterprises — across industries including finance, healthcare, e-commerce, manufacturing, retail, and SaaS. Our ideal clients are companies looking to leverage AI strategically, whether that means automating processes, enhancing customer experiences, or unlocking insights from data. We've successfully delivered projects for companies ranging from 10 to 10,000+ employees.

Question 3

What makes Fayora AI different from other AI consultancies?

Accepted Answer

We combine deep AI expertise with a business-first mindset. Unlike consultancies that prioritize technical sophistication over outcomes, we measure success by your results, not our code. We focus on proven, production-ready AI solutions rather than experimental technology. Our approach includes radical transparency, systematic delivery methodology, comprehensive documentation, and ongoing support — we're invested in your long-term success, not just project delivery.

Question 4

Do you only offer consulting, or do you build solutions too?

Accepted Answer

We do both. We can provide AI strategy consulting to help you identify opportunities and build a roadmap. But we also design, develop, and deploy complete end-to-end solutions. Most of our clients engage us for full implementation — we handle everything from architecture to deployment, training, and ongoing support.

Question 5

What AI technologies and models do you use?

Accepted Answer

We select the best technology for your specific use case. This includes large language models (GPT, Claude, Llama), custom machine learning models, computer vision systems, natural language processing, and predictive analytics. We work with leading platforms including OpenAI, Anthropic, Google Cloud AI, Azure AI, and AWS, as well as open-source frameworks like TensorFlow, PyTorch, and Hugging Face. The choice depends on your requirements for accuracy, cost, latency, and data privacy.

Question 6

Can your AI solutions integrate with our existing systems?

Accepted Answer

Yes. Integration is a core part of every project. We design solutions that work with your existing tech stack — whether that's ERPs like SAP or Oracle, CRMs like Salesforce or HubSpot, databases, cloud platforms, or custom applications. We use standard APIs, webhooks, and middleware to ensure seamless data flow. During the discovery phase, we audit your systems and design integration architecture that minimizes disruption.

Question 7

How do you ensure AI decisions are explainable and trustworthy?

Accepted Answer

AI explainability is critical for trust, compliance, and debugging. We implement several approaches: model interpretability techniques (SHAP, LIME), decision logging and audit trails, confidence scores for predictions, human-in-the-loop validation for critical decisions, and comprehensive documentation of model behavior. For regulated industries, we ensure our models meet compliance requirements for transparency and auditability.

Question 8

Do you build custom AI models or use pre-trained ones?

Accepted Answer

Both, depending on what's most effective for your needs. Pre-trained models (like GPT or Claude) are excellent for natural language tasks and can be fine-tuned for your use case. Custom models are necessary when you need highly specialized behavior, have proprietary data, or require complete control. We recommend the approach that balances performance, cost, time-to-market, and maintainability. Many projects use a hybrid approach.

Question 9

How accurate are AI models, and what happens if they make mistakes?

Accepted Answer

AI accuracy varies by use case, data quality, and model design. We establish target accuracy metrics during discovery and validate rigorously during testing. For critical applications, we implement confidence thresholds, human review workflows, and fallback mechanisms. We also set up monitoring to detect accuracy degradation in production. Importantly, we design systems that fail gracefully and provide audit trails so mistakes can be reviewed and corrected.

Question 10

How do you ensure our data is secure?

Accepted Answer

Security is built into every project from day one. We follow industry best practices including encryption in transit and at rest, role-based access controls, secure API design, regular security audits, and compliance with standards like SOC 2, ISO 27001, and GDPR. We can deploy solutions in your own infrastructure (on-premise or private cloud) for maximum control. All team members sign NDAs, and we never use client data for other purposes or model training without explicit permission.

Question 11

Who owns the data and the AI models you build?

Accepted Answer

You do. All data remains your property, and any custom models, code, or intellectual property created for your project belongs to you. This is clearly stated in our contracts. We can discuss licensing for reusable components if you prefer, but by default, you have full ownership and control. We provide complete source code, documentation, and model artifacts as part of project delivery.

Question 12

Are your solutions compliant with GDPR, HIPAA, and other regulations?

Accepted Answer

Yes. We design solutions with compliance in mind from the start. We have experience with GDPR (data privacy), HIPAA (healthcare), SOC 2 (security controls), PCI DSS (payment data), and industry-specific regulations. During discovery, we identify your compliance requirements and architect solutions accordingly. This includes data anonymization, consent management, audit logging, and documentation needed for regulatory reviews.

Question 13

If you use third-party AI services, how is our data protected?

