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ENTERPRISE AI ENGINEERING
Generative AI Integration Services That Actually Reach Production
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What Blocks Generative AI Integration From Reaching Production
A generative AI pilot proves the model works. It doesn't prove the integration will hold up once real users, real data, and your compliance team get involved, and that gap is usually where the timeline slips. The cause is rarely the model. It's brittle point-to-point API calls, no persistent context for the model to draw on, and no audit trail once compliance asks how a decision got made, the kind of integration debt that's expensive to unwind later.
A working demo and a production integration are not the same deliverable. That's an architecture problem. Citrusbug's generative AI integration services start with an audit of the systems you already run, using the same data integration discipline we'd apply to any enterprise pipeline, so the model gets access to what it actually needs from day one.
A working demo and a production integration are not the same deliverable. That's an architecture problem. Citrusbug's generative AI integration services start with an audit of the systems you already run, using the same data integration discipline we'd apply to any enterprise pipeline, so the model gets access to what it actually needs from day one.
Point-to-Point API Sprawl
Custom connectors that break every time a vendor changes an endpoint or you swap models.
No Persistent Context Layer
The model resets between sessions because nothing feeds it real business data or history.
Missing Audit Trail
No record of what the model saw or why it made a call, which stalls every compliance review.
Retrofit Compliance
Security and legal get looped in after launch instead of shaping the architecture from the start.
Get an Integration Audit Before You Build Anything
We'll map your systems and show you where a generative AI integration would actually connect first.
Schedule a 30-Minute CallGenerative AI Integration Services We Offer
Eight ways we connect generative AI to the systems, data, and workflows an enterprise actually runs on.
Model-to-System Connectivity
We build the connectors between your LLM and the CRM, ERP, and internal databases that hold the context it needs, using MCP-based patterns instead of hand-rolled API contracts that break with every vendor update.
Workflow and Process Embedding
Approval chains, ticket routing, and reporting keep running where your team already works. The model plugs into the workflow instead of becoming a separate tool nobody opens.
RAG and Data Pipeline Integration
We build the retrieval-augmented generation pipelines and vector search layers that give the model real, current data to reason over instead of guessing from a stale training set.
Security, Governance, and Audit Layer
Every model call gets logged, every decision path is traceable, and every escalation to a human is recorded, so a compliance review doesn’t stall the rollout.
Generative AI Consulting and Roadmapping
Before anything gets built, our generative AI consulting work maps which processes actually benefit from integration and which ones don’t, so budget goes where it pays off first.
Custom Generative AI Application Development
When off-the-shelf tooling can’t do what you need, we build a purpose-fit generative AI application from the ground up instead of forcing your workflow around someone else’s product.
Custom LLM Integration and Fine-Tuning
Generic models don’t know your terminology or edge cases. We handle the LLM fine-tuning and integration work so the model performs on your data, not a demo dataset.
Multi-Agent and AI Agent Integration
When one model isn’t enough, we design and integrate coordinated AI agents that hand off tasks to each other through A2A, not a single monolithic assistant trying to do everything.
Our Approach to Generative AI Integration
Every engagement starts with a build vs buy question your team has probably already argued about internally. Once you land on integration over an off-the-shelf tool, we scope the engagement around your actual systems, what needs custom connectors, what can run through an existing platform integration, and what your compliance team needs to see before anything ships.
System and Data Audit
We map your CRM, ERP, and data pipelines before recommending anything, so the architecture reflects what you’re actually running instead of a template.
Custom Model Tuning
When a generic model doesn’t know your terminology or business rules, we bring in custom LLM development to close that gap instead of forcing a workaround in the prompt.
Security and Governance Layer
Access controls, data handling rules, and security and compliance requirements get built into the connector layer, not reviewed after the fact.
Handover and Documentation
You get the architecture diagrams, connector documentation, and source code, not a black box your team has to reverse-engineer to maintain.
The AI Integration Architecture We Build Around Open Standards
Every integration we build routes tool calls through Model Context Protocol connections and coordinates multi-agent handoffs through Agent-to-Agent delegation, the same open standards now backing production deployments at Salesforce, SAP, and ServiceNow rather than a proprietary framework that creates vendor lock-in.
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MCP-based tool and data connections
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A2A protocol for multi-agent handoffs
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Vector search for real-time context
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Full audit logging on every call
Generative AI Integration Services Across Industries
Healthcare
Fintech and Banking
Insurance
Logistics
Real Estate
EdTech
E-commerce
Manufacturing
Our End-to-End Generative AI Integration Process
1
System and Data Audit
We start by mapping what you're actually running: the CRM, ERP, data warehouses, and any existing automation, along with who owns access to each. This tells us what the model needs to reach, what has to stay inside your infrastructure, and where compliance requirements already constrain the design before we draw a single connector.
