BAP builds Generative AI, RAG and AI agent systems for enterprises — from fast-moving startups to large corporate groups. We work end to end: clarifying requirements, selecting the use case, delivering a proof of concept in roughly two to four weeks, then building, integrating and running the production system.
Every project is led by project managers and bridge engineers based in Japan and Vietnam, supported by engineers who have spent years delivering for demanding enterprise clients. Much of BAP’s management came up through engineering, so the people you talk to understand the technical trade-offs and can resolve key decisions without a translation layer in between.
Our delivery model balances quality, speed and cost — and stays with you from the first experiment through to the point where the system is producing measurable value in daily operations.
Book a free 30-minute AI Discovery Session, or email sales@bap.jp to talk through your specific problem.
Why most Generative AI projects never reach production
Plenty of companies have trialled ChatGPT or built an AI proof of concept. Far fewer have AI running inside a real business process. The gap is rarely the model — it is everything around it.
No use case with a defensible ROI
Teams know AI has potential but cannot decide which process to start with. When a use case is chosen because the technology is new or the demo is easy to build, the PoC works and then stalls, because it never produced enough value to justify the next investment.
We assess candidate use cases against:
- Current workload volume
- Time and headcount consumed today
- Data quality
- Feasibility of system integration
- The cost of a wrong answer
- Expected financial or operational gain
- Room to scale after the PoC
The PoC succeeds but cannot go live
A demo that performs on a small dataset says little about security, latency, accuracy, permissions, monitoring or error handling under real load.
Moving from PoC to production means solving:
- Data architecture and integration
- Per-user access rights
- Accuracy testing
- Model usage cost tracking
- Behaviour when the model is not confident
- Human approval for consequential decisions
- Logging, monitoring and incident response
- A process for updating data and prompts
Internal data is scattered
Company knowledge typically lives across file servers, SharePoint, CRM, ERP, email, PDFs, spreadsheets and bespoke line-of-business applications.
Without a data strategy, an AI system struggles to surface information that is correct, current, and appropriate to the permissions of the person asking.
Concern about information leakage
Security teams want a clear answer on whether data entered into an AI system is retained, and whether it is used to train a model.
That question is answered at the level of architecture, contract and service configuration — not by a general assurance that “AI is safe”.
Hallucination and unsupported answers
An LLM can produce an answer that reads well and is wrong. In legal, financial, manufacturing or customer-facing work, a single wrong answer carries real cost.
We reduce that risk with RAG, source citation, rule-based checks, confidence thresholds and human review.
Shortage of AI talent
The scarce profile is not an AI engineer — it is someone who understands the business process, the data and the technology at the same time. Hiring that person, or that team, takes longer than most AI roadmaps allow.
The cost of building the team in-house
A complete in-house AI team means AI engineers, data engineers, a solution architect, backend developers, DevOps, QA and security specialists — assembled and retained before the first line of value is delivered.
A managed offshore team gives you access to those skills on a flexible basis, with dedicated project management as the single point of contact.
Integration with existing systems is hard
AI only creates durable value once it is wired into the processes and systems the business already runs on — CRM, ERP, DMS, accounting, contact centre, manufacturing execution or legacy applications.
We design AI as a component of your overall architecture, not as a chatbot sitting to one side of it.
How BAP works with you
We split delivery into four packages, so you can start wherever you are.
| Package | Right for you if | What you get |
|---|---|---|
| AI Discovery | No confirmed use case or roadmap yet | Prioritised use cases, feasibility assessment, roadmap |
| AI PoC | You need to validate the technology and the value quickly | A working prototype in about 2–4 weeks, plus an evaluation report |
| Production Development | The PoC passed, or requirements are already clear | An integrated, tested system ready for live operation |
| AI Managed Service | You already run an AI system | Monitoring, maintenance, evaluation and continuous improvement |
AI Discovery
We analyse your current processes, data, systems and the problem you actually need solved, then score candidate use cases on business value, feasibility and risk.
