Bucepha Triage™
From AI concept to production.
Bucepha Triage is consulting for any product, AI products included. We help build, test, refine, connect and validate it before it reaches production.
We swap all your API keys at handover, and none of them is ever shown to an LLM. How
An arm of Bucepha LLC, alongside Bucepha Intelligence™.
triage / readiness report
Stage 4 of 6
- Build
- Test
- Refine
- Integrate
- Validate
- Launch
- Tests passing
- 89%
- Error rate
- 0.4%
- p95 latency
- 1.8s
Not ready for production: 3 blockers
- Sign-in fails on Safari after a session expires
- Webhook retries are not idempotent
- No alert when a third-party API times out
Your keys, kept safe
We swap all your keys. None of them ever reaches an LLM.
Testing a product properly means holding its credentials: model APIs, payments, databases, third-party services. Here is exactly what happens to them.
- 01
Submitted securely
Keys reach us through an encrypted channel set up for your engagement. Never by email, chat or a ticket.
- 02
Never shown to an LLM
Keys are stripped from code, logs, prompts and screenshots before any model reads them, ours or anyone else’s.
- 03
Used only where they run
Keys stay in the environment they belong to, scoped to what the test needs, and are never copied into our own systems.
- 04
Swapped at handover
When the work ends we swap every key you shared for a new one and revoke the old, so nothing we saw still works.
Old keys revoked, new keys yours. At the end of every engagement you get a list of each key we held, when it was swapped and confirmation that the old one no longer works.
What Triage does
The operational layer between AI development and production.
A model, an app, an extension or a data pipeline can work in a demo and fail in the real world. Triage is the hands-on work of finding out which, and fixing it. It answers:
- Does it actually work?Test outcomes
- Does it work consistently?Build-over-build regression
- Where does it fail?Defects by cause
- How accurate is the AI?Output accuracy against labeled cases
- What happens in edge cases?Edge-case test suites
- Are false positives or false negatives occurring?Error analysis
- Is the underlying data properly labeled?Annotator agreement
- Are the APIs connected correctly?Contract and dependency tests
- Does it behave correctly across real-world inputs?Success by environment
- What happens when external systems fail?Fault injection
- Is the product ready for production?Readiness by dimension
- What needs to be fixed before launch?Blocker list
Lifecycle
Build, test, refine, integrate, validate, launch.
01
Build
Help turn the initial product or AI concept into a working system.
Capabilities
Product architecture, AI workflow design, Model integration, Feature development, Prototype development, Production architecture planning
Objective: A functional foundation that can actually be tested.
02
Test
Systematically test the product against real-world scenarios.
Capabilities
Functional testing, AI output testing, Edge-case testing, Failure-case testing, Regression testing, Browser testing, App testing, API testing, Workflow testing
Objective: Find the problems before customers do.
03
Refine
Use testing results, analytics and real-world data to improve the system.
Capabilities
AI model refinement, Prompt refinement, Detection accuracy, False-positive analysis, False-negative analysis, Output quality, Workflow optimization, Analytics-driven changes
Objective: Continuous improvement based on observed behavior.
04
Integrate
Connect the product to the systems it needs to operate in the real world.
Capabilities
API connections, Third-party services, AI model APIs, Data providers, Backend systems, Authentication, Webhooks, Production infrastructure
Objective: A complete production environment, not an isolated model.
05
Validate
Validate the entire system end to end before production.
Evaluates
Product functionality, AI performance, API reliability, Data flow, User experience, Performance, Security considerations, Failure scenarios, Edge cases, Real-world inputs
Objective: Decide whether it is ready to leave development.
06
Launch
Prepare the product for its final production release.
Capabilities
Production configuration, Deployment preparation, Monitoring, Final QA, Launch testing, Production API configuration, Release validation
Objective: A product ready to operate in the real world.
Analytics
What a Triage report shows.
One example: a web application, its API and its browser extension, tested across 1,240 cases. The figures are illustrative, not a client result.
Test outcomes
What passed, what failed, and what cannot be trusted
- Passed
- 1,108
- Failed
- 74
- Flaky
- 38
- Blocked
- 20
| Surface | Pass rate | Failed | Flaky |
|---|---|---|---|
| Web app | 90% | 31 | 14 |
| API | 94% | 18 | 6 |
| Browser extension | 83% | 25 | 18 |
Real-world conditions
Task success by environment
- Chrome, desktop99%
- Edge, desktop98%
- Safari, desktop94%
- Chrome, Android93%
- Safari, iPhone88%
- Slow network82%
- Expired session71%
Launch threshold 95% Below threshold
Defects
Which failures to fix first
- 1 Auth and session28%
- 2 API timeouts20%
- 3 Unhandled input16%
- 4 UI state14%
- 5 Data validation11%
- 6 Third-party failures7%
- 7 Other4%
Validation
Is it ready? Six answers, not one
- Functionality88
- Performance81
- Reliability76
- Integrations90
- User experience85
- Security70
Services
Use the ones you need.
