Technical Documentation

1. Platform & Technology Stack

What platforms do you build on?

We mainly use workflow orchestrated automation platforms combined with API-first integrations for flexibility and maintainability:

Primary Automation Platforms

� Make.com: Visual workflow orchestration, SOC 2 Type II certified.
� n8n: Open-source, self-hostable workflow automation.

AI/LLM Providers

� OpenAI API (GPT): General-purpose
� Anthropic Claude (Opus/Sonnet): Complex reasoning
� Google Gemini : Multimodal processing
� Perplexity: Research

Why These Platforms?

Model-agnostic architecture: Not locked into a single AI provider
Vendor independence: You own workflows; we don't gate-keep access
Enterprise-grade security: SOC 2, GDPR, ISO 27001 compliance
Scalability: Handle 1,000 or 100,000 operations/month

When We Use Custom Code

While platforms handle orchestration, we deploy custom code for:

� Agentic environments with self-annealing capabilities
� Complex data transformations beyond platform limits
� Custom ML models or fine-tuned LLMs
� Highly sensitive data requiring air-gapped processing

Integrations Support

We integrate with 1,000+ business tools via pre-built connectors and custom APIs:

CRM & Sales

Salesforce HubSpot Pipedrive Close Zoho CRM

Project Management

ClickUp Monday.com Asana Notion Airtable

Communication

Slack Microsoft Teams Gmail Outlook Twilio

Don't see your tool? If it has an API, we can integrate it. We also support PostgreSQL, MySQL, MongoDB, and custom GraphQL/REST implementations.

2. AI Systems & Architecture

1. Retrieval-Augmented Generation (RAG)

Instead of relying solely on the AI's training data, we connect it to your knowledge base (documents, databases, CRM, etc.) to improve accuracy.

Result: 85-95% reduction in hallucinations compared to zero-shot prompting.

2. Human-in-the-Loop (HITL) Workflows

For high-stakes decisions (e.g., contract analysis), we implement human review checkpoints.

When HITL is Required

Outbound sales emails
Legal document generation
Financial calculations

When HITL is Optional

Internal data entry
Categorization/tagging
Research summaries

3. Hallucination Detection & Monitoring

We implement continuous monitoring to ensure system integrity:

Fact-checking pipelines Cross-reference outputs against source data automatically.
Consistency checks Comparing multiple AI responses to the same query for variance.

4. Error Handling Hierarchy

Automatic retry: Retry with exponential backoff for transient errors (API timeouts).
Fallback models: Switch to Anthropic if OpenAI fails.
Human escalation: Alerts for critical errors for manual intervention.
Graceful degradation: Reduced functionality instead of crash.

5. Customization Levers

Prompt Engineering: Fine-tuning tone, length, and constraints.

Tone (Professional, casual, empathetic, technical, ...)
Length (Concise (50 words) vs. detailed (500 words), ...)
Format (Bullet points, paragraphs, tables, JSON, ...)
Constraints ("Never mention competitors", "Always include pricing", ...)

Few-Shot Examples: Providing 3-5 input/output pairs to guide behavior.
Fine-Tuning: Training on examples for domain specificity.

Standard Operating Procedures

Workflow Reliability Framework

Technical standards and methodologies applied to automation workflows to ensure operational uptime and logic integrity.

Category	Technical Standard	Methodology	Operational Value
1. Triggers	Idempotency Logic	Assignment of unique digital fingerprints to every data point.	Prevents duplicate actions, such as double-emailing leads or processing repeat invoices.
1. Triggers	Smart Filtering	Implementation of pre-defined business criteria to qualify triggers.	Minimizes operational costs by ensuring AI resources are only spent on high-value signals.
2. Context	Data Sanitation	Automated removal of HTML tags, whitespace, and formatting noise.	Eliminates "Garbage In, Garbage Out" risks, ensuring high-quality model inputs.
2. Context	Context Optimization	Condensation of large datasets into high-impact summaries.	Prevents model "forgetting" and keeps the AI focused on mission-critical information.
3. Prompting	System Persona	Hard-coding of professional protocols and behavioral constraints.	Guarantees brand consistency and ensures compliance with industry standards.
	Few-Shot Learning	Embedding of curated "Input vs. Gold Standard Output" mapping.	Trains the model on specific corporate styles through example rather than instruction.
	Chain of Thought	Requirement for step-by-step logical analysis before outputting.	Drastically reduces hallucinations by forcing the AI to verify internal reasoning.
4. Validation	Structured JSON	Mandating AI responses be delivered in machine-readable code.	Enables seamless data integration between the AI and core business systems (CRM, ERP).
4. Validation	Negative Constraints	Explicit programming of "forbidden" actions and topics.	Provides a safety layer that prevents the AI from discussing competitors or sensitive data.
5. HITL	Confidence Thresholds	Routing of low-confidence AI outputs to a human review queue.	Ensures high-stakes tasks remain human-verified while automating routine operations.
5. HITL	Review Interface	Integration of custom Slack/Web dashboards for rapid approval.	Streamlines oversight, allowing team members to manage workloads with a single click.
6. Resilience	Auto-Retry	Development of "Self-Healing" loops for API timeouts.	Maintains 24/7 system uptime and prevents workflow breakage during outages.
6. Resilience	Traceability Logs	Recording of every AI "thought process" in a secure database.	Provides a full audit trail for accountability, troubleshooting, and optimization.

