How We Work: Engineering Delivery, Evaluation & Security Standards
We publish our internal operational playbooks openly. This knowledge base documents the exact 12-step delivery checklist, evaluation metrics, and security controls we enforce across every client sprint.
Our 12-Step Production Delivery Checklist
Every engagement follows this rigorous 12-week progression from architecture spike to full IP handover.
Data Schema & Security Audit
We audit your existing data sources, schemas, and security boundaries. We establish least-privilege API access keys and sign mutual NDAs.
- Data Schema Audit Document
- Security Boundary Architecture Diagram
- Sprint 1 Backlog in Linear
Architecture Spike & Feasibility Verification
We construct a standalone technical spike inside your staging VPC to prove retrieval latency, token consumption, and zero-egress compliance.
- Working Technical Prototype
- Latency Benchmark Report
- Go/No-Go Decision Matrix
Data Pipeline & Semantic Chunking Engine
We implement custom parsers for your PDFs, Confluence, Notion, or SQL databases with layout-aware chunking and metadata tagging.
- Ingestion Pipeline Microservice
- Metadata Tagging Schema
- Automated Incremental Sync Worker
Vector Database Setup & Hybrid Indexing
We deploy pgvector, Qdrant, or Pinecone inside your private cloud with HNSW indices and sparse BM25 lexical keyword search.
- In-VPC Vector Database Cluster
- Hybrid RRF Retrieval Module
- Cross-Encoder Re-ranker Service
Deterministic Guardrails & PII Scrubbing
We wrap all LLM interactions in pre- and post-inference guardrail proxies: Presidio PII scrubbing, toxicity checks, and JSON schema validators.
- Guardrail Proxy Middleware
- PII Redaction Engine
- OWASP Jailbreak Test Suite
API & Frontend Channel Connectors
We build high-speed integrations with your client channels: custom React web widget, Slack Bolt app, Microsoft Teams bot, or Zendesk/HubSpot webhook routers.
- Client UI Component Library
- Webhook Handler Services
- Channel Authentication Vaults
Human-in-the-Loop Handoff & Escalation
We integrate automated sentiment triggers and customer support escalation workflows, passing full conversation context to human queues.
- Human Handoff Webhooks
- Context Packaging Module
- Agent Dashboard Embed
RAGAS Evaluation & Adversarial Fuzzing
We execute an automated 100-query evaluation benchmark measuring faithfulness, answer relevance, context recall, and prompt injection resilience.
- Automated Eval Suite in CI/CD
- Adversarial Fuzzing Report
- Confidence Threshold Tuning Table
Load Testing & High-Concurrency Benchmarking
We simulate 50+ concurrent conversational users to stress-test database connection pools, memory limits, and streaming time-to-first-token (TTFT).
- K6 Concurrency Load Test Scripts
- p95/p99 Latency Profile
- Auto-Scaling Terraform Policy
SOC 2 & GDPR Compliance Review
We verify data retention schedules, KMS encryption keys at rest and in transit, and GDPR Article 17 automated deletion endpoints.
- Compliance Verification Matrix
- Data Processing Agreement Signoff
- KMS Key Rotation Verification
Blue-Green Staging Deployment & Pilot
We deploy to production using blue-green routing with zero downtime, running an internal employee pilot before opening public traffic.
- Production Blue-Green Infrastructure
- Telemetry & Alerting Dashboards
- Internal Pilot Feedback Report
100% IP Transfer & Engineering Runbook Delivery
We transfer all git repository ownership, cloud IAM credentials, and CI/CD pipelines to your internal engineering team with live architecture training.
- Complete Git Repo Handover
- Comprehensive Developer Runbook
- Recorded Architecture Walkthrough
Our Continuous AI Evaluation Framework
We measure reliability with mathematical precision using automated eval pipelines integrated into CI/CD.
