{"id":164761,"date":"2026-08-25T13:14:36","date_gmt":"2026-08-25T06:14:36","guid":{"rendered":"https:\/\/bap-software.net\/?post_type=knowledge&#038;p=164761"},"modified":"2026-08-25T13:21:35","modified_gmt":"2026-08-25T06:21:35","slug":"ai-agent-generative-ai-development","status":"publish","type":"knowledge","link":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/","title":{"rendered":"Generative AI, RAG and AI Agent Development Services for Enterprises"},"content":{"rendered":"<p>BAP builds <strong>Generative AI, RAG and AI agent<\/strong> systems for enterprises \u2014 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.<\/p>\n<p>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&#8217;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.<\/p>\n<p>Our delivery model balances quality, speed and cost \u2014 and stays with you from the first experiment through to the point where the system is producing measurable value in daily operations.<\/p>\n<blockquote><p>Book a <strong>free 30-minute AI Discovery Session<\/strong>, or email <strong>sales@bap.jp<\/strong> to talk through your specific problem.<\/p><\/blockquote>\n<h2>Why most Generative AI projects never reach production<\/h2>\n<p>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 \u2014 it is everything around it.<\/p>\n<h3>No use case with a defensible ROI<\/h3>\n<p>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.<\/p>\n<p>We assess candidate use cases against:<\/p>\n<ul>\n<li>Current workload volume<\/li>\n<li>Time and headcount consumed today<\/li>\n<li>Data quality<\/li>\n<li>Feasibility of system integration<\/li>\n<li>The cost of a wrong answer<\/li>\n<li>Expected financial or operational gain<\/li>\n<li>Room to scale after the PoC<\/li>\n<\/ul>\n<h3>The PoC succeeds but cannot go live<\/h3>\n<p>A demo that performs on a small dataset says little about security, latency, accuracy, permissions, monitoring or error handling under real load.<\/p>\n<p>Moving from PoC to production means solving:<\/p>\n<ul>\n<li>Data architecture and integration<\/li>\n<li>Per-user access rights<\/li>\n<li>Accuracy testing<\/li>\n<li>Model usage cost tracking<\/li>\n<li>Behaviour when the model is not confident<\/li>\n<li>Human approval for consequential decisions<\/li>\n<li>Logging, monitoring and incident response<\/li>\n<li>A process for updating data and prompts<\/li>\n<\/ul>\n<h3>Internal data is scattered<\/h3>\n<p>Company knowledge typically lives across file servers, SharePoint, CRM, ERP, email, PDFs, spreadsheets and bespoke line-of-business applications.<\/p>\n<p>Without a data strategy, an AI system struggles to surface information that is correct, current, and appropriate to the permissions of the person asking.<\/p>\n<h3>Concern about information leakage<\/h3>\n<p>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.<\/p>\n<p>That question is answered at the level of architecture, contract and service configuration \u2014 not by a general assurance that &#8220;AI is safe&#8221;.<\/p>\n<h3>Hallucination and unsupported answers<\/h3>\n<p>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.<\/p>\n<p>We reduce that risk with RAG, source citation, rule-based checks, confidence thresholds and human review.<\/p>\n<h3>Shortage of AI talent<\/h3>\n<p>The scarce profile is not an AI engineer \u2014 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.<\/p>\n<h3>The cost of building the team in-house<\/h3>\n<p>A complete in-house AI team means AI engineers, data engineers, a solution architect, backend developers, DevOps, QA and security specialists \u2014 assembled and retained before the first line of value is delivered.<\/p>\n<p>A managed offshore team gives you access to those skills on a flexible basis, with dedicated project management as the single point of contact.