Prevent outages before customers feel them
Catch regressions in PRs and pre-production, before customers see failures.
- Nya regressions ansa na wɔayɛ release
- Tew nsakraeɛ a ɛsɛe ho dodoɔ so
- Fa awerɛhyɛmu di kan ntɛm
Zof te nhyehyɛe a wo nsɔhwɛ bɔ ho ban no ase.
Platform no kyerɛ adwuma, dependencies, ne CI/CD pipeline a ɛde code kɔ wɔn mu no bere biara. Asiane nsɛnkyerɛnne kɔ graf no so sɛnea nsakrae a ɛsan ba wɔ adwuma baako mu no da biribiara a ɛka ho adi.
ADWUMA A WƆAMAP
Adwuma 20
Wɔ queues, caches, asomfo, ne externals mu.
NSAKRAE HO NIMDEƐ
CI/CD nsɛm
Pipeline ba graf no nkyɛn.
ASIANE HO NTRƐWMU
Edge-level nsɛnkyerɛnne
Mfomso ne dependencies kɔ.
- 01 · SERVICE TOPOLOGY
20 services
28 dependency edges
- 02 · RISK SIGNALS
2 active
83% coverage observed
- 03 · CI/CD AWARENESS
Build succeeded
- Azure DevOps
- 8m 22s
Nsɛm a ɛba ankasa boɔ
Production nsɛm a ɛba keka revenue, customer gyidie, ne mfidie ho mmirikatu ho. Dodoɔ no ara wobetumi asira ho kwan a wɔbɛfa ɔkwan pa so ahwɛ.
$5.6M
Avɛrɛji boɔ wɔ dɔnhwerew biara a wɔanyɛ adwuma (enterprise)
Sika hwere
Sika a wɔhwere tee firi service a enni hɔ ne transaction a ɛhwere mu
80%
Nsɛm a ɛba a nsakraeɛ na ɛde ba, ɛnyɛ infrastructure
Customer gyidie ne churn
Brand sɛeɛ a ɛkyɛ ne customer a wɔfiri a efiri ahotɔsoɔ nsɛm
60%
Nsɛm a wobetumi asira ho kwan a wɔde pre-production testing a ɛyɛ papa bɛfa so
Mfidie ho basabasayɛ
Akuw brɛguo, context sesa, ne dwumadie a ɛkyɛ a efiri nsɛm ho mmuaeɛ
Nea enti a anosiabɔ ho hia sen mmuaeɛ: Bere a nsɛm ho mmuaeɛ ho hia no, sɛ wosira nsɛm a ɛsɛe ho kwan ansa na aduru production no tew boɔ so, kora customer gyidie so, na ɛma mfidie ho akuw ani ka adesie ho na ɛnyɛ ogya dum.
Nea enti a nsɛm a ɛba kɔ so ba (mpo a monitoring wɔ hɔ)
Akwan dada nya nsɛm wɔ akyi a aba. Anosiabɔ hia validation ansa na aduru production.
Monitoring hu nsɛm a ɛsɛe wɔ akyi a aka customer
Observability nnwinnadeɛ bɔ wo amaneɛ bere a biribi asɛe, nanso saa bere no na customer ho aka dada.
Scripts sɛe bere a systems renkɔ akyi
Test suites yɛ mmerɛw bere a applications resakra, na ɛyɛ akwan a coverage nni mu.
Test coverage nyɛ pɛ sɛ ahotɔsoɔ
Coverage akontaa a ɛyɛ kɛse betumi de integration tests a enni hɔ ne edge case nhwehwɛmu asie.
Release mmirikatu ma asiane kɔ soro
Deployments a ɛyɛ ntɛm ma kwan a regressions betumi awura mu a ɛbɛfa mu no dɔɔso.
Sɛnea Zof sira nsɛm a ɛba ho kwan
Ɔkwan a emu da hɔ a wɔahyehyɛ a wɔde nya regressions ansa na aduru production.
Yɛ critical workflows ne dependencies mfonini
Zof yɛ wo environment System Graph: services, APIs, data flows, ne integrations. Bere a wɔayɛ nsakraeɛ no, ɛnim deɛ ebetumi aka no pɔtee.
Tu validation agents a wɔayɛ no soronko
AI agents 100+ sɔ dimension biara hwɛ: functional correctness, performance, security, compatibility, ne integration apɔmuden. Agent biara yɛ ɔbenfo wɔ ne domain mu.
