Generative AI Development
Generative AI is everywhere — but most implementations stall at demo stage. We build production-grade GenAI systems: custom fine-tuned models, RAG pipelines, content engines, and AI-powered applications that run at scale.
What We Build
Custom LLM fine-tuning for your domain
Train models on your data so they understand your terminology, style, and domain nuances — not just generic language patterns.
RAG implementation over your knowledge base
Ground AI responses in your actual data. Accurate, cited, and up-to-date answers from your docs and databases.
AI-powered content generation at scale
Product descriptions, reports, summaries, marketing copy — generated consistently and at volume with your brand voice.
Code generation and developer tools
Build internal developer tools powered by AI — code review assistants, documentation generators, and testing helpers.
Prompt engineering and optimization
Get better results from any model with optimized prompts. Reduce token usage, improve accuracy, and lower costs.
Multi-model architectures for cost and quality
Use different models for different tasks — fast and cheap for simple work, powerful for complex reasoning. Optimize cost without sacrificing quality.
How It Works
Define the use case
What should the AI generate, and for whom? We scope the problem, identify data sources, and define success metrics.
Build the system
Model selection, fine-tuning, RAG pipeline, API layer. We build and test until output quality meets your bar.
Deploy and optimize
Production deployment with monitoring, cost tracking, and quality gates. Continuous improvement as your needs grow.
Tech We Use
Industries We Work With
E-Commerce & Retail
Product descriptions, personalized recommendations, review summaries, SEO content
Marketing & Advertising
Ad copy generation, campaign content, social media posts, A/B test variants
Medical Industries
Clinical note generation, patient summaries, medical documentation, diagnostic support
Legal Services
Contract drafting, legal memo generation, case summaries, document templates
Banking & Finance
Report generation, financial summaries, investment insights, compliance documentation
SaaS & Technology
AI-powered features, code generation, documentation, user content creation
Media & Publishing
Content generation, article drafting, video scripts, transcription summaries
Education
Course content creation, quiz generation, personalized learning materials, feedback
Common Questions
Should we fine-tune a model or use RAG?
Depends on your use case. RAG is better when you need accurate answers grounded in your data. Fine-tuning is better when you need the model to adopt a specific style or handle domain-specific tasks. We often use both together.
Which LLM should we use?
We're model-agnostic. GPT-4, Claude, Llama, Mistral — we pick based on your requirements: accuracy, cost, latency, and data privacy. You can also bring your own keys.
How do you handle data privacy?
Your data can stay on your infrastructure. We support on-prem deployment, BYOK, and flexible hosting options for sensitive use cases.
What does 'production-grade' mean?
It means the system handles real traffic with monitoring, error handling, cost controls, rate limiting, and automated quality checks. Not a Jupyter notebook — a working system.
Can you build AI features into our existing product?
Yes. We build GenAI features as APIs or microservices that integrate with your existing application — content generation, search, summarization, chat, or any custom capability.
Ready to Build AI That Actually Works?
Tell us what you need. We'll scope it, show you the ROI, and give you a realistic timeline.
Book a Demo