<\/p>\n<h3>Integration with existing systems is hard<\/h3>\n<p>AI only creates durable value once it is wired into the processes and systems the business already runs on \u2014 CRM, ERP, DMS, accounting, contact centre, manufacturing execution or legacy applications.<\/p>\n<p>We design AI as a component of your overall architecture, not as a chatbot sitting to one side of it.<\/p>\n<h2>How BAP works with you<\/h2>\n<p>We split delivery into four packages, so you can start wherever you are.<\/p>\n<table border=\"1\" cellspacing=\"0\" cellpadding=\"8\">\n<thead>\n<tr>\n<th>Package<\/th>\n<th>Right for you if<\/th>\n<th>What you get<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>AI Discovery<\/td>\n<td>No confirmed use case or roadmap yet<\/td>\n<td>Prioritised use cases, feasibility assessment, roadmap<\/td>\n<\/tr>\n<tr>\n<td>AI PoC<\/td>\n<td>You need to validate the technology and the value quickly<\/td>\n<td>A working prototype in about 2\u20134 weeks, plus an evaluation report<\/td>\n<\/tr>\n<tr>\n<td>Production Development<\/td>\n<td>The PoC passed, or requirements are already clear<\/td>\n<td>An integrated, tested system ready for live operation<\/td>\n<\/tr>\n<tr>\n<td>AI Managed Service<\/td>\n<td>You already run an AI system<\/td>\n<td>Monitoring, maintenance, evaluation and continuous improvement<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>AI Discovery<\/h3>\n<p>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.<\/p>\n<p>Deliverables typically include:<\/p>\n<ul>\n<li>A prioritised use case list<\/li>\n<li>Current-state process maps<\/li>\n<li>Data requirements<\/li>\n<li>A high-level architecture<\/li>\n<li>Evaluation criteria<\/li>\n<li>A PoC plan<\/li>\n<li>An implementation roadmap<\/li>\n<\/ul>\n<h3>AI PoC<\/h3>\n<p>The PoC package suits teams who need a fast, honest read on:<\/p>\n<ul>\n<li>Data quality<\/li>\n<li>RAG answer quality<\/li>\n<li>OCR and document processing accuracy<\/li>\n<li>An AI agent&#8217;s ability to call your APIs and tools<\/li>\n<li>User experience<\/li>\n<li>Model usage cost<\/li>\n<li>Fit with the business process<\/li>\n<\/ul>\n<p>Expect roughly two to four weeks, depending on complexity, how quickly data can be provided, and integration requirements.<\/p>\n<p>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.<\/p>\n<h3>Production Development<\/h3>\n<p>Once the PoC meets its criteria, we build the production version with everything it needs to survive daily use:<\/p>\n<ul>\n<li>User permissions<\/li>\n<li>Data and system integration<\/li>\n<li>Security<\/li>\n<li>Logging and monitoring<\/li>\n<li>Testing<\/li>\n<li>Human approval workflows<\/li>\n<li>Cost control<\/li>\n<li>An operations plan<\/li>\n<li>A process for updates and improvements<\/li>\n<\/ul>\n<h3>AI Managed Service<\/h3>\n<p>Quality is not fixed at go-live: data changes, the business changes, and models change underneath you.<\/p>\n<p>We can take on:<\/p>\n<ul>\n<li>Output quality monitoring<\/li>\n<li>Analysis of incorrect answers<\/li>\n<li>Knowledge base updates<\/li>\n<li>Prompt and retrieval tuning<\/li>\n<li>Token and infrastructure cost tracking<\/li>\n<li>Model version management<\/li>\n<li>Incident response<\/li>\n<li>API integration maintenance<\/li>\n<li>Regular operational reporting<\/li>\n<\/ul>\n<h2>What drives development cost<\/h2>\n<p>Cost is not decided by which LLM you pick. The main factors are:<\/p>\n<ul>\n<li>Scope of the use case<\/li>\n<li>Number of systems to integrate<\/li>\n<li>Volume and format of data<\/li>\n<li>Language processing requirements<\/li>\n<li>The accuracy level you need to reach<\/li>\n<li>Security requirements<\/li>\n<li>Number of users<\/li>\n<li>Cloud, private cloud or on-premise<\/li>\n<li>How autonomous the AI agent needs to be<\/li>\n<li>Monitoring and operational support<\/li>\n<\/ul>\n<p>For a small PoC with a clear scope and available data, budgets typically start from around <strong>JPY 1 million<\/strong>. We confirm a formal quote once requirements, deliverables and acceptance criteria are agreed.