Autonomous Quality Engineering, powered by Agentic AI.

🤖 AI agents test & analyse

Planning, generation, self-healing, triage, prediction and release-risk agents work in parallel across web, API, mobile, data, cloud and enterprise platforms — doing the analysis and volume work no human team should spend its talent on.

⚙️ Automation delivers at scale

Playwright, Appium, WebdriverIO, Python and cloud pipelines execute the approved work on every change — fast, repeatable and evidenced in the dashboard, from a single app to a global estate.

✓ Humans decide & govern

Human-in-the-loop is structural, not decorative: people set goals and risk appetite, approve agent plans and gates, monitor everything live, and can override any recommendation — with every decision audited.

Agentic AI & Autonomous QE

Multi-agent systems that plan test scope, generate cases, execute, self-heal broken scripts, triage failures and predict defects — governed by your QE lead, never silent.

• Agent orchestration & governance
• Autonomous regression & selective testing
• Open-source + commercial AI tooling

Web & API Automation

Robust automation for web applications, REST / GraphQL APIs and microservices, built for CI/CD speed.

• Playwright, Selenium, WebdriverIO
• API contract & integration testing
• Python & TypeScript frameworks

Mobile & React Native

Native, hybrid and React Native testing across real devices and cloud device farms.

• Appium & WebdriverIO
• React Native end-to-end flows
• Device-farm & CI integration

Test Data Engineering

Test data automation and engineering for complex data estates — the bottleneck most teams ignore until it blocks a release.

• Synthetic & masked data pipelines
• Data provisioning automation
• Database validation & analytics

Data, Batch & Analytics QE

Quality for databases, ETL, batch jobs and reporting — reconciliations, data analytics checks and Python-based validation at scale.

• Batch job testing with Python
• ETL / data-warehouse validation
• Complex data solution testing

Cloud QE — AWS · Azure · GCP

Cloud-native quality engineering: ephemeral test environments, pipeline quality gates and cost-aware execution on all three major clouds.

• AWS, Microsoft Azure, Google Cloud
• GitHub Actions & CI/CD pipelines
• Quality gates & release governance

SAP & Salesforce Automation

Automation solutioning for SAP and Salesforce programmes — upgrades, migrations and regression at enterprise scale.

• SAP functional & regression automation
• Salesforce end-to-end testing
• Migration & upgrade assurance

Mainframe Engineering Automation

Modern automation for mainframe estates — batch, online and integration testing brought into the same dashboard as your digital apps.

• Mainframe batch & job testing
• Data comparison & reconciliation
• Legacy-to-cloud migration QE

Non-Functional & Accessibility

Performance, security and accessibility (ADA / WCAG) testing alongside functional delivery — not as an afterthought.

• Performance & load engineering
• Accessibility (ADA) testing
• Security testing support

Batch Jobs — Testing, Automation & Engineering with Python

Batch jobs move the business while nobody is watching — billing runs, payment cycles, claims feeds, metering, reconciliations and end-of-day processing. We test and automate them as first-class engineering: job schedules and dependencies, job chains and sequencing, input and output files, restart and recovery after failure, reruns and back-out, and performance at production volumes. Our batch job engineering with Python builds reusable harnesses that trigger jobs, prepare input data, wait for completion, and then validate outputs automatically — database records, files and reports — so a batch failure is found by the pipeline, with evidence, not by a customer or an operations call in the morning.

• Batch job testing with Python — automated triggers, checks & evidence
• Job dependency, scheduling & failure-recovery testing
• High-volume & peak-run validation (billing, enrolment, end-of-day)

ETL Testing & Automation — Extract, Transform, Load

ETL — Extract, Transform, Load — is where data is most often quietly corrupted. Our ETL testing and automation validates the full journey: extraction completeness from source systems, every transformation rule and business mapping, data cleansing and deduplication, and loading into data warehouses, data marts and reporting platforms. Automated source-to-target validation compares counts, totals and record-level values before and after each stage, checks referential integrity and history handling, and tests what happens with late, missing or malformed data — so errors are caught at the stage that caused them, not discovered weeks later in a report.