3. Quality Assurance & Safety

QA Process

Phase 1

Unit Testing

Validate individual workflow steps and API connections. Check error handling and retry logic. Confirm data transformations are accurate

Phase 2

Integration Testing

End-to-end runs with test data and edge cases (empty inputs, malformed data, API failures, etc.). Measure latency and performance.

Phase 3

Prompt Validation

Adversarial testing and hallucination audits. Validate prompt engineering.

Post-Deployment

Monitoring

Continuous monitoring of system performance, error rates, and user feedback. Prompt optimization reviews.

Performance Metrics (KPIs)

Accuracy Metrics

Precision % correct outputs
Recall % answers found
Groundedness % source-aligned

Operational Metrics

Latency Average trigger-to-completion time
Throughput Ops per hour/day
Reliability % 24/7 reliability
Cost Efficiency Cost per op (API + Platform)

Business Metrics

Time saved Hours/month manual work eliminated
Cost saved Monthly labor costs eliminated
Conversion rate % leads converted
Revenue uplift Revenue increase
Automation ROI (savings + gains - system costs) / system costs

We engineer real-time KPI dashboards tailored to your specific objectives, enabling you to monitor ROI and pinpoint strategic optimization opportunities across your automated systems and core processes. Custom monitoring solutions that track your defined metrics, not just the ones listed within these tables.

Safety Mechanisms

Content Filters:

OpenAI Moderation API: Flags hate speech, violence, self-harm, ...
Custom blacklists: Block specific words, phrases, or topics
Output validation: Regex patterns or modules to catch policy violations
Human-in-the-Loop: High-risk outputs require approval before sending

Access Controls

Role-Based Access Control (RBAC): Limit who can modify workflows
Audit logging: Track every change and who made it
Two-factor authentication (2FA): Protect admin accounts
IP whitelisting: Restrict access to specific networks

Fail-Safe Mechanisms

Rate limiting: Prevents abuse and ensures fair usage
Budget caps: Limits spending to prevent financial loss
Kill switches: Emergency stop for critical systems
Rollback capability: Revert to previous versions

4. Infrastructure & Deployment

Hosting Options

Cloud-Based (Default): Make.com (EU/US), OpenAI API.
Self-Hosted: n8n, Docker, Air-gapped options.

Redundancy & Outages

Make.com Outage Fallback

Critical workflows replicated on n8n. Auto-failover routes traffic to backup platforms automatically.

OpenAI API Fallback

Circuit breaker pattern detects failure and switches engine to Anthropic Claude or Google Gemini.

Initial Deployment

Staging environment setup: Clone production config for safe testing.
Credential provisioning: Secure API keys and OAuth tokens.
Access control configuration: Set granular user permissions.
Initial data migration: Import historical data if needed.
User training: Walkthrough of system operation.
Go-live cutover: Switch from manual to automated processes.

Updates & Maintenance

Versioning: Workflows that are version-controlled
Rollback: Revert to previous version if needed.
Zero-downtime: New version deployed in parallel.
Notifications: You're notified 48 hours before any update.
Testing: All updates tested in staging.

5. Data Security & Compliance

GDPR Compliance

We implement GDPR best practices as a Data Processor under your instructions.

Processing Agreements

� DPAs executed with all clients
� Documentation of sub-processors
� Purpose limitation & storage caps

Data Subject Rights

� Right to access & rectification
� Right to erasure (Right to be forgotten)
� Machine-readable data portability

Encryption & Access

TLS 1.3 for data in transit. AES-256 for data at rest. OAuth 2.0 for all secure credential storage. Role-based access control (RBAC) is enforced across all platforms.

6. Troubleshooting & Edge Cases

Inconsistent Responses?

