A software factory your team owns and runs

Build what you intend, and use what you learn to refine that intent faster. ROBOCO helps your team establish a production system for continuously building and improving products with AI.

We work with companies bringing outsourced development in-house, businesses pursuing digital or AI transformation, and digital-native teams seeking faster, more accurate delivery beyond vibe coding.

A software factory connects intent → implementation → verification → deployment → observation and improvement. Your team sets the goals and criteria for judgment. Together, we define what AI can execute and where human review is needed. Automated guardrails and harnesses apply security and cost criteria throughout this workflow. The specific security controls are described below in A Software Factory with Built-In Security.

You can explore the concepts and operating principles behind this approach in The Software Factory, a book ROBOCO is writing in public, and see the full flow from request through verification, deployment, and self-improvement working end to end in The Software Factory Demo.

  • Practical pilot: Validate the workflow through real work, leaving your team with a working result and experience improving it yourselves.
  • Software factory setup: Build repeatable pipelines, verification environments, operating standards, and the capability to run them.
  • Operational improvement: Improve the product and its production system while developing your team’s ability to evaluate and adopt new technology.

Training and advisory are integrated into real work at every stage. Certification and compliance consulting remains a specialist service for entering regulated markets.

A Software Factory with Built-In Security

As vibe coding speeds up development, it becomes harder to rely on people reading every line of code to check security. The software factory ROBOCO builds does not treat security as a separate step at the end. Instead, it builds automated security controls into every stage of the production flow, from intent definition to observation and improvement. These controls are enforced by code and repository protection rules, and we design them so that even when an AI agent makes a mistake or is influenced by malicious input, it cannot skip them to reach production.

Production flow Security controls we apply to your factory
Intent definition Request text is treated as untrusted input to defend against prompt injection. Requests from outside are not implemented until an authorized person approves them.
Implementation AI agents receive only the minimum permissions and secrets their work requires. If an agent changes the factory’s verification rules or test criteria, the checks fail.
Verification A different model from the one that wrote the code performs a cross-review. For bug fixes, the factory automatically confirms that the reproduction test fails on the code before the fix and passes after it.
Deployment The risk tier is determined from the paths of changed files. Changes to authentication, data migrations, dependencies, or factory rules are merged and deployed only after human approval. Deployment secrets stay in the deployment stage only.
Observation and improvement Production is checked regularly, and errors and user feedback automatically become issues. When an incident occurs, an automated postmortem analyzes the cause and, if confidence conditions are met, reverts the causing change.

Every change leaves a record, in PR reports and commit signatures, of which agent and model made it, on what basis, and who approved it. This record can serve as change-management evidence in security audits and certification reviews. We design the specific configuration of these controls with you to fit your risk criteria and regulatory requirements, and you can see them working in The Software Factory Demo.

1. Practical pilot

We select a real business task or feature and build a connected development, verification, and deployment workflow. Your team builds a feature and makes the next improvement, learning which systems and capabilities it needs.

What we do together

  • Clarify the problem and intent: Define the intended behavior, expected results, and success criteria with your domain experts.
  • Build a working result: Implement with AI, then verify and deploy against the agreed criteria.
  • Apply guardrails: Establish security and cost criteria and human review points appropriate to the pilot, and incorporate them into the workflow. Baseline security controls such as risk-tier approval rules and secret isolation apply from the first day of the pilot.
  • Practice improving it yourselves: Your team observes how the result is used, then iterates on intent and functionality.
  • Define the next stage: Use the bottlenecks and lessons identified to scope the software factory setup.

What stays with your team: A verifiable result, a reusable workflow, practical experience, and a plan for the next stage.

We agree on scope, timeline, cost, and success criteria for each engagement during the initial consultation. Discuss a pilot

2. Software factory setup

We extend the approach validated in the pilot into a production system your team can run continuously. We build an AI-native development process grounded in modern software engineering, with a cloud-native architecture suited to your environment.

What we do together

  • Make intent and knowledge reusable: Organize requirements, domain knowledge, and decision rationale so people and AI can use them together.
  • Build development pipelines: Connect and automate the recurring steps of implementation, verification, and deployment.
  • Design harnesses and guardrails: Build the environment in which AI work is executed and verified, with security and cost criteria and review and approval procedures. Security controls such as risk-tier assessment, cross-review, evidence checks, and human approval gates are implemented in code across every SDLC stage.
  • Establish a cloud-native foundation: Design infrastructure configuration, deployment, and operations so your team can provision and change the environments it needs.
  • Enable domain experts to develop: Combine practical training and joint development so people without a development background can turn their expertise into features and improvements.
  • Transfer operational capability: Establish the documentation, guidelines, verification criteria, and skills your team needs to operate and improve the system.

What stays with your team: Development environments and knowledge you own, repeatable production processes, and the capability to operate them yourselves.

3. Operational improvement

Feed lessons from product use into the next iteration of intent, and improve the production system alongside the product. We help even small teams evaluate rapidly evolving AI technology and apply it to their work.

