01
Use-case clarity
Every project starts with the real user, operational, or product problem to solve.
LuMay AI
Generative AI Development
Services for Startups
Build intelligent products that solve real customer and operational problems.
LuMay plans, prototypes, develops, and integrates custom Generative AI solutions for startups across SaaS, FinTech, healthcare, technology, and e-commerce, from an initial proof of concept to a production-ready application with practical functionality, controlled cost, and a clear path to scale.
- Startup-focused product development
- Rapid prototyping and validation
- Model-agnostic architecture
- Scalable, integration-ready solutions
Why It Takes More Than a Model
Turn a Practical AI Use Case into a Working Product
02
Knowledge and data
Retrieval, context, and approved data sources shape whether the system can be trusted.
03
Application engineering
Useful AI products need interfaces, business logic, integrations, and fallback behavior.
04
Evaluation and monitoring
Quality, cost, latency, and safety need ongoing visibility after launch, not just at demo time.
Our Generative AI Development Services
End-to-End Generative AI Capabilities
From roadmap and prototypes to RAG systems, agents, deployment, and optimization, we build startup-ready generative AI systems around real business workflows.
Your AI SystemYour Business
ObjectiveValue · Data · Users
ObjectiveValue · Data · Users
01 / 05
Generative AI Consulting & Strategy
We evaluate goals, workflows, data, and constraints, then prioritize the right use cases and delivery path before deeper build work starts.
Business OutcomeA practical roadmap shaped by feasibility, risk, business value, and startup-stage realities.
Included capabilities- Generative AI Consulting & Strategy
- Proof of Concept
- Proof of Value
Our Generative AI Development Services
End-to-End Generative AI Capabilities
From roadmap and prototypes to RAG systems, agents, deployment, and optimization, we build startup-ready generative AI systems around real business workflows.
01 / 05
Generative AI Consulting & Strategy
We evaluate goals, workflows, data, and constraints, then prioritize the right use cases and delivery path before deeper build work starts.
Business OutcomeA practical roadmap shaped by feasibility, risk, business value, and startup-stage realities.
Included capabilities- Generative AI Consulting & Strategy
- Proof of Concept
- Proof of Value
Generative AI Solutions and Use Cases
Common Product and Workflow Patterns We Build
Enterprise & Product Knowledge Assistants
Customer-Service Automation
Sales & Marketing Copilots
Document Processing & Summarization
Intelligent Search & Discovery
Personalized Content Generation
Product Recommendation Systems
Software Development Copilots
Contract & Compliance Analysis
Employee Support Assistants
Workflow Automation
Synthetic Data Generation
Image, Audio & Video Applications
Decision-Support Systems
Business Benefits
Why Startups Invest in Generative AI Product Delivery
Faster knowledge access for users
Less repetitive drafting, classification, and data work
Contextual assistance inside the tools teams already use
Consistent customer experiences with clear escalation rules
Faster research, prototyping, and content workflows
Relevant personalization within defined business rules
Automation that scales with request volume
Structured, reviewable insights for better decisions
Retrieval and access built around approved company data
Faster, lower-risk experimentation before a full build
Our Development Process
From Discovery to Optimization
A startup-ready generative AI engagement needs a path that validates the use case, tests the product shape, and still leads cleanly into production.
- Step 01
Discovery and Business Analysis
We examine users, the business problem, and constraints to produce a documented problem statement and initial scope.
DeliverableProblem statement01 - Step 02
Use-Case Prioritization
We assess options by value, feasibility, and risk to recommend a focused first phase.
DeliverablePrioritized roadmap02 - Step 03
Data and Infrastructure Assessment
We review data quality, ownership, and architecture to produce a readiness plan.
DeliverableReadiness plan03 - Step 04
Architecture and Model Selection
We compare models, retrieval approaches, and deployment options for the best balance of quality, cost, and scale.
DeliverableSystem architecture04 - Step 05
Proof of Concept or Proof of Value
A proof of concept tests whether the approach works; a proof of value also tests whether it is worth building.
