AI does the testing.
You set the standard.
Management, automation, performance, data, and APIs. One AI agent runs the full QA cycle. You review and approve.
Your QA stack costs more every quarter it survives.
Five vendors. Zero integration.
Jira for management. Selenium for automation. JMeter for load. Postman for APIs. Custom scripts for data. Five bills, five UIs, no single source of truth. Nothing trustworthy to show leadership.
A 3-pixel button move breaks hundreds of tests.
40–60% of your automation budget is spent fixing tests that were passing yesterday. Before you write a single new test, you’re paying rent on the old ones.
AI that only writes the opening line.
Copilot drafts a test. Who runs it? Heals it next Tuesday? Classifies the failure? Files the defect? Writes the report? Code-generation copilots get you off the blank page, then stop.
You need 10× the tests. You won’t get 10× the people.
Ship velocity is up and to the right. QA headcount is flat or down. Manual testing has a ceiling, and you’re standing on it. Performance testing alone eats 2–3 days per scenario.
Generate. Execute. Heal.
Analyze. File.
Five beats of an AI-native test lifecycle. Every one used to be somebody's job title. Your team does the one part that matters: set the standard.
Generates.
A requirement goes in. Test cases come out.
Executes.
They run at scale, cloud or local, with a live feed.
Heals itself.
When the UI shifts, the tests fix themselves and keep running.
Analyzes.
You learn what broke and why, not just that it did.
Files defects.
A pre-filled defect lands in your tracker, ready to triage.
Intent or code. Cloud or local. One engine.
Build tests by intent, by recording, by AI exploration, or in plain code. Run them in the cloud or on your own infrastructure. Every element and reusable module lives in one shared repository.
A living map of your entire application.
Crawled by AI, shared across every test. The graph is the single source of truth: every element, page, and flow lives here.
Three tries before we ask the AI.
CSS → a11y role → fuzzy match. If all fail, an LLM reads the live DOM against element history and proposes a heal, with a confidence score.
Reusable like Lego.
Login, search, checkout: package any flow as a module. Drop it into any test. Maintained in one place, used everywhere.
Your functional tests, now load tests.
Recorded flows convert to k6. AI correlates the tokens, you edit any request in the browser, and every run is judged by statistics, not vibes.
Statistics decide pass/fail.
Each transaction is gated against a rolling baseline of your own previous runs. A static threshold can't tell drift from noise; the statistical gate can.
Put your runs side by side.
Most load tools show you one run at a time. Ordel's comparison grid puts multiple runs side by side: every transaction a row, every run a column, every cell coloured by drift against the baseline you pick. Flip the metric (avg, p95, p99, throughput, error rate), and a trend sparkline per transaction shows where you're really heading. Promote any run to baseline with a click; the statistical gate re-judges against it.
Validate the data. Test the pipes.
The data behind every screen gets checked at the source. The APIs underneath get their own suite. Same agent, same graph.
Compare anything to anything.
Postgres to Excel, MSSQL to an API response. AI reads both schemas and proposes the column mapping; tolerance rules decide what equal means: 2% drift on price is fine, one cent on tax is not. Or just type "ensure no null customer IDs" and the check builds itself. Runs on a local agent next to your databases, so data never leaves your network.
APIs are tests too.
Build requests by hand or import curl, OpenAPI, Postman collections, or HAR, including the captures your perf recorder already made. Assertions are visual (status, time, JSONPath, schema) with a code escape hatch. Auth profiles (OAuth2, SigV4, mTLS) attach anywhere; secrets live in a vault, never inline.
Performance, Data, and API testing are included in Pro. See pricing →
Every artifact, linked.
From requirement to report, every artifact stays bound to the next, and the AI does the work at each step. Your team keeps the ledger honest.
Human intent. Machine steps.
One layer reads like a story for stakeholders. The other binds to real elements for the runner. Both stay in sync, automatically.
Requirement to report.
Customize every layer.
Models, fields, rules, automation, infra. Every layer ships ready, and bends to how your team works.
Hosted to start. Your own keys when you want, routed per project.
Any entity, any type. Wired into filters, reports and AI context.
Prompts, guardrails and evals. Tested in a sandbox before they go live.
Schedule runs, or trigger on Jira tickets, PR merges and deploys.
CLI for Jenkins, GitHub Actions and GitLab CI. Your machines, your network.
Management tools don't execute.
Automation tools don't manage.
Rivals caught up on the basics. Only Ordel runs the whole cycle, plus the parts no one else ships.
Comparison reflects vendor documentation as of 2026-04. Competitors' AI features verified: TestRail Copilot, mabl Auto-generated tests, Katalon StudioAssist, Testim AI. A missing dot reflects a feature the vendor does not ship today.
Replace four tools. Pay for one.
One platform for management, automation, performance, and data. Billed per seat, not per surprise.
Run AI-driven QA from day one. Management and automation, hosted AI included.
- →Up to 5 users
- →25 GB storage
- →Cloud + local execution
- →Jira bi-directional sync
- →Email support
The whole cycle, customized. Performance, data, and AI you control.
- →Up to 25 users
- →250 GB storage
- →Everything in Starter
- →Custom AI · prompts + guardrails
- →Performance + Data testing
- →All integrations · priority queue
Ordel governed for scale. On-prem AI, dedicated infra, full compliance.
- →Unlimited users + storage
- →Everything in Pro
- →SSO · SAML · audit logs
- →On-prem / custom LLM
- →Dedicated SLA
Today it asks. Soon it acts.
Ordel already runs the QA cycle and waits for your approval. The next releases shrink what needs approval: the agent explores, files, heals, re-runs, and reports. You set the boundaries it works within.
You choose how autonomous, per project.
Orchestration modes already set the register per project and per task. The roadmap extends the dial: the same agent, trusted with more of the loop.
One agent across the cycle: generates from requirements, executes, self-heals, analyzes failures, files defects, narrates reports. Every action recorded and one-click revertible: act, notify, revert.
Autonomous exploration of new builds. Defects filed with repro. Heal-and-re-run without a human in the inner loop. The attention feed becomes your only required touchpoint.
Your other agents become teammates.
An MCP server with 50+ tools, live today. Claude, or whatever your team runs, can start cycles, run comparisons, query failure history, file and close defects, pull reports. Your coding agent asks what a PR breaks before it lands.
Detail and dates live on the roadmap. See what's in build →
Stop maintaining tests.
Start shipping quality.
Ordel enters production mid-2026. Design partners are onboarding now. Your QA team stops writing tests, and starts setting the standard.