Alchemy

Every conversation and artifact is persisted and organized, so work survives the session it happened in.

Assistants read and extract information from your own material instead of a blank page.

Several people can edit the same artifact at once, alongside the assistant, with comments and feedback on every suggestion.

A modern web interface is included out of the box, with the account and usage controls a whole team needs.

The platform is parametric with respect to both LLM and provider — no lock-in to a single model or vendor.

Advanced LLM tracing gives real observability into what an assistant did and why, instead of a black box.

Alchemy already powers assistants we’ve built ourselves — Cuprum for use case modeling, Iridium for knowledge management, and the assistants inside CuTE for test automation.

When nothing in the catalog fits, we build an assistant specifically for your process and systems — on the same platform, with the same human-in-the-loop guarantees.

Every assistant, ours or bespoke, shares the same foundation, so adding one more never means starting from scratch.

The task itself — and the best practice it applies — is adapted to your established processes and approval workflows.

Review processes, confidence thresholds, and audit trails match how your team already governs its own work.

Output takes your document templates, naming conventions, and terminology — so it looks like it came from your team, because it did.

An assistant is reached through the built-in web UI, or wired into another one entirely — a customer’s own portal, an IDE such as VSCode, or a modeling tool such as Enterprise Architect or Miro.

Deployment is just as flexible: Alchemy can run on site, inside your own VPC, for organizations that need data and LLM traffic to stay within their own infrastructure and security perimeter.

The same pattern extends to other steps of the lifecycle — automatically breaking use cases into well-sliced user stories, generating acceptance criteria and Gherkin scenarios, mapping feature impact, standardizing bug reports, documenting code, or scoring test suite quality.

Each is a candidate for an off-the-shelf assistant or a bespoke one, built the same way.

Every assistant on Alchemy can be connected to Alchemy’s Control Hub, which provides live observability into what each agent is doing, step by step, turning “the output was wrong” into “here’s exactly why.”

Execution parameters and key events are captured for every run in a full, statistically verifiable log.

AI engineers run standardized, auditable evaluations against selected assistants or LLMs, on demand — replacing ad hoc spot checks with evidence comparable across runs, models, and versions.

Results are reviewed, tagged, and preserved in a searchable knowledge base, shareable with reviewers.

Feedback from production use feeds new evaluation datasets, and can be turned automatically into evaluations checked against AI-as-a-judge and LLM observability techniques.