DevLoops is fundamentally a temporal state model of a codebase, designed to be explored as it existed at any point in its history.
We ingest your git history — your code and the third-party code you consume — into a persistent, auditable graph of mechanisms and relationships. Security reviews, institutional knowledge, oversight and agent-ready comprehension come from the same source of truth.
Your AI tools forget. Every conversation starts from zero, and context windows can't hold a large codebase. DevLoops gives agents durable memory of what your code actually does: instead of re-reading the repository every prompt, they query the mechanism graph — answers grounded in real structure, persistent across sessions and tools.
This innovation has as a result very cheap Zero-Data-Retention multi-step queries that allow us to provide the rest of DevLoops services like cybersecurity analysis, knowledge-management and managerial oversight in a high quality / cost-effective manner.
This setup is useful for clients who want to use it with Claude Code, Codex or Cursor, while using another model. DevLoops hands the agent a pre-investigated evidence pack: located mechanisms, verified coordinates, and explicit coverage of what was and wasn't found — so the agent starts from facts, not from an empty repo.
This can be critical in enterprise Zero-Data-Retention environment that the provider's cache is not there to lower the investigation's price.
And unlike today's coding tools, that memory has a time axis. Agents can comprehend the codebase as it stood at any commit — what a mechanism did before a regression, which change introduced a call edge, what a revert actually reverted — instead of reasoning only about HEAD. Debugging, auditing, and archaeology become questions the agent can answer from reconstructed state, not guesswork over git log.
All of it is exposed both as an on-site chat and as an API powering our [2] Cybersecurity, [3] Managerial Oversight and [4] Knowledge transfer capabilities
Using DevLoops proprietary technology and innovations, surface accidental flaws and maliciously positioned mechanisms alike — in your own code and the third-party code you use. Every change is judged against its stated intent: a behavior-affecting diff under a cosmetic goal, a silently weakened security control, an obfuscated payload. Reviews are stored per commit, permanently.
The EU Cyber Resilience Act (Regulation (EU) 2024/2847) entered into force on December 10, 2024. By December 11, 2027, all in-scope hardware and software products sold in the EU must meet essential cybersecurity requirements, pass conformity assessments, and carry the CE marking.
Where DevLoops maps to the text:
DevLoops does not make you CRA compliant — no tool does. It generates the continuous, code-level evidence your conformity assessment asks for.
Whoever oversees an external development team — a programme officer, a project owner, a procuring institution — works from the contractor's own reporting: status decks, tickets, invoices. The repository, the one artifact that can't be spun, is the one they can't read. DevLoops changes that.
Every project manager knows the gap between the ticket board and the repository the hard way: tickets say done, the code says otherwise. Managers like to think that "if it is not in JIRA it does not exist" but the truth is far scarier than that. DevLoops closes that gap from the code side: every change is recorded with what it actually did, not what was claimed; effectively closing the gap between reality and claim. DevLoops is a game changer for managers because every engineering claim made in the past or present is now auditable.
Ingest the contractor's repository and the overseer can interrogate it directly, in plain language: what was actually built this quarter, what changed in the module the incident report blames, does each commit do what its message claims. Answers are grounded in the code, with unverified inferences marked — and the per-commit record is independent of anyone's reporting.
DevLoops doesn't replace the overseer's judgment — it gives that judgment evidence. The human decides; the record makes the decision informed.
Your codebase is among your least documented strategic assets: the reasoning behind every change lives in the heads of the engineers who wrote it — until they leave. DevLoops captures the intent and mechanism of each code change at the moment it happens, so know-how stays in the company instead of walking out the door.
Using DevLoops proprietary technology and innovations, anyone can consult our specially engineered dynamic interface for Questions and Answers — not just programmers. Engineers get technical depth with cited sources; managers and product owners ask in plain language: “how does our product handle an emergency stop?”, “what changed in the door controller since 2022, and why?” Answers are grounded in the actual code, and unverified inferences are marked as such.
On our roadmap: the same machinery generalises to change streams with the opposite problem — ECOs and design revisions in your PLM system, thoroughly documented for compliance yet nearly impossible to interrogate. Decades of recorded reasoning, finally consultable. We'll build it with the right partner when they appear.
Your data, your keys — not a data trap
DevLoops stores your entire analysis in your own Redis database, under your account — and runs inference through your own API key, with the provider of your choice. Cancel any time and the knowledge base is still yours, sitting in your infrastructure.
Battling commit flaws & attacks — and forgotten know-how — one mechanism at a time.
No. Vector retrieval is one of many discovery mechanisms we use—and it works very well for the problems it is suited to. But DevLoops is fundamentally a temporal state model of a codebase, designed to be explored as it existed at any point in its history.
The system combines structural indexes, nodes and edges, dependency relationships, commit-scoped state, and multiple deterministic and probabilistic discovery tools. This makes advanced as-of reasoning practical: asking not only what the code looks like now, but what was knowable, connected, changed, or unexplained at a particular commit.
That matters for time-travel review, forensic analysis, long-range dependency reasoning, and effective audit of codebases—and potentially other versioned engineering systems such as ECOs.
Efficient as-of reasoning is a core design requirement. Without it, deep historical review quickly becomes too slow and expensive to use routinely.
An LLM can be an excellent code reviewer. The problem is that an LLM by itself does not provide the state model needed for reliable historical reasoning.
DevLoops combines deterministic infrastructure with probabilistic reasoning. Evidence retrieval, temporal state, provenance, structural relationships, and attribution can be constrained and reproduced, while models are used where judgment and interpretation are valuable.
That creates several important differences:
The goal is not to replace probabilistic reasoning. It is to give it a better substrate.