What we do together

  • Review intent and outcomes: Check whether features solve the real problem and prioritize the next improvements.
  • Improve verification: Incorporate discovered errors and recurring problems into tests, guidelines, and review procedures.
  • Manage security and cost: Review AI usage and cloud operations, then adjust guardrails to the agreed criteria. Production checks, automated incident analysis, and reverting causing changes automate security response in operations.
  • Evaluate and adopt AI capabilities: Assess models and tools against real work and incorporate them after verifying their effectiveness.
  • Expand team autonomy: Use regular reviews and training to broaden the decisions and improvements your team can handle internally.

What stays with your team: Operating practices for continuous improvement, accumulated verification criteria, and the capability to respond to technological change.

Client case: from a prototype in one month to ongoing improvement

This case is presented without information that identifies the client.

After a prolonged outsourcing effort failed to deliver the intended solution, a company launched a prototype within one month of working with ROBOCO. A small development team has since continued releasing and improving the product while adopting advances in AI.

Together, we changed how development worked:

  • Introduced an AI-native development process grounded in modern software engineering.
  • Established a cloud-native architecture that allows the team to configure and operate the infrastructure it needs flexibly.
  • Built robust pipelines and harnesses that apply security and cost criteria throughout development and verification.
  • Established the capabilities and development system for domain experts with no prior development background to build and improve features themselves.

The client is currently transitioning toward a software factory with ROBOCO. One month was this client’s time to a prototype; each pilot’s scope and timeline are agreed according to the client’s situation.

A production system and the capability to run it

ROBOCO builds the factory with your team and transfers the ability to operate and improve it. Executives and leaders set direction and judgment criteria. Domain experts and developers use AI to experiment and verify results themselves. Your team’s independence is our success.

We apply ROBOCO’s methods and tools, including Intent Engineering and VDLC, to your situation. You can also read our perspective on the software factory.

Certification & Compliance Consulting — Entering Regulated Markets

Entering Korea’s regulated markets requires certification. Cloud providers serving the financial sector must pass K-FSI (the Financial Security Institute’s CSP Safety Assessment), and companies that need to prove their information security posture must earn ISO 27001 or K-ISMS. ROBOCO accelerates the entire certification journey — criteria analysis, gap review, evidence management, and response drafting — with AI agents that run inside your private tenancy.

The materials involved in certification prep are your company’s most sensitive documents: security policies, audit evidence, contracts. In ROBOCO’s approach, none of it leaves your environment. Because both the agents and the knowledge base operate entirely within infrastructure you own and control, the process of preparing for compliance never violates compliance itself.

Right for you if you’re

  • A global SaaS/cloud company preparing for the K-FSI assessment to win Korean financial-sector customers
  • A company facing initial certification or renewal for ISO 27001 or K-ISMS(-P)
  • A company whose headquarters works in English while the audit runs in Korean, making bilingual response and evidence management a burden
  • A company where audit know-how accumulates only in individuals — not the organization — forcing a restart from scratch every year

What’s included

  • Criteria structuring and gap review — We structure the assessment criteria item by item (e.g., K-FSI’s 11 control domains, 48 assessment items, and 162 assessment methods) and run gap reviews based on the previous year’s findings. Reviews are cross-validated across different LLMs to filter out single-model bias.
  • A regulatory knowledge base (LLM Wiki) — We compile assessment criteria, applicable laws, internal controls, and evidence into a cross-referenced wiki. Unlike RAG, which re-searches every time, knowledge understood once gets reused — so response speed compounds throughout the audit, and after certification the wiki remains your asset, becoming the starting point for next year’s renewal.
  • An evidence management system — We build a three-layer mapping from assessment item (§X.Y.Z) to control to evidence file, and test accessibility by asking, “When an auditor reviews this item, how many steps does it take to reach which materials?” Auditor feedback is decomposed and classified at the atomic level so nothing slips through.
  • A register of applicable laws — We collect the full text of the Korean laws cited in the assessment (the Cloud Computing Act, the Regulation on Supervision of Electronic Financial Transactions, the Personal Information Protection Act, and more) and map each to its assessment items, structurally preventing “failure to review applicable laws” findings.
  • Bilingual response management — We review translation and terminology consistency between English draft responses and the Korean submissions (Korean audits accept only Korean-language documents), resolving the language barrier between headquarters and the Korean audit at the workflow level.
  • Operational internalization — After certification, we embed guidelines and workflows so your internal team can run evidence collection, verification, and answer-quality control on a repeatable basis.

Real-world applications

  • K-FSI safety assessment response for a global SaaS company — We compiled 156 pages of assessment criteria and five applicable laws into a private knowledge base, and cross-validated gap reviews based on the previous year’s audit findings across multiple LLMs. We decomposed some 280 auditor feedback items to the atomic level, mapped them to some 50 evidence folders, and ran the daily cycle of feedback processing, evidence submission, and re-request handling as an AI agent workflow — securing both speed and consistency in audit response. Every document was processed exclusively within the client’s own environment.

ROBOCO’s role: Certification preparation partner. The data stays in your tenancy and the know-how stays in your organization; ROBOCO provides the methodology and the agent workflows.

Start with your own business needs

In an initial consultation, we look at your team, product, and the work you want to improve. Together, we select a real task and agree on the pilot’s scope, timeline, cost, and success criteria before starting.

Discuss a pilot · View training and workshop materials