• ETL / data-warehouse validation — source-to-target, stage by stage
• Transformation-rule, mapping & data-quality checks
• Incremental loads, history (SCD) & reject-handling testing

Data Validation, Reconciliation & Analytics QE

Data validation is the discipline underneath both batch and ETL quality. Using Python and SQL, we automate database validation, file-to-database and database-to-report reconciliations, data analytics checks on calculations, aggregations and dashboards, and complex data solution testing across pipelines, warehouses and reporting layers. Validation runs inside your CI-CD pipelines and batch schedules — not as a manual spot-check — and every reconciliation result is logged in the QE dashboard, giving business and audit stakeholders evidence they can trust for BFSI, healthcare and energy & utilities data.

• Automated data validation — Python & SQL, at scale
• Reconciliations — counts, sums, balances & record-level detail
• Complex data solution testing — pipelines, warehouses & reporting

Test Data Provisioning & Environment Management

Data quality means little if the data and the environment arrive late. Our customized agentic AI solution provisions test data on demand — synthetic, subsetted and masked — and provisions, configures and switches the testing environments it runs in, Dev, QA, UAT and Prod, through a Python framework inside your automation toolchain. Every promotion moves as a pull request, with a human approving each gate. See the end-to-end environment management flow in the Workflow section — and the dedicated Test Data Management for QE page for discovery, assessment and design of the full test data solution.

• Agent-driven test data provisioning — synthetic, masked, refreshed, versioned
• End-to-end testing infrastructure & environment management with AI agents
• PR-gated environment switching — Dev → QA → UAT → Prod, human-approved

Automated provisioning, on demand

The headline capability: test data provisioned automatically — self-service for engineers, scheduled for cycles, or triggered by the pipeline — in minutes instead of ticket queues that take days. Every request delivers a known, versioned data set matched to the tests that will run.

• On-demand, scheduled & pipeline-triggered provisioning
• Provisioning wired into CI-CD and batch schedules
• Versioned data sets — recreate any run exactly

Synthetic data generation by AI LLM agents

Where production data cannot be used — or the case simply does not exist yet — LLM agents generate realistic synthetic data from the discovered data model and business rules: valid combinations, negative and boundary cases, rare edge cases and high-volume sets, without exposing a single real customer record.

• Rule- and model-driven synthetic generation
• Edge, negative & boundary data on purpose
• High-volume data for performance & batch testing

Subsetting & masking, privacy by design

Production-like data, safely: intelligent subsetting extracts a small, referentially complete slice of production-shaped data, and masking / anonymisation replaces sensitive values — names, account numbers, health and payment data — consistently across every connected system, so relationships still work but people are protected.

• Referentially intact subsets, not random extracts
• Consistent masking across systems & files
• BFSI, healthcare & utilities privacy rules honoured

Referential integrity, end to end

Data that breaks its own relationships tests nothing. Provisioned data keeps keys, links and history consistent across databases, APIs, files, warehouses and downstream applications — including ETL and batch inputs and outputs — and the Validation Agent proves it before the cycle starts.

• Cross-system & cross-database consistency
• History, ageing & date-sensitive data handled
• Integrity checks as a data-readiness gate

Discovery, assessment & design — before provisioning

The solution is engineered, not improvised. Discovery maps your real data estate; assessment scores quality, coverage and privacy risk honestly; design decides — data type by data type — what to synthesise, subset, mask, reserve or refresh. That design is the blueprint every agent then executes.

• Data discovery & profiling of the full estate
• Test data assessment & readiness report
• Test data solution design, approved by humans

Refresh, reset, reservation & self-service

Managed daily, not rebuilt per project: refresh and reset return an environment to a known-good data state between cycles; reservation lets parallel teams and pipelines hold the data they need without overwriting each other; and a self-service catalogue lets authorised users find and provision approved data sets themselves.

• Refresh & reset to a known-good state
• Data reservation for parallel teams
• Self-service catalogue with role-based access

Traced to requirements & tests

Every data set knows what it is for: provisioned data is linked to the requirements, BDD scenarios, test cases and environments it serves — the same traceability as the rest of our QE — so a data-caused failure can be followed from failing test to data version to source rule, and coverage gaps show as missing data, visibly.