<\/p>\n<h2>Solutions we build<\/h2>\n<h3>Enterprise RAG for internal document search<\/h3>\n<p>RAG lets the system retrieve information from documents and data you have explicitly approved before it generates an answer.<\/p>\n<p>We can connect to:<\/p>\n<ul>\n<li>PDFs and Office documents<\/li>\n<li>Document management systems and file servers<\/li>\n<li>SharePoint<\/li>\n<li>Knowledge bases<\/li>\n<li>CRM and ERP<\/li>\n<li>Internal databases<\/li>\n<li>Process and operating manuals<\/li>\n<\/ul>\n<p>Answers can carry source citations, last-updated dates and document links, so users can verify what they are told.<\/p>\n<h3>AI agents for internal teams<\/h3>\n<p>An agent executes a sequence of tasks rather than simply answering questions:<\/p>\n<ul>\n<li>Find information<\/li>\n<li>Validate data<\/li>\n<li>Generate reports<\/li>\n<li>Raise approval requests<\/li>\n<li>Update the CRM<\/li>\n<li>Create tickets<\/li>\n<li>Send emails<\/li>\n<li>Track work status<\/li>\n<\/ul>\n<p>For consequential steps, the system can require human approval before it acts.<\/p>\n<h3>Customer support agents<\/h3>\n<p>A support agent can:<\/p>\n<ul>\n<li>Answer frequently asked questions<\/li>\n<li>Look up orders or contracts<\/li>\n<li>Classify incoming requests<\/li>\n<li>Summarise conversation history<\/li>\n<li>Draft replies for your agents to review<\/li>\n<li>Escalate complex cases<\/li>\n<li>Create tickets and update the CRM<\/li>\n<\/ul>\n<p>The aim is to remove repetitive volume while keeping people in the cases that call for judgement or empathy.<\/p>\n<h3>Intelligent document processing<\/h3>\n<p>We combine OCR, LLMs and business rules to process:<\/p>\n<ul>\n<li>Contracts<\/li>\n<li>Invoices<\/li>\n<li>KYC documentation<\/li>\n<li>Forms<\/li>\n<li>Reports<\/li>\n<li>Legal documents<\/li>\n<li>Technical documentation<\/li>\n<\/ul>\n<p>The system extracts data, checks it against your rules, flags what is missing and routes exceptions to the right person.<\/p>\n<h3>AI inside CRM, ERP and line-of-business systems<\/h3>\n<p>AI can live inside the tools your people already use, instead of asking them to switch to something new.<\/p>\n<p>Typical applications:<\/p>\n<ul>\n<li>Summarising customer context in the CRM<\/li>\n<li>Generating reports from ERP data<\/li>\n<li>Answering internal policy questions<\/li>\n<li>Checking transaction data<\/li>\n<li>Creating tickets automatically<\/li>\n<li>Suggesting the next best action<\/li>\n<li>Assisting document and transaction analysis<\/li>\n<\/ul>\n<h3>Workflow automation with AI agents<\/h3>\n<p>Agents coordinate across systems to automate multi-step processes. For example:<\/p>\n<ol>\n<li>Receive a document by email<\/li>\n<li>Extract the data<\/li>\n<li>Validate the information<\/li>\n<li>Check it against policy<\/li>\n<li>Update the system of record<\/li>\n<li>Send the approval request<\/li>\n<li>Store the report and notify the result<\/li>\n<\/ol>\n<h3>AIOps and system monitoring<\/h3>\n<p>AIOps applies AI to logs, events and configuration changes in order to:<\/p>\n<ul>\n<li>Detect anomalies<\/li>\n<li>Predict risk<\/li>\n<li>Prioritise incidents<\/li>\n<li>Support root cause analysis<\/li>\n<li>Recommend or execute recovery actions<\/li>\n<li>Shorten time to resolution<\/li>\n<\/ul>\n<h3>AI for software development and testing<\/h3>\n<p>AI can support your engineering team with:<\/p>\n<ul>\n<li>Requirements analysis<\/li>\n<li>Specification drafting<\/li>\n<li>Test case generation<\/li>\n<li>Code review assistance<\/li>\n<li>Defect summarisation<\/li>\n<li>Log analysis<\/li>\n<li>Technical documentation<\/li>\n<li>Legacy migration support<\/li>\n<\/ul>\n<p>AI assists here \u2014 engineers still review the code and the test results before anything reaches production.