AI Model Refinement
Evaluate AI outputs and find where models, prompts, workflows or validation systems need improvement.
Quality & Testing
Test products across real-world conditions to find bugs, edge cases, inconsistent behavior, failures and quality issues.
Data Labeling
Prepare and structure high-quality datasets for AI development, evaluation and refinement.
AI Analytics
Analyze model and product performance to find patterns, weaknesses, failure modes and opportunities for improvement.
API & System Integration
Connect AI models, external APIs, data providers, backend systems, authentication and third-party services into production architectures.
Production Readiness
Evaluate whether an AI product is ready to move from development into a production environment.
Product Launch Support
Support final testing, validation, deployment, monitoring and launch.
Modular by design
An engagement uses the services it needs. A team with a working model may only need labeling and evaluation; a team near launch may only need readiness and launch support.
Data labeling
Better AI starts with better data.
Datasets built for a specific model and a specific evaluation, reviewed by a second annotator, and measured before anything trains on them. Labeling is one step in the loop below, not data entry.
Image labeling · Object detection annotation · Classification · Segmentation · Attribute labeling · Automotive damage labeling · Dataset preparation · Quality control · Annotation review · Evaluation datasets · Human-in-the-loop validation
- Real-world data
- Data labeling
- Testing
- AI evaluation
- Model refinement
- Validation
- Production
Why Triage exists
AI products need more than a model.
A prototype meets a chosen demo set. Production meets different inputs, unusual cases, poor data, real traffic, API failures and unexpected users. Triage exposes those failures first.
Prototype vs production
Task success, from the demo set to the real world
- Different inputs81%
- Unusual cases67%
- Poor-quality data62%
- Production traffic74%
- API failures55%
- Unexpected user behavior70%
- Dataset gaps64%
Demo set, 96% Under real exposure
Triage levels
Choose your level of Triage.
Every Triage engagement is billed at $200/hour. The levels below give estimated project pricing based on the scope and duration of the engagement.
Triage Level 1
Test & Launch
Initial testing
- Duration
- 7 days
- Estimate
- $4,000
- Billing
- $200/hour
Bug testing
Systematically test the product to find:
- Functional bugs
- UI issues
- Workflow failures
- Broken user flows
- Edge cases
- Regression issues
- Browser issues
- Application issues
Quality testing
Whether the product behaves consistently under expected real-world use.
- Core workflows
- User interactions
- AI outputs
- Common failure cases
- Basic edge cases
Beta testing
Controlled beta-style testing before production.
- Real-world workflows
- User-facing issues
- Unexpected behavior
- Product reliability
- Feedback from test usage
API testing
Whether the APIs the product depends on work correctly.
- API requests
- API responses
- Authentication
- Error handling
- Data flow
- Integration failures
- External service dependencies
Production launch preparation
Preparing the product for its initial production release.
- Final QA
- Production configuration
- Deployment checks
- Launch validation
- Basic monitoring
- Production smoke testing
Objective: Find the major problems, validate the core product, and determine whether it is ready to move toward production.
Start Triage Level 1Triage Level 2
AI Optimization
Deeper AI, data and optimization work
- Duration
- 30 days
- Estimate
- $16,000
- Billing
- $200/hour
Everything in Level 1
The full Level 1 engagement, run first.
- Bug testing
- Quality testing
- Beta testing
- API testing
- Production testing
- Launch preparation
Data labeling
The datasets AI evaluation and optimization depend on, built as data infrastructure.
- Image labeling
- Object detection
- Classification
- Segmentation
- Attribute labeling
- Automotive damage annotation
- Dataset cleaning
- Dataset preparation
- Annotation QA
- Evaluation datasets
- Human review
AI optimization
Measure the model against the labeled data, then improve what the measurements show.
- AI model refinement
- Prompt refinement
- False-positive analysis
- False-negative analysis
- Accuracy by input condition
- Regression test sets
- AI analytics
Objective: A model measured on data that represents the real world, and improved where it fails.
Start Triage Level 2Triage Level 3
Production & Security
Advanced optimization, security and production validation
- Duration
- Scoped
- Estimate
- Scoped
- Billing
- $200/hour
Everything in Level 2
Level 1 and Level 2 in full, run first.
- Testing and launch preparation
- Data labeling
- AI optimization
Security
The ways the system can be misused or exposed, tested before it is live.
- Security review
- Authentication and access control
- Secrets and data handling
- Input validation
- Dependency review
Compliance readiness
Whether the system can show what it did with data, and why.
- Data retention
- Audit trails
- Privacy review
- Documentation for review
Production validation
The whole system, end to end, under the conditions it will actually meet.
- Load and performance testing
- Fault injection
- Monitoring and alerting
- Incident runbooks
- Release validation
Objective: A system that is secure, observable and validated end to end before it carries real traffic.
Scope Triage Level 3
Estimates, not fixed quotes. The final figure follows the scope agreed at the start and the time the work actually takes, at the same hourly rate for every level.