• Data set → test → requirement traceability
• Data versions recorded with every test run
• Missing-data gaps flagged by agents

Governed, audited & access-controlled

Authentication and authorisation control who can see, request and provision which data; sensitive provisioning carries human approval; and every generation, masking, delivery, refresh and deletion is logged in the QE dashboard as an audit trail your compliance teams can inspect.

• Role-based access & approval for sensitive data
• Full provisioning audit trail
• Retention, ageing & clean-up policies enforced

Run by AI LLM agents, checked by people

Specialist LLM agents do the volume work — discovery, profiling, assessment, design drafting, generation, masking, provisioning, validation and monitoring — orchestrated as one team and fitted to your security needs with Claude, GitHub Copilot or open-source Llama, structured with LangChain and LangGraph, and driving Python tooling to do the actual data work. Humans approve the design, the masking policy and every sensitive promotion.

• LLM agents: discovery, design, provisioning, validation
• Python data tooling agents execute through
• HITL approval at the sensitive gates

What the discovery, assessment & design engagement delivers

The first engagement on this page is deliberately small and concrete — you finish it with artefacts, not opinions:

• Test data discovery report — applications, sources, models, relationships, volumes and sensitive data mapped
• Test data assessment — quality, freshness, scenario coverage, privacy risk and the gaps blocking your cycles, scored honestly
• Test data solution design — the target provisioning architecture: synthetic / subset / mask strategy per data domain, generation rules, refresh and versioning model, access and approval model, and pipeline integration
• Provisioning pilot — automated provisioning working for one application or data domain, measured against the assessment baseline

Who this page is for

QE and programme teams whose cycles stall on data: regression that waits for a shared database refresh, UAT blocked because the one useful account is already used, defects that cannot be recreated because the data is gone, or compliance reviews that stop production copies outright. It fits BFSI policy, payment and claims data; healthcare patient and provider data under strict privacy; energy & utilities metering, tariff and billing data — and any estate where batch, ETL and database testing depends on data being right before the first test runs.

Related: Data, Batch & Analytics QE · End-to-End Environment Management · Test Data Engineering (Services)

BFSI — Banking, Financial Services & Insurance

Banking, payments, retirement services, life insurance, investment management and digital channels. High-volume transactions, batch cycles, reconciliations and audit-ready evidence.

Healthcare

Clinical, claims, provider and patient-facing systems where data accuracy and privacy are non-negotiable. Interoperability, EHR workflows and compliant test data.

Energy & Utilities

Metering, billing, grid and field-service systems with heavy batch processing, complex tariffs and large data volumes — plus customer digital channels.

QE Datacentre Engineering Transformation

Many estates still depend on data centres and hybrid infrastructure that modern cloud-only testing overlooks. We transform quality engineering for datacentre and hybrid estates: automated environment provisioning and validation, infrastructure-aware test design, batch and systems testing at operational scale, and migration assurance when workloads move between data centre and cloud. Our agents monitor environment health and configuration drift, flag risks before a test cycle begins, and keep evidence for every change — so legacy and modern platforms are governed by the same quality discipline, in the same dashboard.

Security Engineering

Security is engineered, not inspected in at the end. We embed security quality into delivery: security test design alongside functional design, automated checks in the pipeline, vulnerability-focused regression, and agent-assisted triage that groups security findings by real business risk instead of raw alert volume. Working with your security teams and standards, we help you prove — with tracked, auditable evidence — that the controls you depend on are tested as rigorously as the features your customers see.

Performance Engineering

Performance is a feature your users feel every day. Our performance engineering covers the full journey: early performance design and budgets, load and stress testing for web, API, mobile and batch workloads, and continuous performance checks in the pipeline so regressions are caught in days, not discovered in production. Agents analyse results against baselines, highlight the transactions and data patterns that drive degradation, and turn performance from a pre-release scramble into a monitored, managed quality — including peak events such as billing runs, enrolment windows and seasonal demand.