<\/p>\n<h2>How we deliver<\/h2>\n<h3>Step 1: Understand the problem and the data<\/h3>\n<p>We establish:<\/p>\n<ul>\n<li>The problem to solve<\/li>\n<li>Who will use the system<\/li>\n<li>The current process<\/li>\n<li>What data exists today<\/li>\n<li>Which systems must be integrated<\/li>\n<li>Business risk<\/li>\n<li>Financial and operational targets<\/li>\n<\/ul>\n<h3>Step 2: Select the use case<\/h3>\n<p>Candidates are scored on four criteria:<\/p>\n<ul>\n<li>Business value<\/li>\n<li>Technical feasibility<\/li>\n<li>Data availability<\/li>\n<li>Risk exposure<\/li>\n<\/ul>\n<p>We favour use cases that can be validated quickly and have an obvious path forward after the PoC.<\/p>\n<h3>Step 3: Agree the architecture and the evaluation criteria<\/h3>\n<p>Before development starts, both sides sign off on:<\/p>\n<ul>\n<li>The LLM<\/li>\n<li>Data architecture<\/li>\n<li>The integration approach<\/li>\n<li>Permissions<\/li>\n<li>Accuracy targets<\/li>\n<li>Response time<\/li>\n<li>Expected running cost<\/li>\n<li>Which cases must be handed to a human<\/li>\n<li>PoC acceptance criteria<\/li>\n<\/ul>\n<h3>Step 4: Build the PoC<\/h3>\n<p>We build the trial version within a controlled scope, and bring you into the evaluation early rather than at the end.<\/p>\n<p>Where the data and initial requirements are clear enough, we can prepare an early demo so both sides can pressure-test the approach.<\/p>\n<h3>Step 5: Production development and integration<\/h3>\n<p>A PoC only moves to production once the agreed conditions are met. For example:<\/p>\n<ul>\n<li>Accuracy is above the agreed threshold<\/li>\n<li>Critical use cases have been tested<\/li>\n<li>Data provenance and usage rights are clear<\/li>\n<li>Permissions have been designed<\/li>\n<li>Security risk has been assessed<\/li>\n<li>Running cost is acceptable<\/li>\n<li>There is a defined error handling path<\/li>\n<li>Named owners exist for approval and operations<\/li>\n<\/ul>\n<h3>Step 6: Monitor, evaluate, improve<\/h3>\n<p>After go-live we keep watching:<\/p>\n<ul>\n<li>Answer quality<\/li>\n<li>Task completion rate<\/li>\n<li>Escalation-to-human rate<\/li>\n<li>User feedback<\/li>\n<li>Model and infrastructure cost<\/li>\n<li>Integration errors<\/li>\n<li>Changes in data and business rules<\/li>\n<\/ul>\n<h2>Case study: AI-powered contract review<\/h2>\n<h3>Context<\/h3>\n<p>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.<\/p>\n<h3>What BAP did<\/h3>\n<p>We started with a five-person trial team and built:<\/p>\n<ul>\n<li>Automated contract review<\/li>\n<li>Detection of latent risk<\/li>\n<li>Suggested clause alternatives<\/li>\n<li>Rule customisation to match company policy<\/li>\n<\/ul>\n<h3>Team and duration<\/h3>\n<p>The project scaled to a maximum of 25 people per month:<\/p>\n<ul>\n<li>1 project manager<\/li>\n<li>2 bridge engineers<\/li>\n<li>1 tech lead<\/li>\n<li>16 developers<\/li>\n<li>5 testers<\/li>\n<\/ul>\n<p>It has run for over a year and continues to expand.<\/p>\n<h3>Outcome<\/h3>\n<p>The system reached stable operation. The offshore team grew from five to 25 people, freeing the client&#8217;s internal engineers for other strategic work.<\/p>\n<p><a href=\"https:\/\/bap-software.net\/case-study\/ai-powered-contract-review\/\" target=\"_blank\" rel=\"noopener\">Read the AI contract review case study<\/a><\/p>\n<h2>Case study: accelerating KYC with AI<\/h2>\n<h3>Context<\/h3>\n<p>A financial services company needed to automate document checks in its KYC process without giving up accuracy or compliance.