99.98%Uptime
4.7xDeploys/wk
-38%Cost
Deploy pipelinegreen
Security scansgreen
SLO burn ratehealthy
Yansh Systems
From Gen-AI copilots and RAG platforms to computer vision and forecasting, we build AI systems that pass evaluation, survive real users and earn their operating cost.
The gap between a demo and a production AI system is measured in months of eval work, guardrails and retrieval tuning. We do that unglamorous middle , not just the pitch deck.
Whether you're exploring your first Gen-AI use case or scaling a fleet of models, we bring proven patterns from over sixty shipped applications across banking, health and industrial clients.
Use-case discovery, ROI modelling and platform choices scoped to your data and risk profile.
Retrieval, chunking, ranking and prompt design tuned against real user queries and eval sets.
In-workflow assistants and multi-step agents wired safely into your enterprise systems.
Defect detection, OCR, ANPR and video analytics deployed on edge or GPU clusters.
Contract parsing, claim triage and semantic search for the messy documents you actually have.
Demand, churn, price and next-best-action models with proper back-testing and ablation.
SageMaker, Vertex AI, Databricks and open-source MLflow stacks with CI/CD and drift alerts.
Bias, safety and hallucination testing plus governance patterns that satisfy risk teams.
LoRA, SFT and distillation pipelines to shrink cost and latency without losing quality.
Two-week feasibility with a golden dataset, baseline eval and honest go/no-go recommendation.
Six to eight week working prototype with real users, telemetry and cost per interaction.
Hardening, guardrails, MLOps integration and SRE-grade monitoring before general release.
Continuous eval, retraining, prompt versioning and quarterly cost-quality reviews.
A specialty insurer wanted to help adjusters triage claims faster without cutting review quality. We shipped a RAG copilot over eight years of policy documents in eleven weeks, with an evaluation harness that gates every prompt change before release.
Whatever fits — GPT, Claude, Gemini, Llama and Mistral all appear in our shipped work. Selection is a design decision, not a preference.
Private endpoints on Bedrock, Azure OpenAI or Vertex, on-prem inference where required, and PII scrubbing across every retrieval path.
No system is perfect, but we design golden-set evals, refusal patterns and citation-grounding that keep hallucinations measurable and rare.
Fixed-price for pilots up to a defined eval bar; time-and-materials or outcome-based for production — whichever aligns risk best.
Book a 30-minute call with an AI principal. We'll pressure-test your use case and share honest odds on feasibility and cost.