DeliverableValidation decision05 - Step 06
Prototype and User Validation
A functional version lets real users test core workflows, shaping usability and priorities.
DeliverableValidated prototype06 - Step 07
Development and Integration
We build the production application, APIs, data pipelines, and integrations.
DeliverableIntegrated application07 - Step 08
Testing, Evaluation, and Guardrails
We test quality, security, latency, and edge cases, keeping human approval where errors carry real risk.
DeliverableQuality baseline08 - Step 09
Deployment and Team Enablement
We deploy to your chosen environment and make sure your team understands how to run and maintain it.
DeliverableProduction launch09 - Step 10
Monitoring and Optimization
We track usage, quality, and cost after launch, and refine the system as requirements evolve.
DeliverableOptimization loop10
Technology Stack
Model-Agnostic by Design
Technology is chosen for the use case, not forced into one stack. Final selection depends on data sensitivity, workload, budget, latency, and your team's skills.
Foundation models
OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, Cohere, and other suitable open-source models
Frameworks
LangChain, LlamaIndex, Semantic Kernel, Hugging Face, PyTorch, TensorFlow
Retrieval and vector databases
Pinecone, Weaviate, Milvus, Chroma, pgvector, Elasticsearch
Cloud and infrastructure
AWS, Azure, Google Cloud, Docker, Kubernetes, private and hybrid environments
Data and MLOps
Databricks, MLflow, Apache Airflow, evaluation and monitoring tools
Security, Responsible AI, and Governance
Manage Risk Without Freezing Delivery
Generative AI risk cannot be removed completely, but it can be managed through careful architecture, testing, access control, and human oversight.
Operational safeguards
- Encryption in transit and at rest; role-based access and secure authentication
- Permission-aware retrieval and data-retention controls
- Prompt-injection testing, input/output validation, and sensitive-data filtering
- Rate limits, abuse detection, and audit logs
- Hallucination evaluation, source attribution, and human approval workflows
- Bias and quality assessment, with explainability appropriate to the use case
- Model usage monitoring, fallback paths, and incident-response procedures for production issues
- Versioning, change approvals, and rollback plans for prompts, models, and retrieval configurations
- Private or isolated deployment options where required
Governance should fit your industry, data, and legal requirements.
Solutions for Startup Industries
Generative AI patterns tailored to product teams, regulated workflows, and document-heavy operations.
SaaS and Technology
FinTech
Healthcare
E-commerce
Professional Services
Startup Operations
Why Startups Choose LuMay
A Delivery Model Built for Early-Stage Speed and Production Reality
Business-first planning starting from the user problem and measurable outcome
Rapid prototyping to test assumptions before full-scale development
Startup-focused delivery with manageable scope and fast feedback
Model-agnostic architecture that avoids unnecessary vendor lock-in
Cost-conscious implementation from day one, not just after launch
Agile collaboration with short cycles and working demonstrations
Scalable foundations built for future growth and integrations
Startup Industry Fit
SaaS & Technology
AI-native features, onboarding assistants, knowledge search, support automation, and development copilots.
FinTech
Document review, financial information extraction, and controlled risk-analysis assistance with human oversight retained for high-impact decisions.
Healthcare
Administrative assistants, document summarization, and patient-communication workflows with careful privacy and governance review.
E-commerce
Product discovery, conversational shopping, recommendations, and personalized merchandising.
Professional & Digital Services
Research preparation, client onboarding, document drafting, and reporting automation.
Engagement Models
Flexible Ways to Build with Our Team
Choose a starting shape that matches your startup stage, validation needs, and the level of delivery ownership you want from us.