Deliverables typically include:
- A prioritised use case list
- Current-state process maps
- Data requirements
- A high-level architecture
- Evaluation criteria
- A PoC plan
- An implementation roadmap
AI PoC
The PoC package suits teams who need a fast, honest read on:
- Data quality
- RAG answer quality
- OCR and document processing accuracy
- An AI agent’s ability to call your APIs and tools
- User experience
- Model usage cost
- Fit with the business process
Expect roughly two to four weeks, depending on complexity, how quickly data can be provided, and integration requirements.
Where the data and scope are already clear, we can put an early demo in front of you during the consulting phase so you can react to the approach before committing.
Production Development
Once the PoC meets its criteria, we build the production version with everything it needs to survive daily use:
- User permissions
- Data and system integration
- Security
- Logging and monitoring
- Testing
- Human approval workflows
- Cost control
- An operations plan
- A process for updates and improvements
AI Managed Service
Quality is not fixed at go-live: data changes, the business changes, and models change underneath you.
We can take on:
- Output quality monitoring
- Analysis of incorrect answers
- Knowledge base updates
- Prompt and retrieval tuning
- Token and infrastructure cost tracking
- Model version management
- Incident response
- API integration maintenance
- Regular operational reporting
What drives development cost
Cost is not decided by which LLM you pick. The main factors are:
- Scope of the use case
- Number of systems to integrate
- Volume and format of data
- Language processing requirements
- The accuracy level you need to reach
- Security requirements
- Number of users
- Cloud, private cloud or on-premise
- How autonomous the AI agent needs to be
- Monitoring and operational support
For a small PoC with a clear scope and available data, budgets typically start from around JPY 1 million. We confirm a formal quote once requirements, deliverables and acceptance criteria are agreed.
Solutions we build
Enterprise RAG for internal document search
RAG lets the system retrieve information from documents and data you have explicitly approved before it generates an answer.
We can connect to:
- PDFs and Office documents
- Document management systems and file servers
- SharePoint
- Knowledge bases
- CRM and ERP
- Internal databases
- Process and operating manuals
Answers can carry source citations, last-updated dates and document links, so users can verify what they are told.
AI agents for internal teams
An agent executes a sequence of tasks rather than simply answering questions:
- Find information
- Validate data
- Generate reports
- Raise approval requests
- Update the CRM
- Create tickets
- Send emails
- Track work status
For consequential steps, the system can require human approval before it acts.
Customer support agents
A support agent can:
- Answer frequently asked questions
- Look up orders or contracts
- Classify incoming requests
- Summarise conversation history
- Draft replies for your agents to review
- Escalate complex cases
- Create tickets and update the CRM
The aim is to remove repetitive volume while keeping people in the cases that call for judgement or empathy.
Intelligent document processing
We combine OCR, LLMs and business rules to process:
- Contracts
- Invoices
- KYC documentation
- Forms
- Reports
- Legal documents
- Technical documentation
The system extracts data, checks it against your rules, flags what is missing and routes exceptions to the right person.
AI inside CRM, ERP and line-of-business systems
AI can live inside the tools your people already use, instead of asking them to switch to something new.
Typical applications:
- Summarising customer context in the CRM
- Generating reports from ERP data
- Answering internal policy questions
- Checking transaction data
- Creating tickets automatically
- Suggesting the next best action
- Assisting document and transaction analysis
Workflow automation with AI agents
Agents coordinate across systems to automate multi-step processes. For example:
- Receive a document by email
- Extract the data
- Validate the information
- Check it against policy
- Update the system of record
- Send the approval request
- Store the report and notify the result
AIOps and system monitoring
AIOps applies AI to logs, events and configuration changes in order to:
- Detect anomalies
- Predict risk
- Prioritise incidents
- Support root cause analysis
- Recommend or execute recovery actions
- Shorten time to resolution
AI for software development and testing
AI can support your engineering team with:
- Requirements analysis
- Specification drafting
- Test case generation
- Code review assistance
- Defect summarisation
- Log analysis
- Technical documentation
- Legacy migration support
AI assists here — engineers still review the code and the test results before anything reaches production.
