Codium Lab

AI · A new way to build and operate

Build with AI and embed it in workflow

Codium Lab doesn't position AI as just a helpful tool. Code line by line, meeting by meeting, operation cycle by cycle — we follow the texture of human work and naturally weave AI into each step.

AI adoption inquiryView our approach

Two Pillars

We place AI in two places

The place where things are built (development) and the place where work happens (workflow). Codium Lab's approach is to weave AI naturally into both.

01 · Build with AI

We build with AI

Claude · Cursor · MCP — as fast as possible within model limits.

We weave AI agents, code automation, and test generation into the everyday development cycle to shorten the time from PoC to production. Models are tools — decisions are made by people and domain.

  • Standardized pair programming with Claude Code · Codex · Cursor
  • Multi-Model routing (Opus/Sonnet/Haiku) — cost vs. quality separation
  • Connecting internal APIs/docs/schemas to models via MCP servers
  • Spec → Code → Test pipeline automation

02 · Embed in Workflow

We build systems that embed in workflow

AI at the center of workflow, in a place that elevates people.

Slack · Jira · Notion · internal wikis — without interrupting the existing flow of work, we plant RAG, agents, and automation bots inside it. So AI tools become 'colleagues you work with,' not 'tools you use.'

  • Internal document RAG assistant (meeting notes, manuals, product specs)
  • Auto analysis, summarization & triage bots for Jira/Linear/Slack
  • Custom LLM tools based on customer support & CS data
  • Observability AI combined with operational log & issue dashboards

Stack

Technologies Codium Lab can leverage

Not locked into any single vendor — we combine the right tools for each purpose.

  • Models

    • Claude (Anthropic)
    • GPT (OpenAI)
    • Gemini (Google)
    • Open-source LLMs
    • Model selection by purpose

  • Agent · Tooling

    • Claude Code
    • Cursor
    • Codex
    • MCP Server
    • Agent SDK

  • Retrieval · Data

    • Vector DB (pgvector, Qdrant)
    • Embedding pipeline
    • RAG evaluation
    • Schema sync

  • Ops · Integration

    • Slack / Jira / Notion
    • GitHub Actions
    • On-prem · VPC
    • Observability

Pattern

Four stages of AI adoption

We don't just attach results. Discover → Prototype → Integrate → Operate, four stages, built together.

  1. 01step 1

    Discovery

    We interview and observe the team's actual workflow. We first judge whether there's really a place for AI, and whether it should go there.

  2. 02step 2

    Prototype

    We build a small slice assuming production in 1–2 weeks and validate it with real-use data. A live version, not a demo.

  3. 03step 3

    Integrate

    We design AI tools to go inside existing tools (Slack, Jira, internal systems), so users don't need to open a separate screen.

  4. 04step 4

    Operate

    Prompts, models, and data all change over time. We hand over monitoring, rollback, and retraining cycles to the operations team.

Use cases

Systems we buildlike these

Adoptable scenarios — start small, reach production.

Engineering

Spec → Code → Verify Automation

When a Jira issue comes in, multiple models generate specs, the best one is adopted, and a pipeline runs automatically through code → test → verification.

Claude Code · MCP · Multi-Model

Knowledge

Internal Wiki RAG Assistant

Embed scattered meeting notes, manuals, and CS logs, then ask questions in natural language from Slack. Sources are always verified with original links.

Vector DB · Slack Bot · Security isolation

Operations

Issue Triage Bot

Issues posted to CS and operations channels are automatically classified, summarized, and tagged, then routed to the right person. Less volume for humans to read; decisions stay with people.

Agent · Classification · Routing

Harness Pipeline

LIVE

One Jira issue, shipped as code

The moment an issue arrives, the pipeline wakes. An orchestrator coordinates every step, AI routes by task type, and the skill engine executes. People only step in at approval gates.

Pipeline Flow

01 · TRIGGER

Issue Detection

AutoPilot

  • JQL condition detection
  • AutoPilot trigger
  • Issue type classification

02 · HARNESS

Pipeline Control

Orchestrator

  • Scheduler / AutoPilot
  • Approval gate
  • MCP Server
  • WebSocket control

03 · AI ENGINE

AI Processing

Task Routing

  • Task type routing
  • Cost & quality optimization
  • Parallel processing

04 · SKILLS

Skill Execution

Skill Engine

  • Auto development
  • Workflow execution
  • Auto testing
  • 35+ automation skills

05 · OUTPUT

Output

Deploy & Report

  • Pull Request
  • Issue update
  • Auto report generation

What the harness does

  • AutoPilot

    AutoPilot

    Detects issues via JQL and automatically triggers the AI pipeline. Registered as a macOS LaunchAgent daemon.

  • Scheduler

    Scheduler

    Runs skills and workflows on a Cron schedule. AI generates Cron expressions from plain language.

  • Approval Gate

    Approval Gates

    Every planning and output stage requires human review and approval before proceeding. Control lives inside automation.

  • Live Monitor

    Live Monitor

    Every session, tool, and Sub-Agent run is visible live on the dashboard via WebSocket.

Principles

Principles we keep when working with AI

  • 01

    Human-in-the-loop

    AI is never given a seat where it decides alone. A human always reviews last and presses last.

  • 02

    Secure by default

    Designed to meet security requirements: internal networks, VPC, on-premise LLMs. Data flow is defined first.

  • 03

    Operable, not magical

    We take responsibility for the cost of changing prompts, models, and tools. Production-ready systems, not magic demos.

Let's build

A place for AI in your team — let's find it together

We'll check together in a free diagnostic meeting whether your team truly has a place where AI is needed.

Apply for a free diagnostic meetingBack to main