🧠 Deep reasoning, not just pattern matching

When something fails, surface matching is not enough. Our agents reason across requirements, code changes, logs, traces, history and telemetry together — weighing competing causes, stating their confidence, and showing the evidence behind the conclusion — so triage, rerun and correction decisions are explainable, not guesswork.

◉ Observability & telemetry, built in

Quality you cannot observe is quality you cannot govern. Agent actions, test runs, pipeline events, heals and predictions are all emitted as telemetry and correlated in the QE dashboard — giving live observability of the quality system itself, not just a report after the fact.

↺ Self-correction that compounds

Each cycle teaches the system: healed scripts, corrected data, confirmed root causes and human overrides refine the traceability map, the test design suite and the selective-test models. Cycle by cycle, execution gets faster, reruns get narrower and predictions get sharper — while humans keep approval and override at all times.

👆 What one touch authors

Everything a tester would otherwise write by hand, produced as one pack: BDD scenarios, functional and non-functional test cases, negative and boundary coverage, test data definitions, expected results and priorities — plus the executable Playwright & Python automation behind them, ready for the pipeline.

✓ The human stays the author of intent

AI does the drafting; people keep the authorship that matters. Business intent, risk judgement and final approval stay with your QE engineers and product owners — every authored item is reviewable, editable and evidenced before it counts as a trusted test.

⚡ From days of writing to minutes of review

Test authoring typically consumes the largest share of a test cycle. One-touch authoring compresses it to a single starting action and a focused review — freeing senior engineers for exploratory testing, risk analysis and release judgement, and letting a small team cover a far larger estate.

🧩 Customized, not off-the-shelf

Your data models, masking rules, infrastructure and approval chain are not generic, so the solution is not either. The agents, the Python framework and the promotion workflow are designed and configured for your estate — your cloud or data centre, your CI platform, your Jira / Azure Boards tracking, and your compliance rules for BFSI, healthcare or energy & utilities data.

🐍 Python framework as the control plane

One Python framework gives the agents hands: APIs and CLIs to provision data, build environments, switch configuration between Dev, QA, UAT and Prod, run readiness checks and report status. It lives in your Git repositories alongside your Playwright & Python automation — versioned, reviewed and owned by your team, not locked inside a vendor tool.

✓ Every PR movement is a human decision

Promotion by pull request means promotion with a paper trail: the diff, the evidence and the approver are all recorded, and the same PR discipline your developers already trust governs the environments. Agents can prepare, check and recommend — only a human approval at the gate moves anything toward production.

Manage · Monitor · Govern

Every agent action is logged in the QE dashboard: what it planned, ran, healed or predicted — with evidence. No silent changes to your data or releases.

Why it cuts cycle time

Agents work in parallel across planning, data, execution and triage, and selective testing runs only what a change actually risks — instead of the full suite, every time.

Small team, bigger output

A small team of AI-assisted QE engineers, trained and led by our consultants, can cover regression, data and non-functional testing that used to need a much larger manual team — lowering total cost of ownership.

✓ Check with the AI Assistant — before you book

Use the assistant above to check each of these in seconds. Every answer is matched to a real service on this page, so you arrive at the free review already knowing where you stand.

