Question 1

What does "Precision Is Not Negotiable" mean in practice?

Accepted Answer

It means we reject the industry habit of deploying probabilistic AI into high-consequence systems without mathematical bounds. Every model output, tool execution, and boundary transition is guarded by deterministic validation, formal state-space verification, and real-time security inspection.

Question 2

What types of companies and industries does Adept partner with?

Accepted Answer

We partner with enterprise technology leaders, high-growth scale-ups, and regulated organizations across healthcare diagnostics, institutional fintech, autonomous aerospace/robotics, and mission-critical enterprise B2B SaaS.

Question 3

How does Adept differ from traditional IT consulting or QA outsourcing firms?

Accepted Answer

Traditional firms test deterministic code using manual testers or simple string assertions. Adept is an elite AI-native engineering firm building software products (Adept Mayar and Kawas), mathematical verification harnesses, and real-time security proxies specifically engineered for non-deterministic AI architectures.

Question 4

Does Adept build AI models from scratch, or work with existing foundation models?

Accepted Answer

We focus on the complete systems engineering, validation, and defensive scaffolding around foundation models (proprietary API models like Claude, Gemini, GPT-4, and open-weights like Llama and Mistral) as well as custom fine-tunes and compound multi-agent architectures.

Question 5

Why does traditional automated testing fail on AI applications?

Accepted Answer

Traditional tests assert binary equality (input X produces string Y). Because AI models produce syntactically diverse outputs across non-zero temperatures, traditional tests either break with false alarms or pass while missing severe semantic hallucinations.

Question 6

What is an LLM-as-judge evaluation pipeline and how is it made reliable?

Accepted Answer

An LLM-as-judge pipeline uses language models to evaluate semantic quality. To eliminate bias and evaluator drift, Adept implements reference-guided anchoring, pairwise positional shuffling, and multi-model consensus voting with statistical confidence bounds.

Question 7

How do you measure Expected Calibration Error (ECE) in production?

Accepted Answer

We partition model prediction confidences into discrete bins and compare average model confidence against empirical accuracy, identifying regions where the model exhibits dangerous overconfidence on inaccurate assertions.

Question 8

Can synthetic test generation replace real-world user data?

Accepted Answer

Synthetic test generation complements real-world data by mathematically probing the long-tail boundary conditions and adversarial permutations that occur too rarely in standard production logs to be caught before release.

Question 9

What is a deterministic finite-state machine (FSM) wrapper?

Accepted Answer

It is an execution layer that surrounds an autonomous AI agent. The AI proposes actions, but the FSM verifies preconditions, enforces schema invariants, and checks transactional permissions before allowing mutations to execute.

Question 10

How do you prevent agentic tool-calling runaway loops or duplicated transactions?

Accepted Answer

We engineer cryptographically signed idempotency tokens, maximum recursion depth limits, and transactional staging boundaries at the API gateway layer.

Question 11

What techniques does Adept use to achieve sub-second response times in compound AI?

Accepted Answer

We deploy hierarchical model routing (sending simple queries to fast, lightweight models), streaming speculative decoding, semantic embedding caches with 40%+ hit rates, and parallelized tool execution.

Question 12

How do you protect systems against foundation model API outages?

Accepted Answer

We architect automated multi-provider circuit breakers that seamlessly reroute traffic to alternate model providers or localized deterministic fallback engines during upstream outages.

Question 13

Why can’t system prompt instructions prevent prompt injection attacks?

Accepted Answer

System prompts and user inputs share the exact same attention context. Language models cannot mathematically isolate control instructions from untrusted data within a unified token stream without external architectural boundaries.

Question 14

What is the difference between direct and indirect prompt injection?

Accepted Answer

Direct prompt injection occurs when a user enters adversarial instructions directly into a prompt. Indirect prompt injection occurs when an AI ingests external data (like PDFs, emails, or web pages) containing hidden payloads that hijack execution during RAG retrieval.

Question 15

How does the Adept Kawas proxy detect zero-day jailbreaks?

Accepted Answer

Kawas evaluates semantic intent and embedding distance anomalies in sub-15ms rather than relying solely on static regex patterns, neutralizing jailbreaks regardless of cipher encoding or multilingual obfuscation.

Question 16

What is covered during an Adept adversarial red-team engagement?

Accepted Answer

We probe your complete attack surface—including prompt boundaries, RAG ingestion pipelines, vector stores, tool-calling interfaces, training data extraction resistance, and cloud infrastructure security across 100k+ attack vectors.

Question 17

How do Adept Mayar and Adept Kawas work together?

Accepted Answer

Adept Mayar operates primarily in the pre-deployment and continuous integration phase (test authoring, regression gating, evaluation), while Adept Kawas operates in real-time production (inline prompt firewall, data exfiltration prevention, RAG sanitization).

Question 18

Can Mayar and Kawas be deployed in air-gapped or on-premises environments?

Accepted Answer

Yes. Both Mayar and Kawas are containerized microservices that can be deployed via Helm charts to air-gapped Kubernetes clusters or private cloud VPCs (AWS, GCP, Azure) without external network dependencies.

Question 19

What is the performance overhead of the Kawas proxy in production?

Accepted Answer

Kawas is written in high-performance Rust and optimized with AVX-512 vector instructions, completing full token entropy inspection and semantic anomaly detection in under 15 milliseconds.

Question 20

Do Mayar or Kawas store our private training data or user prompts?

Accepted Answer

No. Both products support zero data retention modes where prompts and intermediate activations exist only in ephemeral volatile memory and are immediately purged after processing.

Question 21

How do we begin working with Adept?

Accepted Answer

The standard entry point is an initial fixed-scope Assessment. Submit your project requirements through our Contact form; our engineering team reviews your submission and responds within 48 hours to schedule a technical alignment.

Question 22

What is the typical duration and commitment for an Assessment?

Accepted Answer

An Adept Assessment typically spans two to four weeks, delivering comprehensive vulnerability reports, synthetic regression benchmark suites, and an executive compliance roadmap.

Question 23

How does the Residency engagement model work in practice?

Accepted Answer

Our senior AI systems engineers and security architects embed directly inside your daily agile sprints and code reviews, implementing Mayar harnesses and Kawas proxies alongside your core team.

Question 24

Does Adept sign non-disclosure agreements (NDAs) prior to technical review?

Accepted Answer

Yes. We routinely execute mutual NDAs prior to reviewing proprietary system architectures, source code repositories, or confidential datasets.

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