Hyɛ ase kɔ so (PR, deploy, schedule)
Pull request biara, commit biara, scheduled run biara nya validation. Wonya nsɛm wɔ simma kakra mu, ɛnyɛ wɔ deployment akyi.
Nya regressions wɔ UI, APIs, integrations mu
Agents hwɛ end-to-end workflows, API contracts, third-party integrations, ne user-facing flows. Biribiara nfa mu nkɔ.
Sira release a ɛyɛ asiane kwan ansa na ɛaka
Bere a validation hwere no, wɔsira releases ho kwan ankasa. Mfidie ho akuw nya feedback a emu da hɔ a wobetumi de adi dwuma de siesie nsɛm ansa na aduru production.
Nsɛm a ɛba anosiabɔ loop
Validation a ɛkɔ so sira releases ho kwan ansa na aka production.
Enterprise nsunsuanso a ɛho hia
Akontaa ne tumi a ɛkyerɛ ahotɔsoɔ nkɔso ne ahyɛnsoɔ a ɛboa adwumakuw.
Kɔ 95% so
Production nsɛm kakraa bi
Nya regressions ansa na deployment aba, na ɛtew production nsɛm so na ɛma DORA akontaa yɛ papa.
Kɔ 90% so
Release nkɔsoɔ a ɛyɛ ntɛm
Validation apono a ɛyɛ adwuma ankasa ma releases a wɔayɛ no awerɛhyɛmu mu a ɛrento deployment mmirikatu ase.
Wobetumi asusu
On-call adesoa a wɔatew so
Anosiabɔ validation tew ogya dum so, na ɛma mfidie ho akuw ani ka adesie ho.
Bere ankasa mu
Ahotɔsoɔ dashboards
Ɔkwan a emu da hɔ a wode hwɛ ahotɔsoɔ akontaa ne validation coverage ma akannifoɔ nkyerɛkyerɛmu.
Faako a yei hyɛ wo stack mu
Zof de anosiabɔ layer a aka ka wo ahotɔsoɔ toolchain a ɛwɔ hɔ ho.
CI/CD
Ka ne GitHub Actions, GitLab CI, Jenkins, ne pipelines afoforo bom de sira releases ho kwan ankasa.
Observability
Fa anosiabɔ validation a ɛba ansa na aduru production ka Datadog, New Relic, ne monitoring nnwinnadeɛ afoforo ho.
Nsɛm nhyehyɛeɛ
Tew nsɛm a ɛkɔ PagerDuty, Opsgenie, ne platforms a ɛte saa mu so denam nsɛm a wonya ntɛm so.
Tikiti nhyehyɛeɛ
Ka ne Jira, Linear, ne nhyehyɛeɛ afoforo bom de yɛ tikiti ankasa bere a validation hwere.
Wɔasiesie ama enterprise na wogye di
Wɔayɛ ama nyiyimu a ɛhia ahobammɔ, mmara di so, ne adwumayɛ a ɛyɛ papa.
Ahobammɔ tebea
SOC 2 Type II, GDPR di so, ne SOC 2 Type II ne GDPR controls.
Access controls ne governance
Role-based access control, audit logs, ne mmara di so nkyerɛkyerɛmu.
Enterprise onboarding
Mmoa a wɔde ahyɛ ho, integrations a wɔayɛ no soronko, ne deployment akwan a wɔayɛ no pɔtee.
Mmoa
24/7 mmoa, SLAs, ne customer success a wɔde ahyɛ ho ma enterprise customers.
Reliability sessions
Preventing outages with continuous validation and pipeline visibility.
1:00Enterprise QAScaling Quality Assurance for Enterprise
How AI-powered testing fleets scale enterprise software validation, expanding coverage, accelerating releases, and preventing production incidents with quality intelligence and release confidence.
Watch session
0:54Reliability OperationsRestoring Engineering Velocity
Explore the hidden reliability bottlenecks slowing engineering teams and how quality intelligence with testing fleets restores velocity through evidence-based validation and governed remediation.
Watch session
Gyae nsɛm a ɛba ansa na ahyɛ ase
Hwɛ sɛnea Zof sira production nsɛm a ɛba ho kwan na ɛbɔ wo revenue, edin, ne mfidie ho mmirikatu ho ban.
Deɛ Ɛfa Ho
Hwehwɛ tumi ne nsiesie a ɛfa ho mu