<\/p>\n<h3>What BAP did<\/h3>\n<p>We built a pipeline combining:<\/p>\n<ul>\n<li>OCR<\/li>\n<li>LLMs<\/li>\n<li>Azure AI Document Intelligence<\/li>\n<li>FastDoc<\/li>\n<li>GPT<\/li>\n<\/ul>\n<h3>Duration and outcome<\/h3>\n<p>The PoC ran for roughly two to three weeks. According to figures published by BAP, the solution cut verification time by 60%.<\/p>\n<p><a href=\"https:\/\/bap-software.net\/case-study\/accelerated-kyc-with-ai\/\" target=\"_blank\" rel=\"noopener\">Read the AI KYC case study<\/a><\/p>\n<h2>Why clients choose BAP<\/h2>\n<h3>Delivery track record<\/h3>\n<p>BAP has completed more than 220 projects for over 100 clients. <a href=\"https:\/\/bap-software.net\/case-study\/\" target=\"_blank\" rel=\"noopener\">See our project work<\/a><\/p>\n<h3>500+ engineers<\/h3>\n<p>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.<\/p>\n<h3>Proven in one of the world&#8217;s most demanding markets<\/h3>\n<p>Most of BAP&#8217;s delivery has been for Japanese enterprise clients \u2014 a market with famously exacting standards for quality, documentation and process discipline. Those habits carry into every project we run.<\/p>\n<h3>Local account management, offshore delivery<\/h3>\n<p>Communication and project management sit close to the client; engineering capacity sits in Vietnam. The model keeps response times short without inflating cost.<\/p>\n<h3>Engineering-led management<\/h3>\n<p>Many of our managers came from engineering, which makes requirement discussions and technical decisions considerably more direct.<\/p>\n<h3>ISO 9001 and ISO 27001<\/h3>\n<p>We operate quality management and information security management systems based on the ISO standards we hold.<\/p>\n<h3>Three engagement models<\/h3>\n<ul>\n<li><strong>Offshore:<\/strong> dedicated project managers and bridge engineers with a Vietnam-based engineering team<\/li>\n<li><strong>Hybrid:<\/strong> a local point of contact working alongside the offshore team<\/li>\n<li><strong>Onsite:<\/strong> the project team works at or near your office<\/li>\n<\/ul>\n<h2>Security and AI governance<\/h2>\n<h3>Is our data used to train models?<\/h3>\n<p>We design the data handling approach around each project&#8217;s requirements and the policies of the model provider. Client data is not used for training beyond what has been agreed.<\/p>\n<p>Before development starts we agree:<\/p>\n<ul>\n<li>Which data may be processed<\/li>\n<li>Storage region<\/li>\n<li>Retention period<\/li>\n<li>The relevant API or model policy<\/li>\n<li>Data usage rights<\/li>\n<li>Conditions for deletion<\/li>\n<\/ul>\n<h3>How is access controlled?<\/h3>\n<p>Depending on the system, we can apply:<\/p>\n<ul>\n<li>Role-based access control<\/li>\n<li>Single sign-on<\/li>\n<li>Multi-factor authentication<\/li>\n<li>Department- or document-level permissions<\/li>\n<li>Encryption in transit and at rest<\/li>\n<li>Audit logging<\/li>\n<li>Separate development, staging and production environments<\/li>\n<li>Least-privilege by default<\/li>\n<\/ul>\n<p>The specific design follows an assessment of your security requirements.<\/p>\n<h3>Are prompts and user data stored?<\/h3>\n<p>Whether prompts are retained depends on your monitoring goals, audit requirements and data policy.<\/p>\n<p>We can configure:<\/p>\n<ul>\n<li>No retention of sensitive content<\/li>\n<li>Masking or removal of personal data<\/li>\n<li>Metadata-only logging<\/li>\n<li>Retention limits<\/li>\n<li>Access control on logs<\/li>\n<li>Scheduled deletion<\/li>\n<li>Storage inside an environment you control<\/li>\n<\/ul>\n<h3>How do you reduce hallucination?<\/h3>\n<p>We layer several controls:<\/p>\n<ul>\n<li>RAG restricted to approved sources<\/li>\n<li>Mandatory source citation<\/li>\n<li>A bounded answer scope<\/li>\n<li>Business rules<\/li>\n<li>Format and fact checks<\/li>\n<li>Confidence thresholds<\/li>\n<li>Refusing to answer without sufficient grounding<\/li>\n<li>Human review<\/li>\n<li>A realistic evaluation question set<\/li>\n<li>Tracking and analysis of wrong answers<\/li>\n<\/ul>\n<p>No approach removes hallucination entirely, which is why the system has to be designed around the risk level of the specific process.