• ✓ Service fit — which service solves my exact quality problem?
• ✓ Domain coverage — do you cover BFSI, healthcare, or energy & utilities flows like mine?
• ✓ Tools & stack — Playwright, Python, Appium, cloud, SAP, Salesforce or mainframe on my estate?
• ✓ Native & multi-agentic AI — how would agents plan, generate, heal and predict for us?
• ✓ SDLC & STLC roadmap — where does quality start, and what happens at each stage?
• ✓ Dashboard & prediction — what would the predictive QE dashboard show for my programme?
• ✓ POC & pricing approach — how does a 2–4 week POC work, and how is an engagement scoped?
• ✓ Training & team model — can a small team run this, and can you train ours?
• ✓ Toolchain & CI-CD — can you integrate our Git, GitHub Actions or Azure DevOps pipeline, feature to regression?
• ✓ Automation delivery — BDD test cases generated automatically from our Jira requirements, AI-generated Playwright & Python scripts, AI validation of those scripts, and self-healing when the app changes?
• ✓ Execution loop & failure reporting — automated execution with AI-based looping, AI understanding of defect and code failures in the pipeline, reruns of AI-selected tests only, a comprehensive report, and an automated failure report with evidence — logs, video and screenshots — to Jira or our sprint board?
• ✓ One-Touch journey — from app discovery and requirement traceability mapping, through the test design suite, script creation and test execution, to AI failure understanding, selective reruns, self-correction and learning with deep reasoning, and observability & telemetry — delivered to us as one one-touch solution?
• ✓ One-Touch test authoring — complete BDD scenarios, test cases, test data and Playwright & Python scripts authored from a requirement, journey or application area in one touch, reviewed and approved by a human on one screen, traced to Jira and ready to run — and re-authored when the requirement changes?
• ✓ Environments & test data — a customized agentic AI solution that provisions test data (synthetic, masked, refreshed, versioned) and manages your testing infrastructure and environments end to end, with a Python framework driving environment switching from Dev to QA to UAT to Prod — and a human approving every PR promotion gate before anything moves?
• ✓ Test Data Management — the separate Test Data Management for QE page: discovery of your applications and data, assessment of quality, coverage and privacy risk, design of the test data solution, then automated provisioning — synthetic, subsetted, masked, versioned — validation, refresh and governance, all run by AI LLM agents?
• ✓ Traceability & AI-Led Roadmap — Jira / Xray, Zephyr or Azure Boards traceability, and the phased path from human validation to full automation?

✉ Send your query by email

Prefer email, or want a person to pick your query up? Send it directly to Rajesh Kumar’s team at k2027.rajesh@gmail.com. Include your application, your domain and the quality problem you are facing, and we will reply with the right service, a POV session or a scoped POC proposal.

You can also ask the assistant first: after every answer it offers an “Email this query” link that opens your email app with your question and the assistant’s answer already filled in, addressed to the same inbox — nothing is sent until you press send.

Email: k2027.rajesh@gmail.com · Principal Consultant: Rajesh Kumar · Bangalore, India & New Jersey, USA · Online consultancy worldwide.

Predictive, not just reporting

Defect prediction, pass-rate forecasting and failure-reason analysis — so you act before the cycle, not after it.

Selective testing

Risk- and dependency-scored test selection cuts regression time while protecting the flows that matter.

Quality gates & SLA

QA → UAT → Prod gates, SLA tracking and migration readiness in the same central view.

Built for SMBs too

Small and mid-sized firms get the same centralised, autonomous QE — sized and priced for a lean team.

Automation & execution

Self-healing locators, parallel execution patterns and CI-ready framework starters for web, API and mobile.

Agentic AI & AI tooling

Agent templates for planning, generation, data creation, triage and prediction — with human approval and full audit logs.

Cloud, DevOps & pipelines

Pipeline quality gates, ephemeral environments and device / browser farms wired into your existing DevOps.

Enterprise, data & management

Enterprise-platform regression packs, batch & database validators, and Jira / Xray-connected reporting into the QE dashboard.

Points of View (POVs)

• Agentic AI in QE — where agents help, where humans must stay
• Selective testing vs full regression
• Test data as a product, not a ticket queue
• Quality gates for cloud & mainframe together
• QE for SAP / Salesforce upgrade programmes

Proofs of Concept (POCs)

• 2–4 week POC: one application, one agent workflow, real data
• Playwright / Appium framework starter on your app
• Dashboard pilot on your project data
• Test-data automation spike for one domain feed

Metrics we agree & track

• Cycle time & regression duration
• Automation coverage & flakiness rate
• Defect escape rate & defect prediction accuracy
• Pass rate, gate compliance & SLA adherence
• Cost of quality / cost per release

All visible in the QE dashboard — the same numbers for your team and ours.

Online Consultancy

Quick expert reviews and guidance from senior QE consultants — architecture reviews, framework audits, release-readiness checks — delivered online, worldwide.

Managed Autonomous QE

We run QE as a service: agents + engineers, governed from the dashboard, with weekly reporting against the agreed metrics.

