Move Beyond AI Experiments to Real Product Capabilities
Many teams are excited about Generative AI and large language models, but struggle to move past demos and proofs of concept. Chatbots work in isolation, prompts live in notebooks, and AI features fail under real user load or data complexity.
Our Dedicated GenAI and LLM Engineers model is designed for companies that want AI to become a reliable part of their product, not a side experiment.
You bring the problem. We build AI systems that work in production.
Who This Is For
This solution is ideal for:
-
Startups building AI-first or AI-assisted products
-
SaaS platforms adding LLM-powered features
-
Enterprises experimenting with internal AI tools
-
Teams moving from GenAI PoC to production
-
CTOs needing long-term AI ownership
If AI must deliver measurable value, this model fits naturally.
Common Challenges With GenAI and LLM Projects
Many organizations face:
-
Prompt-based demos that break in real usage
-
Poor handling of private or structured data
-
Lack of evaluation and monitoring for AI outputs
-
High latency and unpredictable costs
-
No clear ownership of AI behavior in production
LLM systems require engineering discipline, not just prompts.
Our GenAI and LLM Engineering Model
We provide dedicated remote GenAI and LLM engineers who combine AI understanding with strong backend and system engineering skills.
The model is designed to:
-
Design AI workflows aligned with business logic
-
Build secure data pipelines for LLM usage
-
Integrate AI outputs cleanly into products and APIs
-
Maintain reliability, cost control, and observability
AI becomes part of your system architecture, not a bolt-on feature.
What Our GenAI and LLM Engineers Deliver
LLM Application Design
-
Use case driven LLM workflows
-
Prompt design with versioning and testing
-
Guardrails and output validation
RAG and Internal Data Integration
-
Retrieval-Augmented Generation pipelines
-
Vector databases and embeddings
-
Secure access to internal and customer data
Backend and API Integration
-
Django or FastAPI based AI services
-
Scalable inference endpoints
-
Async processing and background jobs
Reliability, Cost, and Monitoring
-
Latency and cost optimization
-
Logging and output tracking
-
Evaluation metrics and feedback loops
How We Work With Your Team
-
Understand the business problem and success criteria
-
Assess data sources, security, and constraints
-
Design GenAI workflows suitable for production
-
Build, integrate, and deploy AI services
-
Monitor, refine, and scale with real usage
Delivery stays practical, iterative, and measurable.
Technology Expertise
-
GenAI and LLMs: Applied LLM systems and orchestration
-
RAG: Vector databases and retrieval pipelines
-
Backend: Django, Django REST Framework, FastAPI
-
Data: Structured and unstructured data handling
-
APIs: REST and async APIs
-
Cloud: AWS, GCP, Azure
-
DevOps: CI/CD, monitoring, deployment automation
Technology choices prioritize reliability, security, and cost control.
Business Benefits
-
Faster transition from AI idea to production
-
Reduced risk of unreliable AI behavior
-
Better control over AI cost and performance
-
Secure use of internal and customer data
-
Long-term ownership of AI systems
This turns GenAI into a dependable product capability.
Why Companies Choose This Model
-
Engineers who understand LLMs and systems
-
Focus on production, not hype
-
Clear ownership of AI behavior
-
Remote collaboration with strong overlap
-
Transparent and long-term engagement
We help teams ship AI they can trust.
Engagement Models
-
Dedicated GenAI or LLM engineer
-
Small GenAI product pod
-
GenAI production readiness assessment
-
Long-term AI engineering partnership
Engagements align with product maturity and data readiness.
Build GenAI Features With Confidence
If you want dedicated GenAI and LLM engineers who can take AI from idea to production safely, let’s talk.
Schedule a discovery call and we will help you design and build production-ready GenAI systems.