AI Consulting
Roadmap, readiness, and use-case selection
Proof-of-Concept
Test the technical path with your data and systems
Proof-of-Value
Confirm the solution creates enough value to justify scale
Flexible
Engagement
Models
Engagement
Models
Dedicated AI Team
Specialists working alongside your startup team
End-to-End Development
Discovery, build, integration, testing, and launch
AI Integration
Add generative AI to an existing application or workflow
Engagement
Models
Models
AI Consulting
Roadmap, readiness, and use-case selection
Dedicated AI Team
Specialists working alongside your startup team
Proof-of-Concept
Test the technical path with your data and systems
End-to-End Development
Discovery, build, integration, testing, and launch
Proof-of-Value
Confirm the solution creates enough value to justify scale
AI Integration
Add generative AI to an existing application or workflow
Engagement
Models
Models
AI Consulting
Roadmap, readiness, and use-case selection
Dedicated AI Team
Specialists working alongside your startup team
Proof-of-Concept
Test the technical path with your data and systems
End-to-End Development
Discovery, build, integration, testing, and launch
Proof-of-Value
Confirm the solution creates enough value to justify scale
AI Integration
Add generative AI to an existing application or workflow
Cost and Timeline
Start Focused, Then Scale with More Confidence
Cost depends on your actual product requirements. A simple internal assistant and a multi-agent SaaS platform have very different needs.
A typical engagement moves through discovery, validation, prototyping, development, deployment, and optimization. Starting with a focused proof of value gives a clearer estimate while reducing uncertainty.
Major cost drivers
1
Use-case complexity and expected workflow depth
2
Data quality, retrieval requirements, and knowledge freshness
3
Integration scope across product, SaaS, CRM, or internal systems
4
Model usage, latency expectations, and infrastructure needs
5
Security, privacy, and governance requirements
6
Ongoing support, optimization, and operational ownership
Frequently Asked Questions
What is Generative AI development?
The process of creating applications that use AI models to generate, summarize, transform, retrieve, or analyze information, including application logic, data pipelines, integrations, security, and monitoring.
What does a Generative AI development company do?
It plans, builds, integrates, tests, and maintains AI-powered applications, from use-case discovery and model selection through deployment and optimization.
How can Generative AI benefit my startup?
It can improve product experiences, automate repetitive processes, simplify knowledge access, and enable new AI-native products when the problem and data are well chosen.
How is Generative AI different from traditional AI?
Traditional AI often predicts or classifies based on existing information. Generative AI creates new outputs like text, images, or code, and many applications combine both.
What is the difference between RAG and fine-tuning?
RAG supplies relevant external information at request time, while fine-tuning adjusts model behavior using a training dataset. RAG suits current or private knowledge; fine-tuning suits task-specific consistency.
Should we use a commercial or open-source model?
It depends on quality, cost, latency, privacy, and maintenance needs. Commercial APIs simplify implementation, while open-source models offer more control.
Can Generative AI integrate with our existing software?
Yes. It can connect to websites, SaaS platforms, mobile apps, CRMs, databases, and internal tools through secure APIs and integrations.
How do you protect confidential business data?
Through encryption, restricted access, permission-aware retrieval, data minimization, retention controls, and isolated environments as needed.
How do you reduce hallucinations?
Through retrieval, source attribution, structured prompts, validation rules, and human review. They can be reduced but not fully eliminated.
How long does a project take?
It depends on scope, data readiness, integrations, and complexity. A proof of concept moves faster than a full production application.
How much does it cost?
Cost varies by product scope, model usage, integrations, infrastructure, and support needs. A discovery or proof-of-value engagement gives the most reliable estimate.
Can you build a proof of concept first?
Yes, and for startups a proof of value can be even more useful because it also tests business impact.
Do you provide post-deployment support?
Yes. We support monitoring, maintenance, model updates, prompt optimization, and security patching after launch.
Can a solution be deployed on-premises?
Depending on the model and infrastructure, yes. Cloud, private, on-premises, or hybrid options can be selected based on privacy, cost, and performance needs.
How do you measure success?
Against the original use case: task completion, response quality, adoption, processing time, cost, and user satisfaction.
Build a Generative AI Product with a Clear Path to Production
A successful AI product needs more than a model and a prompt. It needs a valuable use case, reliable data, thoughtful design, secure integrations, and an architecture that supports real users.