Developer & Integration Approach
How Kriv AI connects AI agents, automations, and models into your existing systems—without compromising control or compliance.
We integrate with your EHRs, CRMs, data platforms, and automation tools using well-understood patterns: APIs, webhooks, event-driven workflows, and secure data platforms. Your data stays in your environment whenever possible; our agents and automations are designed around that constraint.
Our Integration Philosophy
Clear principles guide how we connect AI capabilities to your existing systems.
Your Environment First
We prefer to deploy into your cloud, data platform, or automation stack where possible, especially when PHI/PII is involved. We build around your guardrails instead of asking you to bypass them.
APIs, Events, and Webhooks
We rely on stable APIs, event buses, and webhooks—not fragile screen scraping—so integrations are maintainable and auditable.
Least-Privilege Access
We design agents and automations with the minimum access they need: scoped API keys, service accounts, and role-based access control.
Separation of Concerns
Data platforms (e.g., Databricks/Snowflake), operational systems (EHR/CRM), and AI components (LLMs/agents) are clearly separated, with explicit contracts between them.
Auditability & Observability
We log key events and decision points so compliance, security, and engineering teams can understand what the system did and why.
Reference Stacks We Commonly Work With
These are representative patterns—not an exhaustive list. We adapt to your specific environment and requirements.
Microsoft / Azure + Power Automate Stack
Components
- •Azure (data + AI services)
- •Azure OpenAI / Copilot Studio
- •Power Automate / Power Apps
- •Microsoft 365 / SharePoint / Dynamics
Typical Use
Agentic workflows across Microsoft 365, CRM, and line-of-business apps.
Kriv AI Role
Design and implement agentic workflows, orchestration logic, and governance guardrails within your Microsoft ecosystem.
Databricks / Snowflake + BI Stack
Components
- •Databricks or Snowflake as core data platform
- •ETL/ELT tooling
- •BI tools (Power BI, Tableau, etc.)
Typical Use
Data preparation for LLMs, model training, and governed feature stores feeding AI agents.
Kriv AI Role
Define AI-ready data pipelines, connect agents to curated datasets, and ensure lineage and governance.
Zapier / Monday / SaaS-Oriented Stack
Components
- •Zapier (Kriv AI is a Zapier Silver Solution Partner)
- •Monday.com, CRMs, ticketing systems
- •Communication and collaboration tools
Typical Use
Mid-market automation across CRM, finance tools, communication platforms, and internal apps.
Kriv AI Role
Design agentic workflows where AI orchestrates or reviews steps across multiple apps, with human-in-the-loop as needed.
n8n / Event-Driven Automation Stack
Components
- •n8n (self-hosted or cloud)
- •Internal APIs, queues, and events
- •LLM providers (OpenAI, Anthropic, Azure OpenAI)
Typical Use
Complex workflows with more control, often in regulated or engineering-heavy environments.
Kriv AI Role
Architect and implement workflows with clear error handling, retries, and governance hooks.
Systems We Integrate With
These represent capabilities, not official endorsements. Exact systems and integration patterns are tailored to your environment and risk profile.
EHRs & Clinical Systems
- •EHRs via FHIR/HL7-based APIs where possible
- •Scheduling and clinical workflow tools
- •Document repositories for clinical notes
CRMs & Operational Systems
- •CRMs (e.g., Salesforce, HubSpot, Dynamics)
- •Ticketing and case management tools
- •Supply chain and inventory systems
Data Platforms
- •Cloud data warehouses and lakehouses (e.g., Databricks, Snowflake)
- •Operational data stores and analytics layers
Automation & Orchestration
- •Zapier (including advanced, multi-step workflows)
- •Power Automate and Microsoft ecosystem
- •n8n for more customizable workflows
AI & LLM Providers
- •Enterprise LLM providers such as OpenAI, Anthropic, and Azure OpenAI
- •Vector stores and retrieval layers (e.g., Pinecone, etc.)
Exact systems and integration patterns are tailored to your environment and risk profile.
Typical Data Flow & Topology
A high-level reference architecture showing how data and decisions flow through an AI-enabled system.
Source Systems
EHR / clinical systems, CRM, line-of-business apps, data warehouses
Integration Layer
APIs, webhooks, queues, or ETL/ELT into a controlled data platform
AI/Agent Layer
LLMs, agents, and orchestration logic consuming only the data they need, through clearly defined interfaces
Automation Layer
Zapier / Power Automate / n8n flows that execute actions based on agent decisions and business rules
Observability & Governance
Central logging, metrics, audit trails, and governance checks
PHI/PII Handling: In many healthcare and life sciences environments, PHI stays inside your secured cloud or data platform. LLMs and agents are either deployed in that environment or exposed through tightly controlled patterns (e.g., de-identification, retrieval over approved datasets rather than free-text dumps).
Environments, Deployment & Change Management
How we approach infrastructure, releases, and change control in your environment.
Multi-Environment Setup
We prefer to work with clearly separated dev, test/stage, and prod environments. AI agents and automations are promoted between environments using your existing CI/CD and approval flows wherever possible.
Infrastructure as Code (Where Possible)
We align with your infra-as-code practices (e.g., ARM/Bicep, Terraform, Git-based workflows) rather than one-off manual setups.
Controlled Rollouts
We encourage feature flags, pilot scopes, and gradual rollout of new agents or workflows, especially in clinical or financial processes.
Change Documentation
We document changes to automations, prompts, and models in a way that your internal teams and auditors can understand.
Security, PHI/PII & Compliance Considerations
Key principles guiding how we handle sensitive data and regulatory requirements.
Data Residency & PHI
Our default assumption: PHI and other sensitive data stay in your controlled environment. Architectures are designed to respect data residency and regulatory constraints.
Access & Identity
Integrations use service accounts, scoped API tokens, and role-based access control. We work with your identity provider (e.g., SSO, MFA requirements) rather than around it.
Logging & Audit Trails
We design workflows so that key decisions, actions, and model/agent calls can be logged and traced when needed, subject to your logging policies.
Vendor Evaluation
When we propose third-party tools (LLM providers, automation tools), we expect to go through your vendor security review and support providing technical details for that process.
Note: We do not provide legal advice. We work in close collaboration with your security, compliance, and legal teams to align architectures with your obligations (e.g., HIPAA, GDPR).
How We Collaborate with Your Developers
We work as a specialist partner, not a replacement for your internal teams.
Co-Design Workshops
We run architecture and workflow design sessions with your architects, data engineers, and operations stakeholders.
Shared Repos & Standards
We can work within your Git repos, coding standards, and review processes, or maintain separate modules with clear interfaces.
Handover & Upskilling
We document patterns and provide handover sessions so your teams can maintain and extend what we build, rather than creating a black box.
For some clients, we act as an extension of their internal AI/automation team; for others, we design patterns that they implement internally.
Developer & Integration FAQs
Common questions from architects, data engineers, and IT leads.
Want to Pressure-Test an Integration Idea?
Whether you're wondering how an AI agent would sit alongside your EHR, CRM, or data platform, we can walk through practical patterns with your technical team.