<\/p>\n<h3>Can a human approve actions?<\/h3>\n<p>Yes \u2014 human-in-the-loop can be designed into any consequential step.<\/p>\n<p>The AI prepares the proposal; a person approves before the system will:<\/p>\n<ul>\n<li>Send information externally<\/li>\n<li>Execute a transaction<\/li>\n<li>Change critical data<\/li>\n<li>Make a legal or financial decision<\/li>\n<li>Delete or update records<\/li>\n<li>Carry out any high-risk action<\/li>\n<\/ul>\n<h3>How do you test output quality?<\/h3>\n<p>We build an evaluation set with you based on real scenarios, and measure:<\/p>\n<ul>\n<li>Accuracy<\/li>\n<li>Relevance<\/li>\n<li>Completeness<\/li>\n<li>Groundedness<\/li>\n<li>Correct source attribution<\/li>\n<li>Appropriate refusal rate<\/li>\n<li>Task completion rate<\/li>\n<li>Response time<\/li>\n<li>Cost per task<\/li>\n<\/ul>\n<p>Failures are analysed and fed back into the data, retrieval, prompts, business rules or model choice.<\/p>\n<h3>Cloud or private environment?<\/h3>\n<p>We can design for cloud, private cloud, a client-managed environment or a hybrid architecture, based on:<\/p>\n<ul>\n<li>Data sensitivity<\/li>\n<li>Existing systems<\/li>\n<li>Compliance requirements<\/li>\n<li>Your operational capacity<\/li>\n<li>Budget<\/li>\n<li>The chosen LLM<\/li>\n<\/ul>\n<h3>Who owns the code and the data?<\/h3>\n<p>Ownership of code, data, documentation and custom components is defined in the contract.<\/p>\n<p>As a rule:<\/p>\n<ul>\n<li>You retain the rights to your data<\/li>\n<li>BAP does not use that data outside the contracted scope<\/li>\n<li>Rights to bespoke code are agreed explicitly<\/li>\n<li>Open-source libraries and third-party services follow their own licences<\/li>\n<li>Any pre-existing BAP component or framework is listed separately if used<\/li>\n<\/ul>\n<h2>Frequently asked questions<\/h2>\n<h3>How much does AI agent development cost?<\/h3>\n<p>A small PoC with clear scope and available data can start from around <strong>JPY 1 million<\/strong>. The final figure depends on the use case, integrations, data, security requirements and acceptance criteria.<\/p>\n<h3>How long does a PoC take?<\/h3>\n<p>Usually <strong>two to four weeks<\/strong>. Allow more time if the data is not yet available or several systems need to be integrated.<\/p>\n<h3>Which languages can the system handle?<\/h3>\n<p>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.<\/p>\n<h3>Which LLMs do you support?<\/h3>\n<p>We work with commercial or open-source models, chosen for the project on accuracy, security, cost, speed, language coverage and deployment environment.<\/p>\n<p>We do not assume a single model for every use case.<\/p>\n<h3>Can AI integrate with legacy systems?<\/h3>\n<p>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.<\/p>\n<h3>How is confidential data protected?<\/h3>\n<p>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.<\/p>\n<h3>What happens after a successful PoC?<\/h3>\n<p>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.<\/p>\n<h3>Do you support the system after launch?<\/h3>\n<p>Yes. We can handle monitoring, integration maintenance, knowledge base updates, quality evaluation, cost optimisation and ongoing improvement.<\/p>\n<h3>Who is on the project team?<\/h3>\n<p>Depending on scale:<\/p>\n<ul>\n<li>Project manager<\/li>\n<li>Bridge engineer<\/li>\n<li>AI\/ML engineer<\/li>\n<li>Data engineer<\/li>\n<li>Backend and frontend developers<\/li>\n<li>Cloud\/DevOps engineer<\/li>\n<li>QA engineer<\/li>\n<li>Security specialist<\/li>\n<li>UI\/UX designer<\/li>\n<\/ul>\n<h3>Can we start with a small budget?