Accepted Answer

When using third-party AI services (like OpenAI or Anthropic), we ensure data protection through: Business Associate Agreements (BAAs) for HIPAA compliance, opt-out from model training, data encryption, data minimization (only sending necessary information), and using private deployments when needed. For highly sensitive data, we can deploy models entirely within your infrastructure or use air-gapped solutions that never send data externally.

Question 14

How much does an AI project cost?

Accepted Answer

Project costs vary based on scope, complexity, and timeline. Small automation projects might start at $25,000-$50,000, while comprehensive AI platforms can range from $100,000-$500,000+. Factors affecting cost include data complexity, integration requirements, custom model development, and ongoing support needs. We offer transparent pricing and provide detailed estimates after the discovery phase. We also offer flexible engagement models including fixed-price projects, time-and-materials, and retainer-based partnerships.

Question 15

How long does a typical project take?

Accepted Answer

Timelines depend on project scope. A focused automation or AI integration might take 6-12 weeks. More complex systems like custom AI platforms or enterprise-wide solutions typically take 3-6 months. We break projects into phases with regular deliverables, so you see progress continuously rather than waiting until the end. During discovery (1-2 weeks), we create a detailed timeline with milestones. We prioritize delivering working software early and iterating based on feedback.

Question 16

What engagement models do you offer?

Accepted Answer

We offer three primary models: (1) Fixed-Scope Projects — defined requirements, fixed timeline and budget, best for well-understood needs. (2) Iterative Engagements — phased delivery starting with MVP, flexible scope, lower risk, ideal for exploratory projects. (3) Long-Term Partnerships — dedicated team resources, ongoing development and optimization, best for strategic initiatives. We help you choose the model that fits your needs, budget, and risk tolerance.

Question 17

What are your payment terms?

Accepted Answer

For fixed-scope projects, we typically structure payments in milestones (e.g., 30% at kickoff, 40% at development completion, 30% at final delivery). For iterative or retainer engagements, we invoice monthly. We're flexible and can adapt to your procurement requirements. We don't require full payment upfront — our milestone-based approach aligns our incentives with your success.

Question 18

Are there ongoing costs after the project is delivered?

Accepted Answer

Yes, but they're transparent and predictable. Ongoing costs may include: infrastructure hosting (AWS, Azure, etc.), third-party AI API usage (if applicable), monitoring and maintenance, and optional support retainers. We design solutions to be cost-efficient in production. During the project, we provide estimates for operational costs so there are no surprises. You can choose to manage these costs yourself or include them in a support agreement with us.

Question 19

What is your delivery process?

Accepted Answer

We follow a proven 5-phase methodology: (1) Discovery & Analysis — understand your business, assess data, define success. (2) Solution Design — architecture, AI approach, integration planning. (3) Development & Integration — build, test, weekly demos. (4) Testing & Validation — functional testing, accuracy validation, UAT. (5) Deployment & Support — production rollout, monitoring, training, handover. This structured approach ensures predictability while remaining flexible to your needs. See our "How We Work" page for details.

Question 20

How often will we communicate during the project?

Accepted Answer

Communication is continuous and transparent. Expect weekly status updates, bi-weekly progress demos, daily async communication via Slack or Teams, and always-available project dashboards. You'll have access to our project management tools, code repositories, and progress tracking. We believe surprises are bad — you should always know what's happening, what's next, and where we stand against timeline and budget.

Question 21

What support do you provide after launch?

Accepted Answer

We offer several support options: (1) Warranty Period — typically 30-90 days of bug fixes included in project cost. (2) Support Retainers — ongoing monitoring, maintenance, and minor enhancements. (3) On-Demand Support — pay-as-you-go for specific issues. We also provide comprehensive documentation, training for your team, and knowledge transfer so you can maintain the solution internally if preferred. Our goal is to make you self-sufficient, not dependent.

Question 22

Do you train our team to use and maintain the solution?

Accepted Answer

Absolutely. Knowledge transfer is built into every project. We provide user training for end-users, technical training for your IT/engineering team, documentation including architecture diagrams, API references, and operational runbooks, and admin guides for system management. We want your team to feel confident operating and maintaining the solution. Follow-up training sessions can be arranged as needed.

Question 23

Can the solutions you build scale as our business grows?

Accepted Answer

Yes. Scalability is a core design principle. We architect solutions using cloud-native approaches, containerization, microservices, and auto-scaling infrastructure. We also consider data volume growth, concurrent user increases, and geographic expansion. During design, we model future load and build accordingly. Many of our clients start with pilots and scale to enterprise-wide deployments — our solutions are built to grow with you.

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Frequently Asked Questions