Featured case study
AI Broadcast Radio Automation: From Prompt to Live Radio Broadcast
One text prompt in. Broadcast-ready audio out written, voiced and delivered on air, hands-free.
Read the case study
AI Testing, Evaluation and Red Teaming Services
Independent testing that shows whether your AI gives correct, safe answers and resists misuse.
Stack we work in
- GitHub Actions
- Python
- Claude
- Google Gemini
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs your AI needs testing
AI systems fail differently from normal software: they can give confident wrong answers, leak data, be talked into ignoring their rules or behave differently after a model update.
Read the full overview
We test chatbots, RAG applications, copilots and agents the way real users and attackers would. We build evaluation sets from your own cases and score accuracy, grounding and tone with Ragas, DeepEval and Promptfoo, run red-team attacks such as prompt injection and jailbreaks against the OWASP Top 10 for LLM Applications, and set up continuous evaluation and tracing in Langfuse so every change is checked before it reaches users.
Nobody knows the accuracy
The assistant seems fine in demos, but there is no score for real questions.
Model updates break things
A new model or prompt version quietly changes answers users relied on.
Agents with real permissions
AI can now send emails, update records or run code, and nobody has tried to misuse it.
Security review blocking launch
The security team wants evidence the system resists prompt injection and data leakage.
Customer and regulator questions
Buyers and auditors ask how the AI was tested, and there is nothing to show them.
Outcomes
What you get
- A measured accuracy score instead of a feeling
- Security weaknesses found before attackers or customers find them
- Every prompt, model or data change checked automatically
- Evidence for customers, auditors and AI governance
- Confidence to switch to a cheaper or newer model
- Test sets and pipelines owned by you
What we deliver
What you receive
Test plan and threat model
Systems, risks, attack scenarios and scoring methods, agreed up front.
Evaluation datasets
Real cases with expected answers, versioned and reusable.
Quality scorecard
Accuracy, grounding, safety and bias scores with examples of failures.
Red-team report
Findings mapped to the OWASP LLM Top 10, with severity and reproduction steps.
Fixes and guardrails
Recommended or implemented fixes, with retest results.
Continuous evaluation pipeline
Test suites in CI and tracing in production, handed over with documentation.
Systems we test
Customer chatbots
Answer accuracy, tone, refusals and data leakage in customer-facing assistants.
Learn moreRAG and knowledge assistants
Grounding, citations and permission boundaries across your documents.
Learn moreAI agents
Tool misuse, excessive agency and indirect prompt injection in agents that take actions.
Learn moreCopilots in your product
Multi-tenant data isolation and safe actions in in-app assistants.
Fine-tuned models
Quality against the base model and safety regressions after tuning.
Vendor AI tools
Behavior of bought AI features before you roll them out to staff or customers.
Quality evaluation
Measure whether answers are right.
- Evaluation datasets built from real questions, tickets and documents
- RAG metrics: faithfulness, answer relevance, context precision and recall with Ragas
- Task success and correctness scoring with DeepEval and LLM-as-judge, checked against human review
- Tone, format and policy compliance checks
- Model comparison to choose between GPT, Claude, Gemini and open models
Built with
- Ragas
- DeepEval
- Claude
Red teaming and security testing
Attack the system before someone else does.
- Prompt injection, direct and indirect through documents, emails and web pages
- Jailbreak and policy bypass attempts
- Sensitive data and system prompt leakage tests
- Agent and tool misuse: excessive agency, unsafe actions and privilege escalation
- Automated attack generation with Promptfoo, garak and PyRIT, plus manual expert testing
Built with
- Promptfoo
- garak
- PyRIT
Safety, bias and robustness
Check behavior across users and edge cases.
- Harmful content and toxicity checks
- Bias and fairness tests across user groups and languages
- Robustness to typos, rephrasing and unexpected input
- Hallucination rate on questions the system should refuse
Continuous evaluation
Catch regressions on every change.
- Evaluation suites in CI with GitHub Actions or GitLab CI
- Release gates: deploys blocked when scores drop
- Tracing and online evaluation in Langfuse, LangSmith or Arize Phoenix
- Re-testing after model, prompt or data updates
Built with
- Langfuse
- LangSmith
- Arize Phoenix
- GitHub Actions
- GitLab CI
Fixes and guardrails
Turn findings into protection.