Training & Enablement

Hands-on training for your engineers in AI-assisted QE, Playwright, Appium, API and data testing — so capability stays with your team.

✓ Human approves

No agent plan goes live silently. A QE lead or quality owner approves scope, selective-test choices, release gates and predictions before they take effect — at every step, not just at the end.

◉ Human monitors

Every agent action is streamed to the QE dashboard: what it recommended, what it ran, what changed and what it predicted — with evidence a human can inspect at any moment.

⚖ Transparent by design

Agents show their reasoning, inputs and confidence. If a recommendation looks wrong, the human overrides it — and that override becomes training signal for the next cycle.

Ideation & innovation, online

Regular online ideation sessions where we prototype new agent workflows and QE ideas with your team — small experiments, measured honestly.

Why clients choose us

Domain depth in regulated industries, genuine agentic-AI engineering (not slideware), and a dashboard that makes quality visible to everyone.

Who we fit best

Small and mid-sized firms modernising QE, and enterprise programmes that need specialist QE solutioning for complex estates.

Native AI — built in, not bolted on

AI is designed into the engineering process from the first requirement: agents sense change, analyse impact and recommend action at every stage, while proven engineering practice — version control, code review, CI/CD gates and audit trails — keeps every agent action transparent, testable and reversible.

• AI-first design for every QE workflow
• Every agent action logged and evidenced
• Human-in-the-loop approval at each gate

Multi-agentic AI — one team of specialists

No single chatbot can engineer quality. Our multi-agent system divides the work the way a strong QE team does — an orchestrator plans and coordinates, specialist agents generate, execute, heal, provision data and predict — working in parallel, sharing one context and one dashboard, under human governance.

• Orchestrator coordinating specialist agents
• Parallel working across the whole estate
• One ranked plan, approved by people

Playwright & Python — tool-driven solutioning

Agents are only as good as the tools they drive. Playwright delivers resilient, cross-browser web and API automation that runs on every commit; Python powers test data engineering, database and ETL validation, batch job testing and agent tooling — an open, portable stack your own team can own and extend.

• Playwright — web, API & end-to-end automation
• Python — data, batch, analytics & agent tools
• Open-source first, commercial where it fits

Requirements & Test Planning

SDLC: business needs become clear, testable requirements. STLC: scope, risks, entry criteria and the quality strategy are planned. Analysis agents flag ambiguity and untestable statements; planning agents draft the risk-based test plan.

✓ Human check — product & QE approve scope and acceptance criteria

Design & Test Design

SDLC: architecture and integration design. STLC: test cases, coverage models, data needs and environments are designed from that architecture. Design agents map every element to risk, so coverage follows the design — not habit.

✓ Human check — architect & QE lead approve design and coverage

Development & Test Development

SDLC: features are built. STLC: the automation itself is developed — Playwright suites for web and API, Python frameworks for data and batch, Appium for mobile — reviewed and versioned like production code, running in CI on every commit.

✓ Human check — engineers review agent-generated code and tests

Testing & Test Execution

SDLC: the integrated system is verified. STLC: functional, regression, API, data, performance, security and accessibility tests execute — largely autonomously. Generation and self-healing agents maintain the suites; prediction agents select the tests each change actually needs.

✓ Human check — QE lead approves selective-test scope and defect risk

Deployment & Test Closure

SDLC: the release is promoted through QA, UAT and production behind quality gates. STLC: closure reporting summarises coverage, defects, prediction accuracy and residual risk — the honest evidence behind the go / no-go decision.

✓ Human check — the release owner makes the go / no-go call

Maintenance & Continuous Quality

SDLC: the live product is operated and improved. STLC: quality continues — production defects, user behaviour and performance signals flow back into the dashboard, sharpening the agents’ predictions and the next cycle’s requirements.

