AI & GenAI
Practical GenAI products with measurable business outcomes.
CodingYan Technologies
Overview
We integrate AI where it creates clear value — copilots, assistants, document intelligence, and agent workflows — not as a novelty. Every build balances model quality with latency, cost, privacy, and evaluation so your AI features stay useful and under control in production.
What We Offer
- LLM copilots and customer-facing chat assistants
- RAG systems over your documents and knowledge bases
- Agent workflows for internal automation
- Custom ML models for prediction and classification
- Prompt evaluation, guardrails, and cost controls
- Secure data pipelines for training and inference
How We Deliver
Step 01
Use-Case Selection
We pick high-ROI problems where AI beats rules-based approaches, with clear success metrics.
Step 02
Data & Guardrails
Access controls, retrieval quality, and safety policies are designed before prompts go live.
Step 03
Build & Evaluate
We iterate with offline evals and human review so quality is measurable, not anecdotal.
Step 04
Productionize
Caching, rate limits, observability, and fallbacks keep costs predictable and uptime high.
Common Questions
Do we need our own models?
Usually not. Most products start with frontier APIs plus RAG. We recommend fine-tuning or custom models only when data and volume justify it.
How do you control AI costs?
Through caching, routing, token budgets, evaluation gates, and choosing the smallest model that meets quality targets.
