From Chatbots to Coordinated Action
What agentic orchestration really means: an LLM that plans, calls tools, and coordinates specialized agents to finish real, multi-step enterprise work — not just answer questions.
RADIT Labs is our dedicated AI research division, where a team of scientists, engineers, and researchers work on advancing the state of artificial intelligence. We don't just follow trends — we create them.
Our research is deeply integrated with our product development. Every breakthrough in our labs finds its way into real-world applications, ensuring our clients always have access to the latest AI capabilities.
We actively publish our findings, contribute to open-source projects, and collaborate with academic institutions worldwide.
Research Papers
Research Areas
Researchers
Open Source Projects
We conduct deep research across five core domains of artificial intelligence.
Advancing LLM capabilities in reasoning, factual accuracy, and domain-specific knowledge. Our research focuses on efficient fine-tuning, retrieval-augmented generation (RAG), and reducing hallucinations in production systems.
Pioneering research in autonomous AI agents that can plan, reason, and act in complex environments. We're developing novel architectures for multi-agent collaboration, tool-use reasoning, and long-horizon task planning.
Pushing the boundaries of visual understanding with research in real-time object detection, scene understanding, 3D reconstruction, and visual-language models that bridge the gap between seeing and reasoning.
Developing RL algorithms for real-world decision-making, including multi-objective optimization, safe exploration, and reward modeling for RLHF in language models.
Ensuring AI systems are fair, transparent, and aligned with human values. Our research covers bias detection, model interpretability, adversarial robustness, and AI alignment techniques.
Making AI faster, cheaper, and more accessible. Research in model compression, quantization, knowledge distillation, and edge-optimized inference for deploying AI anywhere.
One unified interface to ask, search, and act — with an orchestration layer that plans the work, retrieves grounded knowledge, coordinates agents across your systems, and tracks every workflow end to end.
The real power of AI in the enterprise isn't a smarter chatbot — it's an orchestration layer that turns intent into coordinated, accountable action across every system.
RADIT Labs
From a single natural-language request to a tracked, audited outcome.
A user asks in plain language from one unified interface.
The orchestrator decomposes intent into ordered tasks.
Unified search grounds answers in your knowledge base.
Agents call tools and systems to execute the work.
A critic and guardrails check before anything ships.
Every step is logged, monitored, and auditable.
Each layer has one job and a clean contract with the next — so enterprises can adopt the unified interface first, then extend reach into systems of record without re-platforming.
A single pane to ask, search, and act across every application.
Plans tasks, routes to the right agent, and coordinates multi-step work.
Unified, grounded search across your enterprise knowledge base.
Typed, permissioned connectors into ERP, MES, ticketing, and databases.
State, approvals, and an append-only audit trail for every action.
Search the knowledge base and trigger work from a single screen — no app hopping.
Ask once and retrieve grounded answers with citations across every source.
See every workflow's state, owner, and history in real time.
Human-in-the-loop approvals and full auditability keep workflows safe.
Replace dozens of screens with a single place to get work done.
Find answers and complete multi-step tasks in seconds, not hours.
Evidence-first retrieval keeps answers accurate and citable.
Every workflow is tracked, approved, and fully traceable.
Short reads and solution designs from RADIT Labs on building agentic systems that enterprises can trust.
What agentic orchestration really means: an LLM that plans, calls tools, and coordinates specialized agents to finish real, multi-step enterprise work — not just answer questions.
A single unified interface that searches across your entire knowledge base — wikis, documents, tickets, and source websites — and returns grounded answers with citations.
How agentic systems capture every step as state — owner, status, evidence, and approvals — so workflows are tracked, maintained, and resumable instead of lost in inboxes.
Why scattered point-solution bots create silos — and how a single orchestration layer that helps enterprises coordinate agents, data, and tools delivers compounding value.
A solution design for maker-checker approvals, role-based access, and append-only audit trails that let autonomous workflows act safely inside regulated enterprises.
Evidence-first retrieval and a critic loop that keep agent answers accurate, cited, and trustworthy — the difference between a demo and a production system.
Selected publications from our research team.
A novel RAG architecture that reduces hallucination rates by 73% while maintaining response latency under 200ms for enterprise knowledge bases.
Introducing a hierarchical agent architecture where specialized agents collaborate through structured communication protocols to solve multi-step business processes.
A compact vision transformer architecture achieving 98.7% accuracy on manufacturing defect detection while running at 120 FPS on edge devices.
A dynamic quantization framework that reduces LLM inference costs by 60% with minimal quality degradation, enabling enterprise deployment on standard GPU hardware.
A comprehensive framework for identifying and reducing bias in domain-specific LLMs, with automated testing pipelines for continuous fairness monitoring.
Novel reward shaping techniques that constrain autonomous agents to safe operational boundaries while maximizing task completion rates in production systems.
We believe in advancing AI through open collaboration. Here are some of our contributions.
Open-source framework for building production-ready AI agents with built-in tool-use, memory management, and multi-agent orchestration.
Model optimization toolkit for compressing and quantizing large language models for efficient inference on consumer hardware.
Bias detection and fairness evaluation library for ML models. Automated testing for demographic parity, equal opportunity, and calibration.
We actively collaborate with universities, research institutions, and industry partners. Whether you're a PhD researcher, an academic institution, or a company looking to invest in AI research — we'd love to explore how we can push AI forward together.
Joint research projects, co-authored papers, and shared datasets with leading universities.
Collaborative R&D programs with enterprise partners to solve domain-specific AI challenges.
Summer and full-year research internships for MS and PhD students working on cutting-edge AI.
Contributing to and maintaining open-source tools that advance the AI ecosystem.
Let's discuss collaboration opportunities and how our research can benefit your organization.