Automate complex workflows
Connect knowledge, tools, and human decisions so your team can move beyond repetitive handoffs.
AI research & engineering · Tokyo
BitLabs builds AI agents, adapts language models, and delivers secure applications around your workflows, data, and operating requirements. We connect research with the engineering needed to put AI into everyday use.
Showreel · 0:21
Connect knowledge, tools, and human decisions so your team can move beyond repetitive handoffs.
Identify whether better context, fine-tuning, a smaller model, or inference optimization fits your constraints.
Turn a promising experiment into an application with clear evaluation criteria and an operating plan.
An illustrative workflow shows how we think about context, permissions, and accountability. It uses synthetic data and is not a client case study or a performance benchmark.
A synthetic purchase request illustrates retrieval, limited tool access, approval, and evaluation.
Read the annotated workflowFour monitors, ¥30,000 each. Total: ¥120,000.
Synthetic policy: requests up to ¥150,000. Human approval required before ordering.
SYNTHETIC-POLICY-01Search and draft tools allowed. Ordering and payments denied.
draft_request → pending_human_reviewBeing within budget does not authorize execution. Try either decision.
Waiting for human review.
Five kinds of AI systems we design and deliver, each delivered under one of our services.
Agents that plan multi-step work, call permitted tools, and check their own progress within defined limits.
Agents embedded in recurring processes such as intake, triage, and document handling, with people approving consequential steps.
Spoken interfaces for support, intake, or internal tools, with a clear handoff to a person.
Adapting a model to a domain when better context or prompting does not meet quality, cost, or latency needs.
Serving infrastructure for open-weight models when latency, throughput, or data-residency needs rule out a hosted API.
Map users, data, constraints, and the decisions that need human judgment.
Set a baseline and agree on quality, latency, cost, and failure cases.
Iterate on the model, context, tools, and interface against those criteria.
Document operations, test recovery, and prepare the people who will run the system.
We scope controls around your data, risk, and operating environment. Deployment options can include AWS, Azure, and private infrastructure, subject to project requirements.
Define what a model can see, where data can travel, and what is retained.
Use scoped credentials and allowlisted actions, with approval for consequential changes.
Plan trace visibility, incident handling, rollback, and safe failure paths.
Our research directions focus on smaller models, adaptation, agent design, and reliability. These are areas of investigation, not claims of published results.
ResearchBitLabs is an AI R&D and consulting company based in Tokyo. Our work brings together language model development, agent systems, application engineering, and secure cloud deployment.
Meet BitLabsA workflow to improve, a model to adapt, or a prototype ready for its next step.
Discuss your project