AI Development Services
Built to Survive Contact
With Production
Most AI projects work in a demo. Few survive real users, real data, and real edge cases. LuMay AI designs and builds AI systems — generative AI, machine learning, computer vision, and intelligent automation — engineered from day one to run in production, not just in a pitch meeting.
Custom AI Development
Generative AI Solutions
AI Chatbots and Copilots
Machine Learning
NLP and Computer Vision
AI Integration
Post-Launch Monitoring
Custom AI Development
Generative AI Solutions
AI Chatbots and Copilots
Machine Learning
NLP and Computer Vision
AI Integration
Post-Launch Monitoring
2. AI PROJECT FAILURE SECTION
Why AI Projects Fail (And How We Prevent It)
The LuMay AI Approach
Why Projects Usually Stall
- No measurable definition success
- Fragmented unmanaged data
- Poor workflow fit generic tools
- No evaluation quality checks
- Security compliance considered late
- Prototype never designed production
- No ownership monitoring launch
AI DEVELOPMENT SERVICES
AI services designed around real-world delivery.
From strategy and development to automation and integration, we build practical AI solutions that work in production.
StrategyGenerative AIMachine LearningAutomationVision and NLPIntegration
AI Consulting and Strategy
Define the business case, success criteria, and roadmap for practical AI adoption.
Use-case framingROI planningRisk mapping
Custom AI Software Development
Build production-ready AI software around the workflows, systems, and controls your team actually needs.
System designProduct deliveryProduction hardening
Generative AI Development
Develop assistants, copilots, and search experiences with grounded prompts and guardrails.
LLM appsRAG flowsPrompt systems
Chatbots and Copilots
Design user-facing AI experiences that assist teams, answer questions, and drive repeatable actions.
Support botsInternal copilotsTask orchestration
Machine Learning
Deploy predictive models for classification, scoring, anomaly detection, and forecasting.
ForecastingScoringDetection
NLP
Extract, classify, route, and summarize text for business systems.
ExtractionSummarizationRouting
Computer Vision
Apply image and video understanding to operational workflows and inspection tasks.
InspectionDetectionAnalysis
AI Integration and Automation
Connect AI systems to products, knowledge, APIs, and workflows so the output becomes operational, not isolated.
API integrationWorkflow automationSystem handoffs
4. SOLUTIONS WE BUILD SECTION
AI Solutions Solve Real Business Problems
Company Knowledge
What return policy?
Our return policy allows within 30 days purchase.
SourcesReturn_Policy.pdf
AI Knowledge Assistant
Instant, accurate answers internal documents data.
Billing issue
Shipping update
Account access
Customer Support
Resolved
Customer asked about order status and refund timing.
Pulled order details from CRM
Drafted compliant reply
Ready to send to customer
AI Customer Support Agent
Resolves customer requests quickly while keeping support workflows organized.
Invoice_0425.pdf
VendorAcme Corp
DateApr 15, 2024
Total$12,560.00
Confidence98%
AI Document Intelligence
Extract, validate, process documents high accuracy speed.
Forecasting Dashboard
30d
Demand Forecast
+24.6%
Next 30 days
AI Forecasting System
Predict demand, trends, outcomes make smarter business decisions.
5. AI Development Process
A Proven Process from Strategy to Scale
01
Discovery
We define goals, use cases, and success metrics.
02
Data Readiness
We assess and prepare your data for AI.
03
Architecture
We design the optimal solution architecture.
04
Prototype
We build and validate a focused prototype.
05
Development
We build a secure, scalable, and robust solution.
06
Evaluation
We test accuracy, performance, and reliability.
07
Deployment
We deploy and integrate into your environment.
08
Monitoring
We monitor, learn, and continuously improve.
Industries We Serve
AI Solutions for Every Industry
Healthcare
Finance & FinTech
Retail & Ecommerce
Manufacturing
Logistics & Supply Chain
Real Estate
Education & EdTech
SaaS & Technology
Technology Stack
Modern AI. Proven Technologies.
LLMs & Generative AI
OpenAIAnthropic
Llama
Gemini
Machine Learning
scikit-learn
TensorFlow
PyTorch
- XXGBoost
Data & Vector DB
- PPinecone
- WWeaviate
- QQdrant
- MMilvus
Cloud & Infra
AWS
Google Cloud
Azure
MLOps & Monitoring
- mmlflow
- LLangfuse
Prefect
Grafana
Proof and Case Studies
A bold editorial proof section built around outcomes and delivery context.
Real production outcomes from AI implementations—with the delivery context behind every result.
Reduced document-review time for a regional provider
We designed and deployed a production AI workflow that accelerated review time while maintaining human oversight, security, and compliance.
View case studyRepresentative production outcomes
SPEED42%
Faster response time
Service and workflow acceleration measured after production rollout.
EFFICIENCY65%
Less manual review
Reduction in manual document handling across operational workflows.
IMPACT3×
Faster decision-making
Improved speed in knowledge, forecasting, and triage systems.
Engagement Models
Four delivery models designed around how teams usually buy and validate AI work.
AI Discovery Sprint
Best for teams validating an AI opportunity before build work.
- Duration: Short (1–2 weeks)
- Includes: Problem framing, feasibility review, architecture direction
- Output: Delivery roadmap and decision-ready recommendation
AI Proof of Concept
Best for teams needing evidence that the approach works on a real slice of the problem.
- Duration: Focused (2–6 weeks)
- Includes: Scoped build, evaluation setup, early workflow validation
- Output: Working proof and production-readiness decision point
End-to-End AI Development
Best for teams ready to move from concept to production system.
- Duration: Full project (8–16+ weeks)
- Includes: Architecture, development, testing, deployment, monitoring
- Output: Launched production system
Dedicated AI Development Team
Best for companies with a continuous AI roadmap and multiple delivery tracks.
- Duration: Ongoing engagement
- Includes: Cross-functional delivery capacity and long-term engineering support
- Output: Sustained AI product and platform execution
FAQ Section
Answers to the questions teams usually ask before they commit.
We keep these answers grounded in delivery reality so buyers can evaluate fit without sitting through a sales deck.
How much does AI development cost?
Cost depends on scope, integrations, data readiness, and the level of production engineering required. We usually start by defining the business case, technical shape, and delivery path before estimating build cost.
How long does an AI project take?
Timelines vary by complexity, but the right first step is usually a scoped discovery and feasibility phase. That helps separate quick wins from projects that need deeper data, architecture, and integration work.
Can you integrate AI into an existing product?
Yes. We design AI systems to work with existing applications, internal tools, APIs, data stores, and operational workflows rather than forcing a clean-sheet rebuild.
Do we need our own data?
Not always, but data quality and access strongly affect what is practical. In many cases, existing documents, events, workflows, or application data are enough to begin with a useful scoped solution.
Which AI model should we use?
That depends on accuracy needs, latency, privacy, cost, and the task itself. We choose models based on the production constraints of the system, not on popularity alone.
How do you keep AI systems secure?
We design for access control, data handling boundaries, logging, monitoring, review workflows, and model behavior guardrails. Security is part of system design, not a patch added at the end.
What happens after deployment?
Production is where the real work starts. After deployment, we focus on monitoring, evaluation, optimization, incident visibility, and iterative improvement based on live usage and business outcomes.
Ready to Build AI That Works Outside the Demo?
No sales theater. Just a practical discussion about the workflow, the constraints, and whether this should become a real project.