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TESTPERFORMANCE

Enterprise system performance & automation specialists

You build the systems. We do the uncool stuff so you don't have to.

We help banks, asset managers, payment processors, and growing engineering teams prove their critical systems can handle tomorrow's volume—before production finds out the hard way.

Pragmatic load testing, synthetic Day-Zero test data, and lean end-to-end automation without bloated frameworks.

Let's talk it through Read our technical blog

01What we do

Does any of this sound familiar?

A major release, migration, or capacity spike is looming, and nobody is quite sure where the system will buckle—or performance testing has fallen onto someone's desk as a side-of-desk chore.

Enterprise performance testing

  • Challenge arbitrary SLA numbers and distil real user-story performance targets
  • Load, stress, spike, and soak testing using JMeter, Gatling, Locust.io, and ORQA
  • Deep root-cause analysis using Python (NumPy/Pandas) and targeted DB/network telemetry
  • Clear remediation proposals verified by re-testing

Core banking, asset management & paymentsFixed scope or on-demand

Your team has spent months building a sprawling test automation framework to cover every edge case, yet maintenance eats up the sprint and confidence is still low.

Pragmatic test automation

  • Strict ROI triage: only automate tests repeated hundreds or thousands of times
  • Reusable, composable building blocks instead of over-engineered frameworks
  • End-to-end functional automation with Selenium, Playwright, and ORQA
  • Centralised results database for granular or executive reporting

Financial services & enterprise platformsArchitecture & delivery

You need to test 1-year and 5-year projected transaction volumes—or verify an End-of-Day batch optimisation—but you don't have realistic production data or a way to reconcile millions of rows.

Synthetic data & reconciliation

  • Day-Zero synthetic data generation from statistical models (Plaitpy YAML & Synner)
  • Date-dependent financial transaction synthesis without exposing production PII
  • Automated before/after data reconciliation using Python (datacompy) and Datascade
  • Key-matched tolerance checks with instant break drill-downs

Day-Zero volumes & EOD batch proofTooling & execution

You want an independent, senior assessment of whether your systems, tools, and team are genuinely ready for high-stakes performance testing.

Performance readiness assessment

  • Comprehensive capability evaluation & gap analysis across processes and environments
  • Best-practices review of non-functional requirements and test methodology
  • Tools & observability audit (Dynatrace, open-source load generators, CI integration)
  • Team skills and capacity roadmap with concrete, prioritised recommendations

5-pillar diagnostic reviewShort fixed-fee engagement

Need a senior pair of hands for custom Dynatrace apps, MCP integrations, or a tricky bottleneck? Get in touch.

02How we work

What working together looks like

  1. Question & gather the real requirements

    Targets are often arbitrary until someone asks why. We work with your business and engineering leads to turn vague SLAs into clear, prioritised user stories (e.g. returning search results in under 1 second for 1,000 concurrent users so customers don't abandon a transaction).

  2. Build the harness & synthesise the data

    Getting realistic test data—especially date-dependent financial transactions—is usually the hardest part of capacity testing. We generate accurate Day-Zero or extrapolated datasets and build clean, maintainable load scenarios in Locust.io, Gatling, JMeter, or ORQA.

  3. Execute, correlate & pinpoint the bottleneck

    All test telemetry lands in a local analytical store. When an issue surfaces, we use Python (NumPy + Pandas), Datascade, and targeted resource monitoring (DB, queues, network, Dynatrace DQL) to isolate the true constraint—which is rarely where internal conventional wisdom expects it to be.

  4. Solve & repeat on demand

    We propose concrete architectural or configuration fixes, verify the improvement, and leave you with a repeatable pack that can run in CI/CD or on standby whenever capacity changes.

Case Study · London Payment Processor

Finding the real bottleneck in weeks—on time and on budget

For a London payment processing company with no formal SLAs in place, we gathered stakeholder requirements, built a heavily customised Locust.io load & spike framework alongside a proprietary Python telemetry pipeline, and uncovered the true system bottlenecks within weeks. We proposed and implemented the fixes, delivering a step-change in throughput. Read the full case study →

03Readiness

Are you ready for performance testing your systems?

  1. Comprehensive capability evaluation An honest assessment of your current systems, environments, and readiness to run meaningful performance tests before you commit major budget.
  2. Gap analysis Pinpointing deficiencies across your testing practices, data preparation, and environment parity so nothing blindsides you mid-project.
  3. Best practices review Ensuring your non-functional requirements, workload models, and concurrency assumptions reflect how real users actually behave.
  4. Tools & technology audit Evaluating whether your current load generators, APM (such as Dynatrace), and data reconciliation tools are fit for purpose—or redundant.
  5. Resource & skills assessment Identifying skill gaps or capacity constraints inside your team so performance testing doesn't remain an afterthought.

04Insights & Blog

Notes from the trenches

View all articles (4+) + Write article

AI & Architecture · 2026-09-07

Broker Agent – MCP Server Proof of Concept

Experimenting with MCP servers and multi-account investment data integration via a Claude Code agent—exploring the architecture, trade-offs, and privacy implications of chat-as-the-frontend.

4 min read Read article →

Observability · 2024-10-28

Dynatrace Custom Apps: Correlating Logs Across Distributed Layers

How to build a custom React app on Dynatrace AppEngine using DQL queries to trace transactions across decoupled booking and processing systems.

3 min read Read article →

Test Data · 2024-03-26

Synthetic Data Generation – Full Report

Executive summary comparing Day-Zero synthetic data generation approaches using Plaitpy and Synner for financial services capacity testing.

2 min read Read article →

Test Data · 2024-03-11

Synthetic Data Generation – Part 2: Visual Distribution Modelling with Synner

Using Synner to visually model and generate complex conditional transaction distributions for 1-year and 5-year FX futures volume testing.

3 min read Read article →

If you're wrestling with any of this, let's talk.

Tell us a bit about your system, timelines, or bottleneck. Your message goes straight to our local database—no third-party trackers or marketing bloat.

info@testperformance.co.uk Chislehurst, Kent & London GitHub