- Prioritized findings with severity and reproduction steps
- Prompt, retrieval and tool permission fixes
- Guardrails with NeMo Guardrails, Llama Guard, Lakera Guard or cloud content filters
- Re-test to confirm fixes and evidence for governance records
Built with
- NeMo Guardrails
- Llama Guard
- Lakera Guard
- Llama
Why teams choose Equitysoft for AI testing and red teaming
Tests built from your real traffic
Evaluation sets come from your real questions, tickets and documents, so scores reflect what users actually do.
Automated and manual attacks
Promptfoo, garak and PyRIT generate attacks at scale, and our engineers add the manual, context-specific ones tools miss.
Findings you can act on
Each finding has a severity, reproduction steps and a proposed fix, and we can apply the prompt, retrieval and permission fixes ourselves.
Tests stay in your pipeline
Evaluation suites run in GitHub Actions or GitLab CI with release gates, so a regression blocks a deploy rather than reaching users.
Mapped to OWASP and MITRE ATLAS
Results are mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS, giving you evidence for governance and customer reviews.
Recent work from our team
How we work
Scope
We agree the systems, risks, user groups and threat scenarios to test.
You receive Test plan and threat model
Build test sets
We collect real cases and expected answers with your experts, and write attack scenarios.
You receive Evaluation and attack datasets
Evaluate
We run automated and manual quality tests and score the results.
You receive Quality scorecard
Red team
We attack the system with automated tools and manual techniques across the OWASP LLM Top 10.
You receive Security findings
Fix and retest
We help fix issues, add guardrails and retest to confirm.
You receive Retest report
Automate
We wire the test suites into CI and production monitoring so testing continues.
You receive Continuous evaluation pipeline
Tech stack
Technologies we use
AI and machine learning
Languages
Cloud and DevOps
Testing and quality
Other tools
Engagement
Ways to work with us
One-off assessment
Evaluation and red teaming of one AI application, with a findings report.
- Fixed scope and price
- Retest included
Continuous evaluation package
Test suites, release gates and production monitoring set up in your pipeline.
- Runs on every change
- Owned by your team
Ongoing red teaming
Regular manual and automated attacks as your AI systems and threats change.
- Quarterly or per release
- Trend reporting
Questions, answered
It depends on the number of AI systems, how many tools and data sources they touch, the depth of manual red teaming, and whether you want a one-off assessment or continuous testing. As a guide, a one-off evaluation and red-team assessment of one application usually costs $8,000 to $25,000, and a continuous evaluation pipeline with release gates and monitoring $25,000 to $60,000 or more (USD).
AI output varies between runs and there is rarely one correct string to compare against. Testing needs sets of realistic cases, scoring methods for meaning rather than exact text, and deliberate attacks, run again every time the model, prompt or data changes.
Deliberately trying to make an AI system misbehave: reveal data, ignore its rules, produce harmful content or take unsafe actions. We use automated attack tools and manual techniques to find these weaknesses before real users or attackers do.
A widely used list of the most important security risks for LLM-based applications, including prompt injection, sensitive information disclosure, supply chain risks, excessive agency, system prompt leakage and misinformation. We use it as a checklist for every assessment.
An attack where instructions hidden in user input, a document, an email or a web page make the model ignore its original instructions. It is the most common way AI agents and RAG systems are attacked, and it needs layered defences rather than one fix.
Yes, within the vendor's terms of use. We test the behavior through the interface or API you have access to and report findings you can raise with the vendor.
Ragas, DeepEval and Promptfoo for evaluation, Promptfoo, garak and PyRIT for automated red teaming, and Langfuse, LangSmith or Arize Phoenix for tracing and online evaluation, combined with manual expert testing.
On every change to the model, prompt, retrieval data or tools, which is why we automate the test suites in CI. A deeper manual red-team review once or twice a year, or before major releases, is a good baseline.
Yes, if you want us to. As a development company we can fix prompts, retrieval, permissions and guardrails, then retest. Or we hand findings to your team with clear reproduction steps.
A one-off evaluation and red-team assessment of one application usually takes 2 to 4 weeks. Setting up a continuous evaluation pipeline typically takes 3 to 6 weeks.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. We need access to the system under test, a sample of real queries or tickets, and the policies it should follow. Findings and test data are covered by NDA and stay in your environment.
Not sure how your AI holds up?
Book a call. We will review your AI system and propose an evaluation and red-team plan.