How we deliver
Step 1: Understand the problem and the data
We establish:
- The problem to solve
- Who will use the system
- The current process
- What data exists today
- Which systems must be integrated
- Business risk
- Financial and operational targets
Step 2: Select the use case
Candidates are scored on four criteria:
- Business value
- Technical feasibility
- Data availability
- Risk exposure
We favour use cases that can be validated quickly and have an obvious path forward after the PoC.
Step 3: Agree the architecture and the evaluation criteria
Before development starts, both sides sign off on:
- The LLM
- Data architecture
- The integration approach
- Permissions
- Accuracy targets
- Response time
- Expected running cost
- Which cases must be handed to a human
- PoC acceptance criteria
Step 4: Build the PoC
We build the trial version within a controlled scope, and bring you into the evaluation early rather than at the end.
Where the data and initial requirements are clear enough, we can prepare an early demo so both sides can pressure-test the approach.
Step 5: Production development and integration
A PoC only moves to production once the agreed conditions are met. For example:
- Accuracy is above the agreed threshold
- Critical use cases have been tested
- Data provenance and usage rights are clear
- Permissions have been designed
- Security risk has been assessed
- Running cost is acceptable
- There is a defined error handling path
- Named owners exist for approval and operations
Step 6: Monitor, evaluate, improve
After go-live we keep watching:
- Answer quality
- Task completion rate
- Escalation-to-human rate
- User feedback
- Model and infrastructure cost
- Integration errors
- Changes in data and business rules
Case study: AI-powered contract review
Context
The client wanted to build a platform that used AI to analyse contract risk and give lawyers the information they needed to decide. Internal development capacity was limited and they had no prior offshore experience, so they needed a team that could start small and scale with progress.
What BAP did
We started with a five-person trial team and built:
- Automated contract review
- Detection of latent risk
- Suggested clause alternatives
- Rule customisation to match company policy
Team and duration
The project scaled to a maximum of 25 people per month:
- 1 project manager
- 2 bridge engineers
- 1 tech lead
- 16 developers
- 5 testers
It has run for over a year and continues to expand.
Outcome
The system reached stable operation. The offshore team grew from five to 25 people, freeing the client’s internal engineers for other strategic work.
Read the AI contract review case study
Case study: accelerating KYC with AI
Context
A financial services company needed to automate document checks in its KYC process without giving up accuracy or compliance.
What BAP did
We built a pipeline combining:
- OCR
- LLMs
- Azure AI Document Intelligence
- FastDoc
- GPT
Duration and outcome
The PoC ran for roughly two to three weeks. According to figures published by BAP, the solution cut verification time by 60%.
Why clients choose BAP
Delivery track record
BAP has completed more than 220 projects for over 100 clients. See our project work
500+ engineers
We have the bench to run anything from a small PoC to a long-term dedicated team, and to scale the team up in stages as the work proves out.
Proven in one of the world’s most demanding markets
Most of BAP’s delivery has been for Japanese enterprise clients — a market with famously exacting standards for quality, documentation and process discipline. Those habits carry into every project we run.
Local account management, offshore delivery
Communication and project management sit close to the client; engineering capacity sits in Vietnam. The model keeps response times short without inflating cost.
Engineering-led management
Many of our managers came from engineering, which makes requirement discussions and technical decisions considerably more direct.
ISO 9001 and ISO 27001
We operate quality management and information security management systems based on the ISO standards we hold.
Three engagement models
- Offshore: dedicated project managers and bridge engineers with a Vietnam-based engineering team
- Hybrid: a local point of contact working alongside the offshore team
- Onsite: the project team works at or near your office
Security and AI governance
Is our data used to train models?
We design the data handling approach around each project’s requirements and the policies of the model provider. Client data is not used for training beyond what has been agreed.
Before development starts we agree:
- Which data may be processed
- Storage region
- Retention period
- The relevant API or model policy
- Data usage rights
- Conditions for deletion
How is access controlled?
Depending on the system, we can apply:
- Role-based access control
- Single sign-on
- Multi-factor authentication
- Department- or document-level permissions
- Encryption in transit and at rest
- Audit logging
- Separate development, staging and production environments
- Least-privilege by default
The specific design follows an assessment of your security requirements.