<\/h3>\n<p>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.<\/p>\n<h2>Book a free 30-minute AI Discovery Session<\/h2>\n<p>In the session we will work through:<\/p>\n<ul>\n<li>The problem you need solved<\/li>\n<li>Candidate use cases<\/li>\n<li>The data required<\/li>\n<li>Integration feasibility<\/li>\n<li>Risks to manage<\/li>\n<li>A proposed PoC scope<\/li>\n<li>Agreed evaluation criteria<\/li>\n<li>An early demo, where scope and data allow<\/li>\n<\/ul>\n<blockquote><p><strong>Contact:<\/strong> sales@bap.jp<\/p><\/blockquote>\n<p><\/p>","protected":false},"author":43,"featured_media":164763,"template":"","meta":{"_acf_changed":false},"tags":[],"blog-cat":[],"class_list":["post-164761","knowledge","type-knowledge","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.1 (Yoast SEO v28.1) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Generative AI, RAG &amp; AI Agent Development | BAP<\/title>\n<meta name=\"description\" content=\"BAP builds Generative AI, RAG and AI agent systems for enterprises \u2014 from a 2\u20134 week PoC through production integration, operations and continuous improvement.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Generative AI, RAG and AI Agent Development Services for Enterprises\" \/>\n<meta property=\"og:description\" content=\"BAP builds Generative AI, RAG and AI agent systems for enterprises \u2014 from a 2\u20134 week PoC through production integration, operations and continuous improvement.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/\" \/>\n<meta property=\"og:site_name\" content=\"Software development - offshore service | BAP Software\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/bap32\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-25T06:21:35+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cdn.bap-software.net\/2026\/08\/25123353\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1731\" \/>\n\t<meta property=\"og:image:height\" content=\"909\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@bapsoftware\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"49 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/\",\"url\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/\",\"name\":\"Generative AI, RAG & AI Agent Development | BAP\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/cdn.bap-software.net\\\/2026\\\/08\\\/25123353\\\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png\",\"datePublished\":\"2026-08-25T06:14:36+00:00\",\"dateModified\":\"2026-08-25T06:21:35+00:00\",\"description\":\"BAP builds Generative AI, RAG and AI agent systems for enterprises \u2014 from a 2\u20134 week PoC through production integration, operations and continuous improvement.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[[\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/\"]]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/#primaryimage\",\"url\":\"https:\\\/\\\/cdn.bap-software.net\\\/2026\\\/08\\\/25123353\\\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png\",\"contentUrl\":\"https:\\\/\\\/cdn.bap-software.net\\\/2026\\\/08\\\/25123353\\\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png\",\"width\":1731,\"height\":909},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/ai-agent-generative-ai-development\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/bap-software.net\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Knowledge\",\"item\":\"https:\\\/\\\/bap-software.net\\\/en\\\/knowledge\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Generative AI, RAG and AI Agent Development Services for Enterprises\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/bap-software.net\\\/en\\\/#website\",\"url\":\"https:\\\/\\\/bap-software.net\\\/en\\\/\",\"name\":\"Software development - offshore service | BAP Software\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/bap-software.net\\\/en\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Generative AI, RAG & AI Agent Development | BAP","description":"BAP builds Generative AI, RAG and AI agent systems for enterprises \u2014 from a 2\u20134 week PoC through production integration, operations and continuous improvement.