What we test, and the evidence we leave behind.

Salesforce sits at the centre of how many enterprises sell, serve, and report. Around it grows a web of custom objects, flows, permission sets, and integrations that shifts with every release, and each change can quietly alter who sees what, whether a record is correct, and whether a business process still completes.

A clean deployment is not the same as a working business process.

We validate the journeys that actually matter: business workflows, automation and flows, permission and sharing rules, data integrity, and the integrations that feed and consume Salesforce. We test what the configuration does for a real user, not just that it deployed without error.

Because Salesforce changes often, we automate regression across core journeys and release impact, so a change in one area does not silently break another, and every release ships with evidence of what still works.

What we test What you get

Confidence that every release protects the workflows, permissions, and data your teams rely on, with regression evidence to prove it.

Discuss Salesforce & Custom Application Assurance →

Your most critical customer journeys depend on connections most people never see: APIs, MuleSoft flows, data mappings, security policies and downstream systems working together without failure. When those connections break, the impact is immediate. Customers experience delays. Teams lose confidence. Leaders are forced into release decisions without enough evidence. TestHub finds integration risk before production finds it for you.

The cheapest defect is the one that never reaches the code.

We take a contract-first approach, validating the API contract for completeness, consistency, and conflict before a single line of code is written. We validate every status code, success path, error path, and edge condition the endpoint can produce, so the consumer is never surprised by a response the contract did not declare.

Once the contract is trusted, we test the MuleSoft implementation against it: behaviour, transformations, policies, error handling, routing, and runtime responses. We confirm that data flows correctly from system to system, end to end, and we automate regression so downstream systems do not break silently when upstream services change.

What we test What you get

Integration defects surfaced at design time, and automated regression that catches breakage before a customer ever sees it.

Discuss API & integration assurance →

Data is the quiet dependency every enterprise decision rests on. It feeds the dashboards leaders read, the reports auditors review, the customer profiles service teams use, and the AI agents that increasingly reason over all of it.

When a pipeline fails silently, the failure does not announce itself.

It surfaces weeks later, in a number that does not reconcile, a decision made on stale data, or a customer record that no longer matches reality. We test the sources by profiling data before it enters the pipeline. We test the transformations: business rules, type handling, null handling, deduplication, and enrichment. We test the loads by confirming row counts, key integrity, and reconciliation against source.

We test the orchestration too: scheduling, dependency chains, retry logic, and failure recovery. Salesforce data movement through MuleSoft is one of the most common patterns we assure, covering both the pipeline side and what the inbound records then do once they land inside Salesforce.

What we test What you get

Confidence that the data behind every report and decision is accurate, complete, and reconciled to source.

Discuss data pipeline assurance →

As enterprises adopt AI and Agentforce, the question is no longer whether the agent can respond. The real question is whether the response can be trusted, repeated, explained, and safely used by customers, employees, and the business.

AI agents can sound confident even when they are wrong.

A response can appear credible but not be supported by the approved source of truth. In an enterprise environment, that is not just a technical issue: it can create customer confusion, operational risk, compliance exposure, and loss of confidence in the technology.

TestHub tests the agent's accuracy, grounding, guardrails, escalation behaviour, and permitted actions. We check whether the agent stays within its approved knowledge base, refuses what it should refuse, and escalates when it cannot answer safely. The goal is simple: give leaders the evidence they need to deploy AI with confidence. Not just because the agent works, but because it can be trusted.

What we test What you get

Documented assurance that an AI agent stays grounded, refuses what it should, and is safe to put in front of customers.

Discuss AI trust engineering →

Systems rarely fail in the demo. They fail at month-end, on launch day, or when a dependency slows down, the moments when the business can least afford it. Load is not an edge case. It is the real world arriving all at once.

It works at your desk. The question is whether it holds at peak.

We model realistic load against APIs and end-to-end journeys: sustained traffic, spikes, soak tests, and stress to breaking point. We measure response times, throughput, error rates, and resource behaviour, and we find the bottleneck before your customers do.

We also test resilience: how the system behaves when a dependency is slow or unavailable, whether it degrades gracefully or fails hard, and how it recovers. The output is a clear picture of capacity, limits, and the headroom you actually have.

What we test What you get

A clear view of how much load the system holds, where it breaks, and the headroom you have before peak demand finds the limit for you.

Discuss performance & resilience testing →

Most enterprise breaches do not start with something exotic. They start with a missing authorisation check, an over-permissive endpoint, or input nobody validated, the ordinary gaps the OWASP API Security Top 10 was written to catch.

An API that works for the right user can still be wide open to the wrong one.

We test APIs against the OWASP API Security Top 10: broken object and function level authorisation, broken authentication, excessive data exposure, injection, and security misconfiguration. We probe the paths an attacker would take, not just the happy path a functional test covers.

Security is validated continuously inside delivery, not bolted on before go-live, so issues surface while they are cheap to fix and every release ships with documented evidence of what was checked.

What we test What you get

Documented assurance that your APIs are tested against the OWASP API Top 10, with the access-control and authentication gaps closed before release.

Discuss API security testing →

Robotic process automation quietly runs work that used to need a person: moving data between systems, processing transactions, reconciling records. When a bot drifts, it does not raise its hand. It keeps going, at scale, making the same mistake thousands of times before anyone notices.

An unattended bot fails quietly, and keeps going.

We test MuleSoft RPA end to end: triggers and schedules, decision logic, screen and system interactions, the data the bot reads and writes, and the exception paths that decide what happens when something is not as expected.

Because bots depend on the systems around them, we test how the automation behaves when an underlying screen, field, or API changes, so a routine update upstream does not silently break the automation downstream.

What we test What you get

Confidence that unattended automation does the right thing on every run, and fails safely and visibly when the systems around it change.

Discuss MuleSoft RPA testing →

Most enterprise release decisions still rely on old rituals: a meeting, a status deck, a manually updated spreadsheet, a sign-off form. The approval is captured, but the evidence behind it is often scattered across pipelines, test tools, defect trackers, dashboards, and emails.

TestHub builds release readiness directly into the CI/CD pipeline.

We embed automated quality gates so the right checks happen when code moves between stages, not days later in a review meeting. Every commit, pull request, build, and deployment generates evidence in real time.

This creates a live view of release readiness: which requirements are covered by tests, which tests were triggered by a change, which passed or failed, which build introduced the change, and what evidence supports the decision. The trail is not assembled after the fact. It is generated by the delivery process itself.

What we test What you get

A live, defensible go or no-go signal on every release, generated by the delivery process itself.

Discuss the Continuous Trust Pipeline →