Flyaps

Expertise

Flyaps combines expertise in:

• Business process automation
• Software engineering
• Cloud technologies
• Artificial intelligence
• Machine learning

The team focuses on building future-proof products that are cost-effective, easy to use, maintain, and scale. Its approach includes user-centric design, cloud-native development, AI and machine learning, and efficient delivery.

Software development services

• SaaS product development: UI/UX design, Python and React development, cloud and DevOps
• Application modernization: API integrations, cloud migration, and technology modernization
• Team augmentation: Python developers, React developers, and DevOps engineers
• Business automation: Robotic process automation, custom AI solutions, data engineering, predictive analytics, and business intelligence systems

Industry domains

Flyaps has experience in:

• Telecom: Data-driven solutions and business support systems
• HR tech: AI-based recruitment automation and hiring platforms
• Media: Bespoke content management systems

Flyaps reports a 4.9 average client satisfaction score and has degrees in mathematics and computer science represented on its team.

• Custom AI application development: Machine learning developers and data experts create models using a client's exclusive data and work with in-house specialists.
• External AI tool integration: Integration of third-party software, APIs, or open-source code.
• Flyaps AI solutions: Pre-built machine learning models that can be customized for specific industries or business challenges.

Flyaps can build an AI proof of concept through an Agile pilot in an average of three months to test project feasibility.

AI capabilities

• Generative AI: Models that generate text, images, and other content, including pre-built LLMs and full-scale systems
• Advanced analytics: Predictive analytics, business forecasting, credit scoring, and anomaly detection
• Intelligent automation: Intelligent document processing, predictive maintenance, and visual intelligence using machine learning and RPA

Use cases

Flyaps covers AI use cases in telecom, HR tech, media and retail, logistics and warehousing, urban planning, and cross-industry applications. Examples include resume screening, recommendation systems, route optimization, fraud detection, customer support automation, document understanding, and time series analysis.

MLOps and lifecycle support

Flyaps provides MLOps, ModelOps, and ML governance services, including cloud cost optimization, security and compliance, automated governance, deployment, monitoring, support, and maintenance.

The AI development process covers opportunity discovery, solution design, proof-of-concept development, engineering, and production deployment.

Technologies

Flyaps specializes in Python and works with TensorFlow, PyTorch, Keras, and scikit-learn. Technology selection depends on the project.

Security

AI development security measures include encryption in transit and at rest, removal of unsafe or personally identifying data, zero-trust security, authentication, secure data supply chains, access controls, security audits, and incident response protocols.

Flyaps develops pre-built and custom generative AI models for enterprises, including businesses in finance, telecom, aerospace and defense, and other regulated industries. Its pre-built components are described as reducing time-to-market by up to 30%.

Services

• Generative AI consulting
• LLM development
• Natural language processing
• AI chatbot development
• AI agent development
• MLOps
• Data engineering

Capabilities

• Model assessment and strategy: Solution blueprints, model evaluation, hallucination and bias testing, factuality checks, and LLM benchmarking
• Model training and development: Retrieval-Augmented Generation, model training, secure data pipelines, multimodal solutions, and custom model development
• Security and compliance: GDPR and EU AI Act alignment, protection against data leaks, misuse, and prompt attacks
• Post-deployment: Instruction tuning, human-in-the-loop and RLHF approaches, monitoring, version control, CI/CD, drift detection, and retraining

Flyaps can develop solutions that process text, images, audio, and other input types. Its AI agents can generate reports, update dashboards, trigger workflows, and search internal systems. Chatbots can support internal workflows and customer-facing applications.

Process

The generative AI lifecycle includes opportunity identification, proof-of-concept development, evaluation, deployment, MLOps, and ongoing improvement. Flyaps states that it can build a proof of concept within weeks and scale to full production with MLOps in under a year.

Domains and use cases

Flyaps supports generative AI applications in telecom, HR technology, retail, media, urban planning, finance, aerospace and defense, energy, and agriculture. Use cases include customer support agents, candidate screening, product descriptions, content recommendations, investment summaries, threat intelligence reporting, energy forecasting, and agricultural diagnostics.

Own R&D model

Flyaps has developed a generative AI model for visual content creation. The model can restyle images, improve resolution, fill in missing pieces, and imitate creative methods such as pencil drawing. Potential applications include e-commerce photo enhancement, stylized filters, image repair, and content creation platforms.

Pricing model

Flyaps states that generative AI development engagements use a Time and Material model, meaning clients pay for the work performed. The company says pricing is approximately 55–70% below U.S. on-site employment cost, with a customized quote available after discussing project requirements.

Flyaps provides MLOps consulting to audit machine learning setups and implement scalable, end-to-end machine learning operations. Services include deploying, monitoring, and continuously updating models in production.

Flyaps reports the following MLOps outcomes:

• 40% faster deployment time
• 4X better adaptability
• 60% reduction in degradation incidents

MLOps capabilities

• Data engineering
• Model implementation
• ML pipeline development
• Model deployment
• Model monitoring
• Model versioning
• Experiment tracking
• Data and model validation
• Governance, compliance, audit trails, and access controls
• CI/CD, automation, and orchestration

Flyaps can implement MLOps across on-premises and cloud-based environments, and can manage versioning for models, datasets, and code. Monitoring solutions can include real-time alerts and dashboards, while data drift detection can trigger retraining workflows.

MLOps audit

The MLOps audit assesses architecture, code and configuration, pipeline execution, data and feature stores, monitoring, cloud costs, risks, security, uptime, and compliance. The audit process has nine stages, from a kickoff call through an action plan with a 30/90/180-day roadmap.

Flyaps states that an audit produces a tailored report within two weeks, including practical next steps, tool recommendations, and a roadmap.

Service options

MLOps audit

• From $299
• Duration: two weeks
• ML pipeline review
• Risk scoring
• Improvement backlog

If a client hires Flyaps after the audit, 70% of the audit fee is credited toward the next project.

MLOps as a Service

• From $399 per month
• Minimum duration: three months
• Dedicated MLOps architects and engineers
• 24/7 monitoring
• Cost optimization
• Pipeline upkeep
• Monthly performance reports
• Ongoing roadmap execution

On-demand MLOps

• From $799 per month
• Minimum duration: two months
• Work scoped to current needs
• Transparent time tracking and billing
• Monthly progress reports
• Response and turnaround times depend on availability

Flyaps also offers a free 60-minute consultation with its MLOps experts.

The website provides options to get in touch, book a free consultation, and request a call.