01 / Agents
Agentic solution development
Production workflows with tool permissions, orchestration, and review paths.
Tokyo AI Research and Engineering Lab
Agentic systems, model programs, and inference stacks for real deployment.
BitLabs designs and builds enterprise AI systems with a lighter path from concept to production.
Research to productionWe keep model, product, and deployment choices tied to business goals and control boundaries.
Expert Lab
Focused capability across agent systems, model work, and inference.
BitLabs connects model decisions, application design, and deployment discipline in one delivery path.
02 / Pre-training
LLM and SLM training programs
Training, fine-tuning, and evaluation handled as one system.
03 / Inference
Inference stack architecture
Serving paths shaped for latency, GPU efficiency, and deployment control.
04 / Reliability
Enterprise delivery discipline
Quality, latency, safety, and failure modes measured before launch.
System Map
One path from business problem to secure AI deployment.
We map the operating path before implementation so the core decisions stay aligned.
01
Business problem
02
Data boundary
03
Model strategy
04
Inference stack
05
Agent workflow
06
Evaluation
07
Secure deployment
Approach
We start with the problem, then choose the right model and serving path.
We begin with the operating problem, then shape the model, inference, and application architecture around it.
01. Workshop
Pain pointsClarify the operating problem.
Map workflow pain points, systems, and hard constraints.
02. Solution Design
Best-fit proposalChoose the right stack.
Set the architecture, model approach, inference path, and control boundary.
03. MVP Development
Focused MVPBuild the focused MVP.
Ship the smallest system that can validate fit and value.
Research to Production
From research direction to working system.
We use focused MVPs to validate value, then harden what works.
Research
Model programs
Pre-training, fine-tuning, and controllability work.
Architecture
Inference boundaries
Model, data, serving, and deployment choices tied to real constraints.
Implementation
Working systems
Focused MVPs, agentic systems, and custom AI software.
Operation
Reliable rollout
Release criteria, observability, and steady improvement.
Capabilities
What BitLabs builds.
Agentic solutions
Many teams have demos, but not a reliable agent workflow connected to real tools and data.
Enterprise AI architecture
Generic adoption plans rarely answer data boundaries, ownership, or long-term operating control.
LLM and SLM training
Training plans become expensive quickly when data, evaluation, and release goals are not aligned.
Custom AI applications
AI value drops fast when software does not fit existing workflows.
Inference stacks and secure deployment
Latency, GPU cost, and deployment control decide whether a system can hold up in production.
Security & Deployment
Production AI starts with control boundaries, evaluation, and deployment discipline.
Security, data handling, and release checks are part of the first architecture pass.
- Private or regional deployment options with explicit trust boundaries
- Integration patterns for model access, tool use, and sensitive data handling
- Release criteria for regulated or high-accountability environments