Services
Closed Loop takes remote engagements building production AI systems. The articles on this site aren't marketing — they're the evidence, and the same rules apply to your codebase.
Autonomous task loops, multi-agent pipelines, and background workers that produce reviewed, evidenced output — not chat transcripts. Designed with hard gates, verification baselines, and human-in-the-loop control surfaces.
Good fit: Teams with a backlog that could be worked overnight, if only the output could be trusted.
Evidence — Folders Are States — an autonomous dev loop on Claude Code
Get an engineering team from chat-window usage to structured agentic workflows: skills, subagents, review separation, CI-safe verification, and the operating rules that keep autonomous coding safe on a production codebase.
Good fit: Engineering leads who have the tool licence and not yet the operating model.
Evidence — The verification and separation-of-duties patterns in the writing here
Architecture and delivery for LLM-backed products: RAG pipelines, evaluation harnesses, cloud AI services, and the unglamorous plumbing — cost control, observability, failure handling — that makes them production-grade.
Good fit: Products past the prototype, where the next bug is a customer-facing one.
Evidence — Many years of production delivery in regulated industries, plus the systems documented here
A security review of what AI built, and of the AI system itself. Two surfaces: generated code — auth and access control, injection, secrets handling, exposed endpoints, dependency and supply-chain risk in an app assembled by Lovable, Bolt, Cursor or Claude Code; and the agentic layer — prompt injection, tool permissions and blast radius, data egress, and what an agent can reach when a prompt turns hostile. Automated first pass through our own audit tooling, then manual verification of every finding, ending in a fix, refactor or rebuild call. Written authorisation required before any testing starts.
Good fit: Teams shipping AI-written or AI-powered code to production that has never had a security review.
Evidence — CosmWasm/Go audit work with a custom multi-agent audit harness, and paid bounty findings
Every offer is scoped and priced the same way — constraint, one number, fixed fee on first-year value. How we work →
How an engagement runs
A short call to find the constraint, then a written brief with one KPI — baseline and target — as the gate criterion, and a fixed fee priced on first-year value. The full method is on How we work.
Remote, in your repo, on a branch. Daily written updates; nothing merged without your review.
Every deliverable ships with its verification: test runs, baselines, and the operating rules for keeping it running.
Documented so your team runs it without us. That is the point of a closed loop.
Get in touch
What is hurting, roughly what it costs you, and how to reach you. You get a straight answer on whether Closed Loop is the right fit — including when it isn’t.
Prefer email? hello@closed-loop.dev