Diagnosis: Check prompt variability, review temperature settings (0.1-0.3 recommended), and ensure RAG retrieval is fetching the same context.

Solution: Lower temperature to 0.0 for deterministic results, add few-shot examples, or switch to GPT-4o for higher consistency.

System Latency?

Benchmarks: AI Email (3-8s), Custom Agent (10-30s), Document Analysis (30-60s).

Strategy: Group requests via batch processing, implement caching for frequent queries, or use faster models like Claude 3 Haiku for simple tasks.

Integration Breaks?

Procedure: Monitoring detects failure ? System auto-pauses ? Assessment of provider changelog ? Update implementation ? Verify in staging ? Deploy fix.

API rate limits?

Diagnosis: Workflow fails with "429 Too Many Requests" response from a third-party provider.

Solution: Implement intelligent queuing, add jitter between requests, or upgrade API tiers for higher throughput.

7. Glossary: Key Terminology

AI & Machine Learning Terms

Large Language Model (LLM) A neural network trained on massive text corpora capable of understanding and generating human-like text. Examples: GPT-4, Claude 3, Gemini.

Retrieval-Augmented Generation (RAG) An AI technique that combines LLMs with external knowledge bases or vector databases to improve factual accuracy.

Hallucination When an AI generates false or nonsensical information presented as fact. Caused by lack of grounding in source data.

Prompt Engineering The process of designing prompts (instructions) to elicit specific behaviors or outputs from LLMs.

Few-Shot Learning Training technique where the AI learns from a small number of examples (3-10) rather than thousands.

Fine-Tuning Customizing a pre-trained model by training it on domain-specific data. Improves accuracy for specialized tasks.

Embedding A numerical vector representation of text that captures semantic meaning. Used for similarity search.

Token The smallest unit of text processed by an LLM (roughly 4 characters). Models have token limits (e.g., GPT-4: 128K).

Temperature A parameter controlling randomness in AI outputs. Lower (0.0-0.3) = consistent, higher (0.7-1.0) = creative.

Context Window The maximum amount of text an LLM can process at once. GPT-4: ~128K tokens (~300 pages).

Automation & Integration Terms

API Application Programming Interface. Protocols that allow different software systems to communicate.

Webhook Mechanism for sending real-time data from one system to another when an event occurs.

OAuth 2.0 Authentication protocol that allows secure access to user data without exposing passwords.

Rate Limit Maximum number of API requests allowed within a time period. Prevents system overload.

Circuit Breaker Design pattern that detects failures and prevents cascading errors by pausing operations.

Idempotency Property where performing the same operation multiple times produces the same result.

Async Processing Executing tasks in the background without blocking other operations. Used for long-running processes.

Quality & Reliability Terms

Human-in-the-Loop (HITL) Design where humans review or override AI outputs before usage. Critical for high-stakes decisions.

Groundedness Measure of how well an AI's output aligns with source documents or trusted context.

Precision The percentage of AI outputs that are correct. Formula: True Positives / (TP + FP).

Recall The percentage of correct answers the AI finds. Formula: True Positives / (TP + FN).

F1 Score The harmonic mean of precision and recall. Balances accuracy and completeness.

Latency Time between triggering an automation and receiving output. Target: <10s for real-time.

Throughput The number of operations processed per unit of time (e.g., 1,000 emails/hour).

Infrastructure & Security Terms

Vector Database Specialized database for storing embeddings. Enables semantic search for RAG. Examples: Pinecone.

CI/CD Automated pipelines that test, validate, and release software to production environments.

Docker Containerization technology that packages applications with dependencies for consistency.

VPC Virtual Private Cloud. Isolated section of cloud infrastructure logically separated from others.

SOC 2 Type II Standard for service organizations' security, availability, and privacy controls.

GDPR EU regulation governing personal data. Grants rights to access, erasure, portability, etc.

DPA Data Processing Agreement. Contract between controller and processor specifying handling.

Encryption at Rest Encrypting stored data. Protects against physical theft or unauthorized storage access.

Encryption in Transit Encrypting data during transmission over networks using TLS/SSL (HTTPS).

8. Contact Technical Support

For Existing Clients

Contact your support engineer

Target Response Times

Critical: 2-4 business hours

Normal: 8-24 business hours

Please include error logs, screenshots, and timestamps for faster resolution.

For Prospective Clients

Discuss partnership options

BRING THIS TO THE CALL:

� Current tech stack (CRM, databases)
� Data volume estimates
� Compliance requirements (GDPR, etc.)

Book Partnership Call