Agents code alongside us. Not instead of us.
We ship production systems with coding agents working in parallel — Claude Code for architecture, Codex CLI for quick iteration, Gemini CLI for huge context, Snowflake Coco for data-native work. Senior engineers driving, agent teams executing.
Coding agents won't replace your engineers. They'll compound the ones who know how to drive them.
AISOFT's build loop is rewired around agentic engineering. Not to ship slop faster — to ship what used to take six months in days. The skill isn't prompting. It's orchestration: which agent does which part, in what order, with which context, under what review.
Four coding agents. Each for what it's best at.
Pick-one-tool people lose. Every agent has a sharp edge and a dull one. We know both, and we route work accordingly.
Claude Code
Our default for architecture, complex refactors, and multi-file changes. Strong at reading large codebases, producing coherent design decisions, and honoring house style via CLAUDE.md. We run it with parallel sub-agents for independent tasks.
Strengths: reasoning about system design, long-context code comprehension, agentic workflows, file-aware edits.
Sharp edges: slower on simple one-liners; can over-architect if not constrained.
Codex CLI
What we reach for when we want a loop: write, run, read the error, try again. Bash-native, fast turnaround, opinionated. Great for one-off scripts, data munging, quick tests.
Strengths: shell-native workflow, tight feedback loops, raw speed on small tasks.
Sharp edges: weaker at holding a large architecture in its head; use Claude Code for that.
Gemini CLI
Gemini's million-token context is a real differentiator for codebase-wide passes — lint entire repos, generate migration playbooks, summarize legacy systems. Also our go-to for multimodal work where Gemini Vision is the right tool.
Strengths: enormous context window, multimodal (image/video), Google ecosystem (GCP, BigQuery).
Sharp edges: less precise on local file edits than Claude; use it to analyze, Claude to edit.
Snowflake Coco
When the work is data — Snowflake warehouses, SQL, pipelines, semantic layers — Coco operates where the data lives. Think of it as a coding agent that already has your schema, your usage patterns, and query planning context.
Strengths: warehouse-native, no copying data to the model, understands cost/performance trade-offs.
Sharp edges: narrower scope — it's for data work, not general engineering.
Orchestration is the skill. Four patterns we rely on.
Do N things at once.
When a task has 3+ independent pieces — "refresh screenshots across all products," "lint every repo," "critique these landing pages" — we spawn one agent per piece, in parallel. A senior engineer orchestrates. Elapsed time goes from hours to minutes.
The agent only knows what you tell it.
We pre-build a shared memory layer (CLAUDE.md, wiki/, plan files) so every agent starts with the same mental model of the project. No more re-explaining the architecture three times a day. Context is a first-class artifact, not a prompt.
Review is the bottleneck — so make it fast.
Agents write, humans review. We keep diffs small, commits atomic, and PRs focused. Code-review agents run alongside us for first-pass catches, but a senior engineer reads every line that ships. Dan from AutoHDR said it best: "we've been dialing back some of the AI" — we agree when the reviewer can't keep up.
Different agent for different work.
Claude Code for architecture and refactors. Codex CLI for tight loops. Gemini CLI for codebase-wide passes. Coco for warehouse work. Our engineers drive all four — picking the right tool for the task in front of them.
What a quarter looks like.
Shipped systems with real users — not benchmarks, not demos.
Legacy rewrite: months → days
A focused team on Claude Code and Codex CLI rebuilds what an enterprise team needed months for — cleaner architecture, sharper UI — in days.
8 shipped products · 1 quarter
Undervolt, RefereAI, Sideline, StudyPal, CoachClaw, HD Research, Studio Copilot, Homenest. Eight live products, eight active user bases — not eight prototypes. See the work →
Small teams beat big armies.
One giant agent army doesn't work. Small, sharp sub-teams do. Make one group agent-native first; the gains pull the rest of the org along.
Multi-agent workflows in real production.
Agents wired into revenue-bearing systems, working alongside humans, scoped and evaluated end-to-end. Not pilots, not science projects.
Three engagements, one goal: your team ships faster without shipping slop.
Where are agents real leverage?
We sit with your engineering team, map your workflow, and identify the 3–5 places agentic coding actually wins — and the places it doesn't. Honest report, no sales push.
Install the stack that fits
Claude Code, Codex, Gemini CLI, Coco — whichever match your workload. CLAUDE.md conventions, shared memory, agent-ready templates, review workflows. We don't just install tools; we install the habits.
Work alongside your team
We join your repos and build with your engineers for a sprint. Show, don't tell. By the end, your team has the muscle memory to run agents without us.
Your team should be using agents. Most don't know how.
Free 30-minute call. We'll show you what changed for us — and whether it's the right call for your team.