✓ Human check — teams review outcomes and set the next priorities

Quality process first — then aligned processes

We establish the quality process before wiring any tools: entry and exit criteria, quality gates, defect and evidence standards, and who approves what. We then align the surrounding processes to it, so nothing works against quality:

• Quality engineering process — planning, design, execution and closure, risk-based throughout
• Development process — shift-left checks, code review and testability built into delivery
• Build management — versioned, reproducible builds with clear promotion rules
• CI process — fast feedback on every commit, with gates that stop bad builds early

AI-enabled application assessments

AI agents and consultants assess your applications functionally and non-functionally, so solution design is based on evidence about your real estate — not assumptions:

• Functional assessment — coverage, critical business flows, integration points and regression health
• Non-functional assessment — performance, security, accessibility (ADA) and resilience
• Technology landscape — languages, frameworks, platforms, data stores and third-party services in use
• Ecosystem & domain integration — how applications, APIs, batch and enterprise platforms connect across BFSI, healthcare and energy & utilities domains

TDD-BDD driven automation, across applications

Automation is created the disciplined way. With Test-Driven Development (TDD), tests define expected behaviour before and alongside the code. With Behaviour-Driven Development (BDD), scenarios are written in plain business language — Given / When / Then — that product owners, developers and QE engineers all share, and those same scenarios become the executable automated tests. One specification, understood by the business and run by the pipeline, with no translation gap in between.

• Automated BDD test case generation from Jira — AI generates Gherkin scenarios automatically from the requirements and user stories on your Jira requirement board, each one traced back to its Jira requirement
• AI code & script generation, validated with AI — approved scenarios become Playwright & Python scripts, validated by AI (standards, dry runs, assertion & coverage checks) before they join the trusted pack
• Self-healing delivery — broken locators and steps heal automatically when the application changes, with every heal logged and reviewable
• AI-based looping execution & automated failure reporting — in the pipeline, AI understands each defect and code failure from logs, stack traces and code diffs, then reruns only the selected affected tests in a governed loop (exiting on a pass, an attempt limit, or escalation to a human), and raises an automated failure report with evidence — logs, video and screenshots — to Jira or your sprint board, alongside the comprehensive run report
• One-touch journey, discovery to learning — the same pipeline carries the full journey as one solution: app discovery, requirement traceability mapping, the test design suite, script creation and test execution, then self-correction and learning with deep reasoning, watched end to end through observability and telemetry AI
• One-touch test authoring — one starting touch on a Jira requirement, a user journey or an application area authors the complete test pack in one pass: BDD scenarios, test cases, test data and validated Playwright & Python scripts, presented on one review screen for human approval, published traced to its requirement and ready to run in the pipeline — and re-authored in the same one touch when requirements change
• Customized test data & environment agents — a customized agentic AI solution provisions test data on demand (synthetic, subsetted, masked, versioned) and manages testing infrastructure and environments end to end; a Python framework drives environment switching from Dev to QA to UAT to Prod, and every promotion moves as a pull request with a human approving each gate — see End-to-End Environment Management in the Workflow section
• BDD scenarios traced directly to business requirements
• Playwright & Python automation for web, API, data and batch
• Appium / WebdriverIO for mobile & React Native

A comprehensive pipeline — feature to regression

We build one comprehensive automated pipeline that follows your branching model end to end: fast unit, API and BDD checks on the feature branch; integration and smoke suites on merge; and the full, risk-selected regression suite on the regression / release branch — with quality gates at every promotion. The service is tailored to your requirements and to the CI platforms you already run:

AI agents watch every pipeline run, correlate failures to the change that caused them, and recommend the selective tests that cover the most risk — so pipelines stay fast as they grow.

AI-driven discovery, test design & business traceability

AI-driven discovery scans your applications, requirements, user stories and existing tests to build a real picture of what needs testing. AI then supports test requirement analysis and test design — drafting cases, data needs and coverage from that evidence — and integrates everything into your end-to-end systems with full business traceability:

• Jira with Xray or Zephyr, or Azure DevOps Boards — requirements, tests, defects and releases linked both ways
• Every business requirement traced to its BDD scenarios, automated tests, results and defects — and back again
• Gaps are visible instantly: a requirement with no test, or a test with no requirement, is flagged by the agents

LLM services & tools that drive it

The discovery, analysis and traceability work is powered by AI LLM services, chosen to fit your security and licensing needs — commercial or open source — orchestrated as agents rather than one-off prompts:

LangChain and LangGraph structure the multi-agent workflows; Playwright and Python are the tools the agents drive to execute, validate and reconcile — so automated business traceability is produced by the pipeline itself, not maintained by hand in spreadsheets.