Are prompts and user data stored?
Whether prompts are retained depends on your monitoring goals, audit requirements and data policy.
We can configure:
- No retention of sensitive content
- Masking or removal of personal data
- Metadata-only logging
- Retention limits
- Access control on logs
- Scheduled deletion
- Storage inside an environment you control
How do you reduce hallucination?
We layer several controls:
- RAG restricted to approved sources
- Mandatory source citation
- A bounded answer scope
- Business rules
- Format and fact checks
- Confidence thresholds
- Refusing to answer without sufficient grounding
- Human review
- A realistic evaluation question set
- Tracking and analysis of wrong answers
No approach removes hallucination entirely, which is why the system has to be designed around the risk level of the specific process.
Can a human approve actions?
Yes — human-in-the-loop can be designed into any consequential step.
The AI prepares the proposal; a person approves before the system will:
- Send information externally
- Execute a transaction
- Change critical data
- Make a legal or financial decision
- Delete or update records
- Carry out any high-risk action
How do you test output quality?
We build an evaluation set with you based on real scenarios, and measure:
- Accuracy
- Relevance
- Completeness
- Groundedness
- Correct source attribution
- Appropriate refusal rate
- Task completion rate
- Response time
- Cost per task
Failures are analysed and fed back into the data, retrieval, prompts, business rules or model choice.
Cloud or private environment?
We can design for cloud, private cloud, a client-managed environment or a hybrid architecture, based on:
- Data sensitivity
- Existing systems
- Compliance requirements
- Your operational capacity
- Budget
- The chosen LLM
Who owns the code and the data?
Ownership of code, data, documentation and custom components is defined in the contract.
As a rule:
- You retain the rights to your data
- BAP does not use that data outside the contracted scope
- Rights to bespoke code are agreed explicitly
- Open-source libraries and third-party services follow their own licences
- Any pre-existing BAP component or framework is listed separately if used
Frequently asked questions
How much does AI agent development cost?
A small PoC with clear scope and available data can start from around JPY 1 million. The final figure depends on the use case, integrations, data, security requirements and acceptance criteria.
How long does a PoC take?
Usually two to four weeks. Allow more time if the data is not yet available or several systems need to be integrated.
Which languages can the system handle?
We build solutions that work with English, Japanese, Vietnamese and Korean documents. Real-world quality should be measured during the PoC using a representative sample of your own data.
Which LLMs do you support?
We work with commercial or open-source models, chosen for the project on accuracy, security, cost, speed, language coverage and deployment environment.
We do not assume a single model for every use case.
Can AI integrate with legacy systems?
Yes, where an API, database or other data exchange method exists. For legacy systems without an API, we assess an integration layer, data synchronisation or phased modernisation.
How is confidential data protected?
Before development we map data flows, permissions, encryption, logging, retention, deployment environment and model provider policy. The agreed approach is documented in the design and the contract.
What happens after a successful PoC?
We evaluate the PoC against the criteria agreed up front. If it passes, we produce a production plan covering architecture, integration scope, security, testing, operations, staffing, cost and schedule.
Do you support the system after launch?
Yes. We can handle monitoring, integration maintenance, knowledge base updates, quality evaluation, cost optimisation and ongoing improvement.
Who is on the project team?
Depending on scale:
- Project manager
- Bridge engineer
- AI/ML engineer
- Data engineer
- Backend and frontend developers
- Cloud/DevOps engineer
- QA engineer
- Security specialist
- UI/UX designer
Can we start with a small budget?
Yes, and we recommend it. Start with one use case that has a clear scope, available data and a measurable success criterion. Once feasibility is proven, expand in stages.
Book a free 30-minute AI Discovery Session
In the session we will work through:
- The problem you need solved
- Candidate use cases
- The data required
- Integration feasibility
- Risks to manage
- A proposed PoC scope
- Agreed evaluation criteria
- An early demo, where scope and data allow
Contact: sales@bap.jp