| Evaluation Metric | Target Threshold | Measurement Methodology | Architectural Rationale |
|---|---|---|---|
RAG Faithfulness Score Accuracy & Grounding | ≥ 0.95 (95%+ grounded claims) | Using the RAGAS framework, we compare generated statements against the retrieved context chunks using automated LLM-as-judge evaluation. | Guarantees that the chatbot never outputs unsupported statements or fabricates facts outside your private documents. |
Answer Relevance Score Accuracy & Grounding | ≥ 0.90 | Measures whether the generated answer directly resolves the user's specific question without irrelevant rambling. | Prevents confusing, verbose responses that frustrate customers looking for rapid answers. |
Context Precision & Recall Accuracy & Grounding | ≥ 0.88 | Evaluates whether the vector search and re-ranking pipeline retrieved all necessary context chunks while filtering out irrelevant noise. | High precision prevents token waste and 'lost-in-the-middle' context degradation in the LLM context window. |
Prompt Injection Interception Rate Safety & Guardrails | 100% on OWASP LLM01 suite | Automated regression testing against 250+ standard adversarial prompts (direct override, base64 encoding, persona jailbreaks, indirect document injection). | Prevents attackers from exfiltrating system prompts, internal database schemas, or bypassing business logic. |
PII Scrubbing Adherence Safety & Guardrails | 100% detection of SSNs, CCs, and passwords | Microsoft Presidio regex and named entity recognition (NER) scanner runs on all user inputs and model outputs before transmission. | Mandatory for GDPR and HIPAA compliance to prevent customer PII from leaking into logs or downstream models. |
Time-to-First-Token (TTFT) Performance & Latency | Median ≤ 400ms (p99 ≤ 800ms) | Measured from client HTTP connection initialization to the first byte received via Server-Sent Events (SSE). | Immediate conversational feedback keeps user engagement high and mirrors natural dialogue pacing. |
Zero-Trust Security & In-VPC Baseline
Non-negotiable architectural controls enforced in every Master Services Agreement we sign.
Zero-Egress Private Cloud Isolation
Deployments run inside the client's AWS VPC or Azure VNet. Security groups enforce strict 0.0.0.0/0 egress denial with private VPC endpoints for internal cloud communication.
KMS Envelope Encryption at Rest & In Transit
All vector embeddings, database volumes, and cache clusters are encrypted with client-managed KMS customer master keys (CMK) using AES-256. TLS 1.3 enforced for all network transit.
Role-Based Access Control (RBAC) on Chunks
Chunk metadata carries fine-grained tenant and permission tags. Vector queries inject mandatory metadata filters before similarity calculation, preventing cross-tenant or cross-department leakage.
GDPR Article 17 Right-to-Erasure Pipeline
Customer data resides exclusively in mutable database and vector stores, never in frozen model weights. When a deletion request is triggered, all chunks and metadata are purged instantly.
Deterministic Human-in-the-Loop Thresholds
Write operations (payments, CRM updates, ticket closures) require explicit human approval whenever model confidence falls below 0.90.
Pillar Architecture Spec Templates (Printable / PDF)
Standardized specifications you can use internally or print directly for your next architecture review.
AI Chatbot Engineering Specification Template (Printable / PDF Spec)
Our standardized engineering scoping template used to define channels, guardrails, fallback thresholds, and latency requirements before starting chatbot development.
1. Channels & Target Personas
Specify where the chatbot will operate and who it serves.
- •Target channels (Web App, Slack, Teams, WhatsApp, Mobile App)?
- •Primary user persona (End-customer, authenticated B2B client, internal employee)?
- •Expected concurrency (peak queries per second)?
2. Knowledge Sources & Ingestion Boundaries
Define the authoritative documentation the bot is allowed to consult.
- •Document formats (PDFs, Markdown, Confluence, Zendesk Help Center)?
- •Update frequency (real-time, daily batch, weekly sync)?
- •PII sanitization requirements before vectorization?
3. Guardrails & Fallback Policies
Define non-negotiable safety and escalation rules.
- •Minimum similarity score threshold for automated answers (recommended: 0.65)?
- •Standard refusal response when evidence is insufficient?
- •Escalation routing criteria (sentiment degradation, explicit user request)?
4. Infrastructure & Hosting Requirements
Cloud boundaries and compliance constraints.
- •Hosting environment (Client AWS, Azure, GCP, or on-prem)?
- •Foundation model preference (Open-weights in-VPC vs Claude/OpenAI hosted endpoints)?
- •Compliance mandates (SOC 2, HIPAA, GDPR)?
RAG Pipeline Architecture Specification Template (Printable / PDF Spec)
The technical blueprint template used to specify chunking strategies, vector database configurations, re-ranking models, and evaluation criteria for enterprise knowledge search.
1. Corpus Scale & Characteristics
Define the volume, formats, and structure of source data.
- •Total document count and estimated total tokens?
