Enterprise AI Quality Consulting

Your AI shouldn't
go live until it's
been broken.

Taseti LLC helps enterprises deploy AI they can trust. We test it, pressure it, and sign off on it — before it touches a customer, a patient, or a contract.

crucible — run_all.py
$ crucible run --dataset loan_advisor.csv \
  --provider anthropic --rubric financial
 
─────────────────────────
[ 1/7 ] dataset registry  — no drift detected
[ 2/7 ] agent runner     — 120 tests
  ✓ pass rate: 97.5%  tokens: 48,320
[ 3/7 ] rubric scorer    — financial rubric
  ✓ avg score: 4.3/5  compliance: 4.6
[ 4/7 ] gate checker     — evaluating...
  ✓ all 6 gates passed
[ 5/7 ] api validator    — 8 endpoints
  ✓ p95: 280ms  SLA: met
[ 6/7 ] agentic harness  — 3 scenarios
  ✓ 3/3 journeys passed
[ 7/7 ] defect reporter  — 0 failures
 
✓  RELEASE GATE PASSED — exit 0

Most enterprise AI
ships before it's ready.

Enterprises move fast. Vendors promise capability. Procurement signs off. And then an AI model meets a real customer — and something goes wrong that nobody tested for.

68%
of enterprise AI deployments experience at least one significant quality failure within the first six months of production use.
$4.4M
average cost of an AI-related data or compliance incident — before reputational damage is factored in.
12%
of enterprises have formal AI quality testing processes in place before deployment. The rest rely on observation after go-live.
01

Hallucination in production

AI models confidently fabricate facts. Without systematic testing, the first time you know is when a customer acts on bad information.

02

Prompt injection vulnerabilities

Adversarial users exploit AI chat interfaces to bypass safety filters, extract system prompts, and manipulate outputs.

03

Regulatory exposure

In banking and healthcare, an AI that gives wrong advice isn't just a UX problem — it's a compliance failure with real legal consequences.

04

No deployment gate

Most teams have no formal quality threshold before an AI model ships. Every deployment is a judgement call, not an evidence-based decision.

05

Silent drift

Model updates, prompt changes, and data shifts degrade AI quality over time. Without continuous testing, nobody notices until customers do.


Every module.
One pipeline.
Zero surprises.

Crucible runs every AI output through a multi-stage testing pipeline — functional correctness, hallucination detection, adversarial probing, API contract validation, and browser-level UI testing — then produces signed evidence records your governance team can stand behind.

agent_runnerDataset-driven test loop — CSV or JSON in, structured results out
gate_checkerConfigurable quality gate — blocks deployment on threshold breach
rubric_scorer1–5 dimensional scoring across accuracy, compliance, safety
agentic_harnessMulti-turn conversation and tool-calling scenario testing
api_validatorHTTP contract tests — status codes, schemas, latency SLAs
ui_testerPlaywright-driven browser testing against deployed AI products
defect_reporterFailures filed as GitHub Issues or Jira tickets automatically
◆ Crucible by Taseti

Built for the
cases that matter most.

120 pre-built test scenarios across functional correctness, robustness, hallucination detection, safety, security, business logic, hallucination traps, and prompt injection. Ready to run against any AI provider from day one.

120
Pre-built test scenarios
8
Testing categories
3
AI providers supported
0
Dependencies to install*

* beyond pip install requests pyyaml

Request a Demo →

One command.
Seven stages.

Run the full pipeline with a single CLI command. Every stage is independently configurable — unused stages are skipped silently, never blocking the run.

01
Dataset Registry
Drift detection — warns if test data changed since last run
02
Agent Runner
Executes every test case — pass/fail per prompt
03
Rubric Scorer
Adds 1–5 dimensional scores across key quality axes
04
Gate Checker
Checks thresholds, writes signed evidence record
05
API Validator
HTTP contract tests, schema checks, latency SLAs
06
Agentic Harness
Multi-turn journey scenarios, tool mocking, HITL
07
Defect Reporter
Failures filed as tickets in GitHub or Jira

What we
actually do.

Taseti engagements are hands-on and evidence-driven. We embed with your team, run Crucible against your AI, and leave you with a repeatable testing process that lives in your CI pipeline.

01 — Assessment
AI Quality Assessment
A structured evaluation of your AI product's current quality posture — before it causes a problem.
  • Full Crucible run against your AI system
  • Hallucination and injection vulnerability report
  • Rubric scoring across safety, accuracy, compliance
  • Executive-ready findings summary
02 — Implementation
Testing Pipeline Build
We install and configure Crucible in your environment and wire it into your deployment pipeline.
  • Custom test dataset design for your use case
  • Gate threshold configuration and calibration
  • CI/CD integration — GitHub Actions or equivalent
  • Team onboarding and handover documentation
03 — Ongoing
Continuous QA Retainer
Retained AI QA capability — your team gets expert support without hiring a full-time specialist.
  • Monthly Crucible runs and trend analysis
  • New scenario design as your AI evolves
  • Drift monitoring and regression alerting
  • Regulatory readiness support on request

Built for
high-stakes AI.

Crucible is designed for the industries where AI failures aren't just embarrassing — they're consequential. Our testing scenarios map to the regulatory frameworks your team already operates under.

🏢
Financial Services
AI advisors, credit decision systems, fraud detection, and customer-facing chat that touches regulated products.
FCA CONCPRA SS1/23MiFID IIBasel III
🏥
Healthcare
Clinical triage assistants, patient-facing AI, diagnostic support tools, and administrative automation.
HIPAANHS DCB0129CQC StandardsMDR 2017
📋
Insurance
Underwriting AI, claims automation, customer advisory tools, and risk assessment systems.
Solvency IIIFRS 17FCA ICOBSLloyd's
🏛
Public Sector
Citizen-facing AI services, government automation, and public sector digital transformation.
FedRAMPFISMASection 508WCAG 2.2
💻
Enterprise SaaS
AI copilots, code assistants, and embedded AI features where hallucination or data leakage destroys customer trust.
Feature hallucinationCross-tenant leakagePrompt injection
🛒
Retail & eCommerce
AI recommendation engines, customer support bots, and product advisors where wrong answers cause returns, complaints, and churn.
Policy accuracyPricing integrityProduct safety
👥
HR & Recruiting Tech
AI screening and assessment tools where bias or inconsistency creates litigation exposure and reputational damage.
Bias detectionEEOC consistencyADA compliance
Legal & Professional Svcs
AI legal research and drafting tools where fabricated citations and incorrect advice create malpractice and sanctions risk.
Citation integrityJurisdiction accuracyAdvice boundaries

Try it now.
No booking required.

Type a prompt below. Crucible sends it to a real AI, validates the response live, and scores it against our financial services rubric — right here in your browser.

What you're testing
A mock AI financial advisor. Try asking it about loans, debt, financial difficulty, or investment advice. Watch Crucible catch what's wrong.
Rubric active
accuracy compliance risk_disclosure policy usefulness
AI mode:
Compliant mode: AI follows FCA guidelines
Try:
USER PROMPT

Let's talk about
what you're
shipping.

Tell us about your AI deployment — where it sits, what it touches, and what keeps you up at night. We'll tell you honestly whether Crucible can help.

Emailhello@tasetitech.com
Based inUnited States — working globally
Response timeWithin one business day