Training, Tools, Technology & Assessment

The initial phase builds the foundation honestly. We assess your applications, current test assets, pipelines, skills and quality maturity; evaluate the tools and technology — existing and candidate — against your estate; and train your engineers and QE leads in AI-led working: directing agents with effective prompts, interpreting agent output critically, and governing automation. Nobody is asked to trust a system they do not understand.

✓ Human check — leaders approve the assessment findings, tool choices and training plan

Comprehensive Roadmap & Solution Design

Next we build the comprehensive roadmap and solution design together: the target architecture of agents, prompts and skills; the toolchain and CI-CD / CI-CT integration design; traceability and test-management design; the order in which applications and domains are onboarded; and the metrics that will prove value at each stage. The roadmap is sequenced so every phase delivers something usable — not a big-bang promise at the end.

✓ Human check — stakeholders approve the roadmap, design, priorities and success metrics

Implementation

Then implementation: agent workflows and their prompt libraries are built for discovery, test requirement analysis, test design, TDD-BDD automation and reporting; skills-based frameworks are implemented in Playwright and Python; authentication, authorisation and centralised test management are configured; and business traceability is wired into Jira / Xray, Zephyr or Azure DevOps Boards. Everything is built on your real applications and reviewed like production engineering — because it is.

✓ Human check — engineers and QE leads review and approve generated tests, prompts and configurations

CI-CT Integration & Test Case Onboarding

Next, integration into the CI-CT pipeline — continuous integration joined with continuous testing. Existing test cases and business use cases are onboarded systematically: inventoried, prioritised by risk, converted or wrapped as TDD-BDD automation where needed, tagged for traceability, and scheduled into the right pipeline stage — feature-branch checks, merge gates or full regression — so they run seamlessly in the pipeline on every change, with results flowing back to test management and the dashboard automatically.

✓ Human check — QE lead approves quality gates and which suites are trusted in the pipeline

Validation — a human interprets & approves at every step

Throughout implementation and early operation runs the validation phase: a human interprets and approves at every step. Agent test designs are read before use; generated automation is reviewed before it joins the trusted pack; selective-test recommendations are approved before anything is skipped; predictions are compared against actual outcomes. Approvals, corrections and overrides are all recorded — they are the evidence base that decides what may be automated next, and they continuously improve the prompts and skills themselves.

✓ Human check — interpretation and approval at every step, with a full audit trail

Sustainability & Continuous Monitoring with AI

Finally, sustainability: the solution is operated as a living system, not a finished project. AI continuously monitors pipeline health, coverage, flakiness, defect patterns, prediction accuracy and release risk across your business products and applications, heals routine breakage, and raises prioritised recommendations. Validated workflows now run as a fully automated engineering solution inside their guardrails — while humans govern by exception, audit regularly, and can tighten or revoke any autonomy at any time.

✓ Human check — governance by exception: monitor, audit, intervene when judgement is needed

How the four “led” approaches fit together

• AI-led — AI sets the analytical agenda: what changed, what is risky, what should be tested and what can wait.
• Agent-led — specialist agents carry out the work end to end: discovery, design, generation, execution, healing, triage and reporting.
• Prompt-led — work is directed through governed, versioned prompt libraries, so agent behaviour is consistent, reviewable and improvable — not dependent on who happened to ask.
• Skills-based — capability is organised as defined QE skills (test design, data engineering, performance, traceability, release risk…) held by agents and people together, so gaps are visible and trainable.

From validation to full automation — autonomy that is earned

Stage A — Validated: agents recommend, humans decide everything. Stage B — Supervised automation: proven, low-risk workflows run automatically; humans approve gates, scope and exceptions. Stage C — Fully automated engineering: validated workflows run end to end in the CI-CT pipeline under continuous AI monitoring, with humans governing by exception and holding override at all times. A workflow only advances a stage when its measured accuracy — predictions vs outcomes, approvals vs overrides — justifies it, and it can step back just as easily.