AI Agent Development, AI agents that know your business - and cite their sources.
AI agents grounded in your own documents and systems - with permission-aware retrieval, source citations on every answer, and a human in the loop wherever a decision matters. Delivered bilingually from Hanoi and Yokohama.
Grounded, not guessing
Answers come from your approved knowledge, with a link back to the source document.
Permission-aware
Access is filtered at the data layer - by role, level and project - not just hidden in the UI.
AI proposes, humans decide
Agents draft, summarize and flag. People approve anything that carries consequence.
Measured, not assumed
Evaluation sets, usefulness ratings and cost dashboards from the first pilot onward.
Four agent patterns
Most requests map to one of these. They share a single knowledge platform, so you can start with one and grow into the others.
Expert Q&A agent
Answers professional questions from your own standards, manuals and project history - always with a citation, and saying so when it is unsure.
Task assistant agent
Takes on the repetitive layer of a role: summarizing status, drafting minutes and action items, logging and categorizing issues, flagging what looks overdue.
Review & advisory agent
Reads a document or dataset and reviews it for completeness, consistency and risk - proposing concrete improvements for a human to accept or reject.
Agent team
Several role-specific agents sharing one knowledge platform, each with its own scope, tone and access rights, coordinated by a routing layer.
Retrieval-augmented, multi-agent
A layered design so the model tier can be swapped without rebuilding the system - and so knowledge, permissions and quality stay yours.
- LAYER 1
Channel
The agent appears where people already work - Teams, Slack, your web app or LINE - as one or several distinct identities.
- LAYER 2
Orchestrator
Authenticates the user, applies permissions, detects intent and difficulty, then routes the request to the right agent, knowledge scope and model tier.
- LAYER 3
Secure retrieval
Hybrid semantic and keyword search over the knowledge base, filtered by the user’s rights before anything enters the answer context. Isolation happens at the data layer.
- LAYER 4
Generation
The language model answers strictly from retrieved context and returns citations. Cheap models handle routine questions; harder ones escalate to a stronger tier.
- LAYER 5
Knowledge base
Vector store plus original documents and metadata: content type, applicable role, sensitivity, confidence and version - with an ingest-and-approve workflow.
- LAYER 6
Learning loop
Useful conversations are nominated, reviewed by your experts and promoted into the knowledge base, so coverage and accuracy compound over time.
- LAYER 7
Governance & observability
Role, level and project permissions; full query-retrieval-answer logging for audit; dashboards for quality, usage and cost.
Three routes. We recommend the middle one.
We assess every project on feasibility, effectiveness and cost - then tell you which route fits, including when the cheapest build isn't the right one.
Packaged SaaS platform
Build on a vendor’s low-code agent studio with native chat integration. Fastest to stand up, least engineering required.
- Feasibility
- Very high
- Quality ceiling
- Moderate
- Running cost
- High, per licence
- Knowledge control
- Limited
Best when: you need something live quickly and your knowledge needs are straightforward.
Self-built RAG on open source + LLM API
We build the orchestration and retrieval layers on mature open-source components and call models by API, with tiered routing.
- Feasibility
- High
- Quality ceiling
- Highest
- Running cost
- Low-moderate, per token
- Knowledge control
- Full
Best when: you want the best quality-to-cost balance and real control over knowledge and permissions.
Self-hosted open-weight models
Run open-weight models on your own GPU infrastructure. No token fees, but fixed infrastructure and MLOps cost.
- Feasibility
- Moderate
- Quality ceiling
- Moderate
- Running cost
- High, fixed
- Knowledge control
- Full, on-premise
Best when: volume is very large, or data residency rules require on-premise inference.
Quality where it counts, cheap everywhere else
Model pricing varies enormously by tier. Routing most traffic to a small model and escalating only hard questions is the single biggest lever on running cost - so we build it in from day one.
Tiered model routing
A small, inexpensive model answers most questions; only hard or high-stakes requests escalate to a stronger model.
Context caching
Repeated context and recurring questions are cached, which most providers discount heavily.
Batching background work
Long summarization and indexing jobs run as batch work at a lower rate, then notify the user.
Hybrid retrieval
Better retrieval means smaller prompts - accuracy and cost improve together rather than trading off.
Cost is reported per agent and per project on an operations dashboard, so spend stays visible rather than surprising you at renewal.
Pilot first, then scale
Value early, risk low. Each phase ends with something in real use and a measurement you can act on.
- PHASE 0
Foundation & pilot
Stand up the retrieval framework, permission model and one agent on a narrow, well-chosen knowledge set. Establish the evaluation set and cost dashboard.
OUTCOMEOne pilot agent answering with citations for a small user group - with numbers. - PHASE 1
Live agents
Roll out the agents your roles need, full permission model, knowledge admin console and in-channel feedback. Start the knowledge accumulation loop.
OUTCOMEAgents in daily use; the knowledge base begins to compound. - PHASE 1.5
Stabilize & optimize
Broaden knowledge coverage, standardize the ingest-and-approve process, tune routing, caching and batching, run evaluations against regression.
OUTCOMEStable quality, optimized spend, both measurable per agent and project. - PHASE 2
Review & advisory
Connect project and document systems so agents can review real work - assessing completeness, risk and health, and proposing prioritized actions.
OUTCOMEAgents contribute to output quality, not just answer questions.
The questions every buyer asks
Will it leak knowledge between projects or clients?+
What stops it from making things up?+
Will our data be used to train someone’s model?+
What if the knowledge is wrong or out of date?+
How do we keep token cost predictable?+
What if the team doesn’t adopt it?+
Microsoft Teams
Chat 1-1 or inside a project channel.
Slack
Same agent, same knowledge, different workspace.
Your web app
Embedded panel or API for your own product.
LINE
For customer-facing agents in the Japanese market.
Have a use case in mind?
Tell us the workflow you'd like an agent to take on. We'll tell you honestly whether an agent is the right answer - and scope a pilot if it is.