Genaiprotos

GenAI Protos has deployed AI systems for Fortune 20 companies and helps organizations validate and scale AI use cases. Its team includes trusted advisors, GenAI SME architects, GenAI developers, and data engineers.

Mission

GenAI Protos helps businesses move from idea to intelligent application in weeks through hands-on engineering, strategic advisory, and on-demand innovation. The company focuses on practical solutions, rapid prototyping, and production-ready outcomes rather than consulting decks.

Background

The founding team began with more than 20 years of data engineering and analytics expertise. During a planet-scale data platform migration involving more than 300,000 legacy scripts, the team developed a migration accelerator using large language models and custom automation components. This work later expanded into LLM-driven productivity tools, sentiment analysis, chatbots, and multimodal applications.

Core Values

• Championing client success
• Passion for innovation
• Doing it right every time
• Integrity and transparency

GenAI Protos works with enterprise and technology leaders, global system integrators, entrepreneurs with GenAI app ideas, and business owners exploring AI solutions.

The company describes the following delivery targets:

• Working prototype in 3 days
• Production MVP in 2–4 weeks
• Project delivery that can be 30–50% faster

GenAI Protos also provides prototypes on a client's chosen platforms, tools, and LLMs; realistic proofs of concept using data that closely replicates real-world data; detailed documentation, source code, and project plans; mentoring for GenAI teams; and structured roadmaps from idea to deployment.

Contact

• Email: contact@genaiprotos.com
• Locations: Irvine, California, USA; Pune, Maharashtra, India; Chennai, Tamilnadu, India

Visitors can book an AI discovery session, schedule a consultation, schedule a data strategy call, or discuss an AI use case. Project inquiries request a name, email, phone, country code, company, service or area of interest, and a brief use-case description.

Its AI-augmented delivery model is designed to reduce data modernization discovery from months to days, compress ETL development timelines by 3x to 5x, and produce architecture-grade outputs that client teams can own, extend, and run in production.

Delivery Areas

• ETL automation: Generate, refactor, and optimize data scripts and transformation workflows.
• Legacy system modernization: Decode undocumented SQL logic and convert legacy code from platforms such as Teradata and Oracle to modern stacks including Snowflake and PySpark.
• Data modeling and lineage: Accelerate schema design, data mapping, metadata extraction, and lineage documentation.
• Data platform migration: Convert and migrate codebases across platforms, including stored procedures to PySpark, Teradata to BigQuery, and Oracle to Snowflake.
• Testing and quality automation: Generate test cases, synthetic datasets, regression validations, and data-quality checks.
• Documentation and knowledge management: Generate pipeline documentation, transformation logic, data dictionaries, and technical specifications.

Accelerators

• Code Converter: An AI agent that orchestrates SQL-to-PySpark conversion, validates outputs, refines mismatches, and streams results to engineers.
• Intelligent Data Dictionary: Connects to databases, analyzes schemas, creates data dictionaries, performs PII tagging and quality profiling, and documents dataset relevance.
• Fast Data Catalogue: Discovers, documents, and explains enterprise data across sources.

Architecture Options

GenAI Protos works with data lakehouse architectures, cloud data warehouses, and AI-ready data foundations. Platform examples include Databricks, Microsoft Fabric, Delta Lake, Snowflake, Google BigQuery, and Amazon Redshift.

Services include:

• Rapid AI prototyping and development
• Production-grade AI architecture
• Custom LLM integration and fine-tuning
• AI agents and agentic workflows
• Retrieval-augmented generation (RAG) applications
• AI copilots and intelligent automation
• Multimodal AI and voice AI
• MLOps, monitoring, and ongoing support

Development Process

1. Discovery and use-case definition: Understand business objectives, technology stack, data assets, constraints, success metrics, and technical feasibility.
2. Architecture and technology selection: Select appropriate LLMs, frameworks, data pipelines, APIs, and infrastructure, with security, scalability, and compliance considered from the start.
3. Rapid prototyping and validation: Build a functional prototype with source code and validate its behavior against real data.
4. Full-stack development and integration: Build the frontend, backend, AI model layer, data pipelines, and APIs, then integrate with existing systems, databases, and workflows.
5. Testing, security, and deployment: Test model accuracy, API performance, security, load handling, and user experience before deployment.
6. MLOps and support: Implement model monitoring, performance tracking, retraining triggers, maintenance, updates, and capability extensions.

Example Applications

GenAI Protos has built or describes the following applications:

• ScheduleWise: AI calendar automation that unifies multiple calendars, prevents double-bookings, enforces business rules, and maintains audit trails.
• Legal AI Assistant: Document analysis and legal research that supports contract review, clause extraction, risk identification, and precedent analysis.
• FactPulse: Multi-agent fact checking that retrieves evidence, verifies claims, and provides verdicts with confidence scores.

AI development engagements span healthcare, legal, finance, retail and eCommerce, SaaS and software products, and software engineering.

Private AI services include on-premise and air-gapped AI deployments. Edge AI services run AI directly on cameras, sensors, industrial hardware, and other edge devices to reduce latency, bandwidth usage, and cloud dependency.

Services

• Strategic edge AI architecture and use-case design
• Edge AI application development for NVIDIA DGX Spark, Jetson, Google Coral, and TinyML platforms
• Platform selection, proof-of-concept development, and feasibility assessment
• Edge AI testing, accuracy validation, and hardware compatibility verification
• Integration with IoT infrastructure, industrial systems, 5G MEC networks, and hybrid edge-cloud architectures
• Containerized deployment, Edge MLOps, fleet management, and over-the-air model updates
• Edge data pipelines, preprocessing, caching, and privacy-preserving storage
• Team training in edge AI, on-device optimization, TinyML, and Edge MLOps

Deployment Process

The process covers requirements assessment, architecture design, model optimization and platform validation, development and integration, testing and validation, and deployment with MLOps support.

Testing can cover model accuracy, inference latency, data isolation, access control, reliability, offline operation, and on-device performance.

Example Solutions and Industries

Examples include local multilingual voice AI, local LLM inference on NVIDIA hardware, and Spark Vault, an on-premises enterprise search solution for medical documents.

Private and edge AI solutions serve healthcare, financial services, legal, manufacturing and industrial organizations, and software engineering teams.