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AI Does Not Remove the Bottleneck
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AurvikAI · AI Integration
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 have 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
SLA
Uptime commitment on every integrated system, matched to our measured production record
Clients we've worked with
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 600+ 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.
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
Integration capabilities
From legacy systems to modern cloud-native architectures
Legacy system integration
SOAP services, mainframe data stores, proprietary protocols. We have 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
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.
Discuss your integration2 weeks
Architecture mapping and contract definition
Zero
Integration-related production outages in the last 24 months
Thinking worth reading
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Selected work
AI systems running in production, described rather than named.
A UK e-prescription startupBuilding an e-prescription platform around trust, access, and scale.Clinic management and electronic prescribing with eIDAS-compliant signatures, and a pharmacy lookup that needs no account at all.16 wksfrom kickoff to a launch-ready platformRead the case
A US medical equipment resellerBuilding a sales channel the business could ownA storefront of their own beside a high-volume eBay business, with inventory kept in step both ways and double sales resolved automatically.5 minfor stock to reflect across both sales channelsRead the case
A US remote-monitoring healthtechSupporting rehabilitation after dischargeRemote therapeutic monitoring built so the billing qualification cannot be faked, with an AI care plan no patient sees unapproved.15 mina day for a physician to review a full patient panelRead the caseWhat we build with
Interfaces
- REST / APIs
- Webhooks
- NestJS
- FastAPI (Python)
Reliability
- Redis
- BullMQ
- Sentry
Models
- Anthropic Claude
- GPT (OpenAI)
Questions we get asked
Where does the integration effort go?
Rarely into the model. It goes into permissions, identity, error handling and the systems that were never designed to be called this way. A feature that works in a demo and fails in your environment is almost always failing at that boundary, which is why we map it before designing anything.
Can AI reach systems with no API?
Sometimes, and it is usually the wrong place to start. Where a system exposes nothing, the options are a database view, a scheduled export, or a middleware layer somebody has to own. We would rather build that integration honestly than have an AI feature depend on screen-scraping a screen that will change.
How do you keep permissions correct?
The AI operates with the permissions of the person asking, never with a service account that sees everything. That is harder to build and is the only approach that survives a security review, because anything else means a user can reach data through the assistant that they could not open directly.
What happens when a connected system is down?
The feature degrades in a way you have chosen in advance. Requests queue and retry where the work can wait, fail fast with a clear message where it cannot, and escalate to a person where something must still happen. Silent failure is the outcome we design hardest against.
Will this slow our existing systems down?
It can, and that is a design question rather than an accident. AI features generate query patterns nobody planned for, so we read from replicas, cache aggressively and rate-limit our own calls. Your production systems keep serving the people who depend on them.
How long does an integration take?
6 wks for a first system, and the timeline usually rests on access rather than engineering. Credentials, environments and whoever owns the system being integrated are what set the pace.
Partners in delivery
AI Cloud PartnerPartner Network
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.