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/","og_locale":"en_US","og_type":"article","og_title":"Generative AI, RAG and AI Agent Development Services for Enterprises","og_description":"BAP builds Generative AI, RAG and AI agent systems for enterprises \u2014 from a 2\u20134 week PoC through production integration, operations and continuous improvement.","og_url":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/","og_site_name":"Software development - offshore service | BAP Software","article_publisher":"https:\/\/www.facebook.com\/bap32","article_modified_time":"2026-08-25T06:21:35+00:00","og_image":[{"width":1731,"height":909,"url":"https:\/\/cdn.bap-software.net\/2026\/08\/25123353\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_site":"@bapsoftware","twitter_misc":{"Est. reading time":"49 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/","url":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/","name":"Generative AI, RAG & AI Agent Development | BAP","isPartOf":{"@id":"https:\/\/bap-software.net\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/#primaryimage"},"image":{"@id":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/#primaryimage"},"thumbnailUrl":"https:\/\/cdn.bap-software.net\/2026\/08\/25123353\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png","datePublished":"2026-08-25T06:14:36+00:00","dateModified":"2026-08-25T06:21:35+00:00","description":"BAP builds Generative AI, RAG and AI agent systems for enterprises \u2014 from a 2\u20134 week PoC through production integration, operations and continuous improvement.","breadcrumb":{"@id":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":[["https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/"]]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/#primaryimage","url":"https:\/\/cdn.bap-software.net\/2026\/08\/25123353\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png","contentUrl":"https:\/\/cdn.bap-software.net\/2026\/08\/25123353\/exec-c9fe06e2-65d8-4ba0-9c4a-0130086df067.png","width":1731,"height":909},{"@type":"BreadcrumbList","@id":"https:\/\/bap-software.net\/en\/knowledge\/ai-agent-generative-ai-development\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/bap-software.net\/en\/"},{"@type":"ListItem","position":2,"name":"Knowledge","item":"https:\/\/bap-software.net\/en\/knowledge\/"},{"@type":"ListItem","position":3,"name":"Generative AI, RAG and AI Agent Development Services for Enterprises"}]},{"@type":"WebSite","@id":"https:\/\/bap-software.net\/en\/#website","url":"https:\/\/bap-software.net\/en\/","name":"Software development - offshore service | BAP Software","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/bap-software.net\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/knowledge\/164761","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/knowledge"}],"about":[{"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/types\/knowledge"}],"author":[{"embeddable":true,"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/users\/43"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/media\/164763"}],"wp:attachment":[{"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/media?parent=164761"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/tags?post=164761"},{"taxonomy":"blog-cat","embeddable":true,"href":"https:\/\/bap-software.net\/en\/wp-json\/wp\/v2\/blog-cat?post=164761"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}