- •Structural complexity (dense tables, financial charts, technical diagrams)?
- •Language requirements (single language vs multilingual corpus)?
2. Indexing & Retrieval Mechanics
Vector indexing parameters and hybrid search strategy.
- •Chunking strategy (semantic paragraph boundary vs recursive character split)?
- •Target chunk size (e.g. 512 tokens with 10% overlap)?
- •Vector database selection (pgvector, Qdrant, Pinecone) and HNSW parameters?
- •Sparse search configuration (BM25 with Reciprocal Rank Fusion)?
3. Re-Ranking & Context Window Engineering
Filtering and prompt assembly rules.
- •Cross-encoder re-ranker model selection (e.g. Cohere Rerank, BGE-Reranker)?
- •Number of candidate chunks fetched (top-K) vs retained for synthesis (top-N)?
- •Citation format and deep-link generation schema?
4. Quality Benchmarks & Continuous Evals
Automated regression testing baselines.
- •Ground truth evaluation dataset size (minimum 100 domain queries)?
- •Minimum acceptable RAGAS faithfulness threshold (target: ≥ 0.95)?
- •Latency budget per query turn (target: sub-600ms)?
Autonomous AI Agent Orchestration Spec Sheet (Printable / PDF Spec)
The orchestration template for scoping stateful multi-step agentic systems, tool schemas, idempotency controls, and human-in-the-loop validation checkpoints.
1. Goal Definition & Task Decomposition
Define the ultimate business objective and required sequential actions.
- •Primary agent goal (e.g., invoice reconciliation, IT onboarding)?
- •Input data payloads (incoming email, webhook, scheduled cron trigger)?
- •Success criteria definition and acceptance testing rules?
2. External Tool Interfaces & API Boundaries
Catalog all external systems the agent is authorized to interact with.
- •Connected APIs (ERP, CRM, payment gateway, messaging)?
- •Authentication patterns (OAuth2, API keys, mTLS)?
- •Read-only vs write permissions per tool?
3. State Management & Error Recovery
Define persistence and retry policies.
- •Persistent state engine (PostgreSQL state graph, Redis queue)?
- •Maximum allowable loop iterations before triggering failure escalation?
- •Idempotency key implementation to prevent duplicate transactional writes?
4. Human Oversight & Auditability
Deterministic checkpoints and regulatory compliance.
- •Financial or operational thresholds requiring explicit human sign-off?
- •Notification channels for pending approval requests (Slack, email)?
- •Immutable audit log retention period for all agent actions?
Enterprise Software Scoping & Sprint Blueprint (Printable / PDF Spec)
The engineering blueprint template used to scope 4-week MVPs, full-stack enterprise web platforms, mobile architectures, and database schemas with 100% client code ownership.
1. Core Architecture & Tech Stack
Establish foundational technologies and deployment targets.
- •Frontend framework (Next.js App Router, React Native for mobile)?
- •Backend runtime (Node.js/TypeScript, Python FastAPI, Go)?
- •Database architecture (PostgreSQL, Redis, ClickHouse)?
2. Non-Functional Requirements & SLAs
Scalability, latency, and uptime requirements.
- •Target concurrency and peak traffic throughput?
- •Target API latency (p95 ≤ 200ms)?
- •Uptime availability SLA (99.9% or 99.95%)?
3. Security & Compliance Architecture
Data protection and enterprise identity standards.
- •Identity provider (Auth0, Clerk, AWS Cognito, Entra ID)?
- •Encryption standards (AES-256 KMS, TLS 1.3)?
- •Compliance targets (SOC 2, HIPAA, GDPR)?
4. Sprint Schedule & Deliverables Handover
Milestones, weekly demo cadence, and code ownership.
- •Sprint 1 architecture spike scope and acceptance criteria?
- •Weekly demo time and shared communication channels (Slack, Linear)?
- •Git repository and CI/CD pipeline handover procedure?
About Aurevion AI
Custom AI systems, private RAG architectures, and sovereign software development for growing engineering teams.
Aurevion AI engineers dependable production artificial intelligence systems, sovereign retrieval-augmented generation pipelines, autonomous agentic workflows, and modern high-concurrency software applications. We partner directly with engineering leaders across North America and Europe, deploying zero-trust private cloud architectures with complete intellectual property ownership, deterministic reliability guarantees, and transparent sprint-based technical team collaboration.