AI work that holds up in production.
LLM apps, RAG, vision, speech. We build the part that comes after the demo.
There is a big gap between an AI demo and an AI system that works at scale. Most teams underestimate how big. We treat AI like engineering: real evaluations, monitored failure modes, fallbacks for when the model gets it wrong. The papers are interesting, but your users just want it to work.
LLM applications
Chat, agents, copilots. With guardrails, evaluations, and the monitoring you need to actually ship.
RAG and search
Document retrieval that finds the right answer. Embeddings, reranking, and proper citation handling.
Computer vision
Object detection, OCR, document parsing. Production pipelines, not notebook experiments.
Speech and language
ASR, TTS, and translation, with a focus on Nepali and other underserved languages.
Fine-tuning and evals
For when the off-the-shelf model is not good enough. We measure before and after so there is no guessing.
MLOps
Model versioning, deployment, monitoring. The infrastructure nobody talks about until it breaks.
- 01
Frame the problem
Is this actually an AI problem? Sometimes a SQL query is the right answer. We will tell you when it is.
- 02
Prototype on real data
We need your data, not synthetic samples. A week of prototyping teaches us more than a month of planning.
- 03
Evaluate honestly
We build the evaluation set first. That way we know whether changes are actually helping.
- 04
Ship behind a flag
Roll it out to a small group. Watch the numbers. Expand or roll back based on what we see.
Will my data train someone else's model?
No. We use providers and configurations that keep your data yours. We can do self-hosted setups when you need that.
Can you build something on-prem?
Yes. We have shipped fully local LLM and vision systems for clients in regulated industries.
How do you measure if AI is working?
Task-specific evaluation sets. Sometimes human review. Always something more concrete than gut feeling.