AurvikAI

AI Integration Services

AI that fits into your systems — not the other way around.

18 years of enterprise integration experience. We connect AI to your existing stack without rebuilding what already works.

Most AI projects fail at integration, not at modelling. We've integrated AI into legacy ERP systems, CRMs, data warehouses, and custom platforms across 70+ countries. Zero disruption, full capability.

600+Production integrations delivered
70+Countries with active deployments
99.9%Uptime SLA on integrated systems

The integration gap most teams face

AI models that work in a notebook but fail in production. The gap is almost always integration — not intelligence.

Without proper integration

  • AI models running in isolation, disconnected from business workflows
  • Manual copy-paste between AI tools and production systems
  • Inconsistent data formats causing silent prediction errors
  • No fallback logic when the AI service is slow or unavailable
  • Security and compliance gaps at every integration point

With AurvikAI integration

  • AI predictions delivered directly into the systems your team already uses
  • Automated data flows with validation at every handoff point
  • Standardised API contracts that both sides build to
  • Circuit breakers, retry logic, and graceful degradation built in
  • Full audit trails and access controls meeting enterprise compliance

Integration patterns we've mastered

Every pattern battle-tested across hundreds of production deployments.

RESTful and GraphQL APIs that make AI capabilities accessible to any system in your stack.

Real-time inference APIsSynchronous

Low-latency endpoints for predictions that need to happen in the user's workflow.

Batch processing APIsAsynchronous

High-throughput endpoints for processing large datasets overnight or on schedule.

Webhook integrationsEvent-driven

Push-based notifications when AI models detect conditions that require action.

SDK developmentDeveloper experience

Client libraries in your team's languages that make integration straightforward.

Enterprise-grade reliability

Integration that doesn't break at 3am.

Production AI integration is not a feature — it's an infrastructure commitment. We build every integration with the assumption that something will go wrong, and design the system to handle it gracefully.

99.9%

uptime across all production AI integrations

AurvikAI operations data

01

Circuit breakers

Automatic failover when downstream services are slow or unresponsive — preventing cascade failures.

02

Retry with backoff

Intelligent retry logic that recovers from transient failures without overwhelming your systems.

03

Graceful degradation

When AI is unavailable, the system falls back to deterministic logic — not to a blank screen.

04

Full observability

Every request traced end-to-end with latency, error rates, and prediction quality dashboards.

Integration capabilities

From legacy systems to modern cloud-native architectures

Legacy system integration

SOAP services, mainframe data stores, proprietary protocols — we've integrated AI into systems that other teams refuse to touch. Your legacy investment is protected while gaining modern AI capabilities.

LegacyERPMainframe
Zero

system rebuilds required

Engineering team reviewing AI system architecture diagrams on a whiteboard

AurvikAI integration architecture review for a global logistics client

System architecture diagram showing AI integration points

Our methodology

Map first. Integrate second.

Integration failures almost always trace back to assumptions made before any code was written. Our methodology starts with mapping your existing data flows, APIs, authentication patterns, and integration points — before touching a single AI component. The integration contract is defined, agreed, and tested before production deployment begins.

We specify exactly how AI outputs will be consumed by your existing systems — data formats, latency requirements, fallback behaviour, and error handling.

2 weeks

Architecture mapping and contract definition

Zero

Integration-related production outages in 2024

Common questions about AI integration

From teams evaluating how AI fits into their existing architecture.

Yes. We've integrated AI into COBOL-based mainframes, SOAP web services, on-premise databases, and proprietary systems built in the 1990s. The approach is an API abstraction layer that keeps your legacy system untouched while giving AI models clean access to the data they need.

Ready to connect AI to your existing systems?

Let's start with a conversation about your architecture — we'll map the integration points and give you an honest assessment of what's involved.