DMS AI Lab 路 Technical Brief No. 001
Research that ships.
Not research that sits on arXiv.
01 Problem
Most AI research never leaves the lab
Papers publish. Code rots. The gap between a NeurIPS spotlight and a production system is measured in years, not weeks.
02 Approach
Applied research with production intent
We train LLMs, build agents, and deploy RAG pipelines. Every research question comes from a real project with paying customers.
03 Output
Code, not citations
Research prototypes, open-source tools, and deep-dive write-ups: all Apache 2.0, all reproducible, all shipping to production.
04 Collaboration
Partner with us
We work with startups, enterprises, and researchers. Weekly demos. Clean code. Full IP ownership.
DMS AI Lab : under DMS Lab 路 Established 2020 路 6 active research areas
Research Areas
Six Questions We're Working On
No theoretical fluff. No sandbox experiments. Every question below comes from real projects where we've built AI for paying customers. And sometimes, the best answers start as reckless Friday afternoon prototypes.
LLM Training
How do you train and fine-tune large language models for real-world use cases?
Calling an API is fast, but you don't control the quality. We train and fine-tune open models like Llama, Mistral, and Qwen using LoRA and QLoRA on local GPU infrastructure. From dataset preparation and fine-tuning strategy to quantization and inference optimization: every step is measured and documented. The result is faster models, lower costs, and full control over output behavior.
LLM Agents
Can AI agents reason, plan, and act reliably in production?
Multi-step agent architectures fail silently more often than they succeed. We build agents with tools, memory, and decision-making capabilities, then stress-test them against real enterprise workflows where failure costs money, not benchmark points.
RAG Systems
How do you build retrieval-augmented generation that actually works at scale?
The gap between a RAG demo and a production RAG system is about six months of engineering. We're closing that gap: vector search optimization, hybrid retrieval strategies, chunking at scale, and embedding pipelines that don't break at 10M+ documents.
Workflow Automation
What does it take to orchestrate multi-model, multi-step AI pipelines?
Calling three APIs is easy. Building pipelines with conditional branching, human-in-the-loop fallbacks, and graceful degradation across five models : that's engineering. We design workflows that handle edge cases, not just the happy path.
Model Evaluation
How do you honestly benchmark and stress-test AI systems?
Leaderboard scores measure nothing that matters in production. We build custom evaluation frameworks that test for regression, adversarial robustness, and real-task performance. If you can't measure it, you can't ship it, and we take measurement seriously.
AI Security & Defense
How do you secure AI systems and use AI for system defense?
Two sides of the same problem. Side one: protecting AI models from prompt injection, adversarial attacks, and data leakage through input filtering, output validation, and privilege isolation. Side two: using AI itself to detect anomalies, automate threat response, and build defensive shields for your infrastructure. We research both directions, from architecture through deployment.
What We Build
Research That Ships
Research credibility doesn't come from citation counts. It comes from working code that other engineers can download, test, and build on. Here's what we produce.
Research Prototypes
Complete implementations of novel AI architectures : agent systems, RAG pipelines, evaluation harnesses. Not slideware. Runnable code you can clone, test against your own data, and integrate into your stack.
Open-Source Tools
Benchmarks, evaluation frameworks, and utility libraries: all Apache 2.0 licensed, fully documented, and actively maintained. We open-source what we learn because shared tooling compounds in value. Better tools raise the entire ecosystem.
DMSLab.ai Integration
Research validated on Monday flows directly into DMSLab.ai's production API, agent platform, and automation engine. Discovery to customer delivery in the same week. That feedback loop is our competitive advantage.
Technical Deep-Dives
Experiment write-ups, architecture decisions, and methodology notes, with reproducible code and raw data. We document what worked, what failed, and what surprised us. No cherry-picked results. No survivorship bias.
Work With Us
Hard AI Problems Need Engineering Partners
We work with startups, enterprises, and independent researchers. Building agents? Evaluating models? Deploying RAG at scale? Securing AI systems? You need a research partner who ships code, not slide decks. Let's talk.
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