AI development questions.
Clear, practical answers.

We build LLM apps, AI SaaS, AI agents, RAG, chatbots, web services and platform MVPs. We also handle automation, crawling and data pipelines, integrations, dashboards and existing-system maintenance. One team covers planning through source-code handover.

There are three ways to start: a two-week pilot to validate one core process in 14 days; AI product and system development, typically 2–6 months; or automation and integration, typically 2–8 weeks. If this is your first AI project, a pilot can lead into a larger build. Share your situation in a free consultation and we will suggest a suitable starting point.

Yes. A sentence about what you need is enough. We review feasibility, likely cost and timing within the same business day, and clarify requirements in a free consultation. If there is a better route than custom development, we will recommend it.

The two-week pilot is fixed at 14 days. Automation and integration typically take 2–8 weeks, while AI products and systems take 2–6 months. The exact timeline is stated in the quote after scope is agreed. We share intermediate deliverables and provide a preview link as soon as an interface is available.

Systems with APIs, including Google Workspace, Slack, Notion, ERPs and online stores, can be integrated. For legacy systems without APIs, we consider scraping or RPA; on-premise systems can connect through a VPN. We define an integration scope that avoids conflicts with existing systems before work begins.

Start with frequent, repetitive tasks whose rules can be explained. Existing examples include reconciliation and spreadsheet entry at about 15 minutes per item, customer support with 78% of 180 daily inquiries answered automatically, and learning-material production reduced from four hours to 18 minutes per item. Ambiguous values are flagged for human review instead of automatic judgment.

We estimate from functional scope, complexity and duration, and include the basis of the estimate. A confirmed quote stays fixed during delivery. If the scope changes, we agree on an additional quote before proceeding.

A two-week pilot has a fixed price of KRW 5–7 million, excluding VAT. Automation and integration start in the low millions of KRW, and AI products and systems start around KRW 20 million, depending on scope. A detailed written quote follows the free consultation.

Running costs depend on workload; most are around KRW 500,000–2 million per month. We estimate AI API token charges, cloud infrastructure and OCR separately during quoting. Additional costs do not arise without prior approval.

It depends on the work. In existing case studies, a D2C fashion brand recovered its pilot cost in two months through approximately KRW 2.8 million in monthly labor savings, while a B2B SaaS project recovered it through additional first-month revenue. We can calculate an estimate for your workflow during a free consultation.

We confirm scope, duration and cost in writing and contract per project. Payment is split 50% at signing and 50% on acceptance, with electronic tax invoices. One month of defect warranty after handover is included.

The customer owns them. We hand over 100% of the source code, runtime setup and operating documentation in a Git repository, with no usage expiry or separate licensing fee. Your internal team or another development company can take over operations.

Data is stored only in the customer’s operating environment, such as AWS, GCP, Azure or on-premise infrastructure. We do not transfer it to Plan AI servers and prohibit external removal. Sensitive information is processed after PII masking.

No. We configure the AI provider’s Zero Data Retention option to prevent input data from being used for model training. A separate isolated environment can be configured if needed.

Yes, including during consultation before the project contract. Shared materials are used only for estimation. When publishing a case study, our principle is to keep company names private and disclose the figures and methods.

We follow least privilege: requesting only the permissions needed for the project, such as read-only access for reporting automation. Issued accounts and permissions are returned when the project ends.

We combine Claude and GPT according to the task, such as complex reasoning, long-document analysis or code generation. Hybrid setups can balance cost and performance. We also consider open-source LLMs when appropriate.

We design with hallucinations in mind. Answers show supporting evidence and sources, guardrails limit unsupported output, and ambiguous values are flagged for human review. We provide accuracy-validation results based on real data before handover.

The system can make context-aware judgments beyond fixed rules. It can handle changing document formats and connect steps such as reading content, assessing urgency, assigning an owner and delivering a summary. If rules alone are enough, we will recommend building without AI.

We design three safeguards: automatic retries, alerts if retries fail, and rollback to an earlier version. Logs make important tasks available for manual handling. Bugs and defects are fixed free of charge during the one-month warranty; after that, support terms can be agreed in a monthly maintenance plan.

Yes. We deliver the full source in a Git repository. Python and TypeScript make it possible for other developers to take over, and documentation explains the system and how to extend it. A monthly maintenance plan is available by agreement if you need help operating it.