2
Integration Architecture Design
We design the connector layer against MCP and A2A standards where they fit, decide what runs through retrieval-augmented generation versus a fine-tuned model, and document exactly what data crosses which boundary. You see the architecture before any of it gets built, not after.
3
Connector and Guardrail Build
Engineers build the actual connections to your systems along with validation layers that catch hallucinations, enforce output boundaries, and define when the model hands off to a human instead of guessing. This is where most vendors stop at a demo. We build for the traffic your systems actually carry.
4
Testing Against Real Data
We test against your actual data patterns and edge cases, not a clean demo dataset, and run the integration through the same compliance and security review your legal team would run anyway. Problems surface here, before go-live, where they're cheap to fix, instead of after launch when a user hits them first.
5
Phased Rollout
We roll out to a limited user group first, watch how the model performs against real workloads and real query patterns, and expand access once accuracy and latency hold up under production traffic rather than a pilot's worth of usage. Nothing goes enterprise-wide on day one.
6
Monitoring and Model Updates
Once live, we track accuracy, cost, and system latency against the baseline we set during testing, retrain or reroute when a model starts to drift, and handle the migration work when a provider deprecates a model or ships something better suited to your workload.
Our Generative AI Integration Engagement Options
Generative AI Integration Services scope differently depending on how many systems are involved and how deep the audit needs to go.
Single-System Integration
One model, one system, one clear use case.
- Scoped in weeks, not quarters
- Fixed integration boundary
- Good fit for a first production deployment
Multi-System Integration
Model connects across CRM, ERP, and internal tools.
- Shared context layer across systems
- Governance built in from the start
- Typical for a first enterprise rollout
Enterprise Integration Program
Ongoing integration work across multiple business units.
- Dedicated senior engineering team
- Rolling roadmap, not a single deliverable
- Built for organizations still discovering where AI fits
COMPLIANCE
Compliance-Ready Generative AI Integrations
Generative AI integrations need to meet the security, privacy, and governance requirements that apply to your business and the data flowing through your systems. Compliance is considered at the architecture stage, so controls are part of the integration rather than an afterthought.
Schedule Your Integration Review
RECENT WORK
View All Case Studies →
Related Projects
MEDIA
Ecommerce
How Much Does Generative AI Integration Cost?
Most projects run $30,000 to $200,000+ depending on how many systems are involved and how deep the compliance review goes.
Tell us what you're working with and we'll scope a real number.
Why Enterprises Choose Citrusbug for Generative AI Integration Services
Built on MCP and A2A Standards
Most integration vendors wire APIs one at a time. Citrusbug uses Model Context Protocol and Agent-to-Agent standards where they fit, creating a more flexible integration layer that can adapt as models, agents, and connected systems change.
Secure ADLC From the First Line of Code
Security isn’t a final review before launch. Our Secure ADLC methodology incorporates access controls, data handling rules, and audit logging into the architecture from the beginning, reducing the need for costly security retrofits later.
Architecture Based on Your Existing Systems
Every engagement starts with an audit of your CRM, ERP, data pipelines, and existing automation. The resulting architecture reflects your actual environment rather than forcing your workflows into a fixed connector template.
Senior Engineers With Clear Ownership
Named senior engineers handle the integration layer, giving your team visibility into who is working with production data and how the system is being built.
Full Source Code and IP Ownership
You receive the source code, connector documentation, architecture diagrams, and integration assets at delivery. The resulting integration belongs to your organization rather than being locked behind a proprietary framework.
Support Beyond Go-Live
Production integrations change as APIs, models, data sources, and business requirements evolve. L1, L2, and L3 support helps keep the integration reliable while providing a path for model updates, connector changes, and ongoing optimization.
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How much do Generative AI Integration Services cost?
Most Generative AI Integration Services engagements run from $30,000 for a single-system connector project to $200,000+ for a multi-system enterprise program, depending on compliance scope.
How long does a typical enterprise integration take?
A single-system integration usually runs 6 to 10 weeks. Multi-system or compliance-heavy programs typically run 3 to 6 months, depending on how much of the architecture already exists.
Can you integrate generative AI into legacy or on-premises systems?
Yes. We connect through APIs, middleware, or direct database access depending on what the legacy system exposes, without requiring a cloud migration first.
Who owns the code, connectors, and models after delivery?
You do. Full source code ownership transfers at delivery, and we operate under NDA by default throughout the engagement.
How do you keep our data out of third-party model training?
We configure data handling policies at the API and infrastructure level so your data isn't used for provider-side training, and deploy in your own environment when the requirement is strict.
What happens when the underlying model provider changes or deprecates a model?
We handle the migration. Because the integration is built on open standards rather than a single vendor's SDK, swapping models doesn't mean rebuilding the connector layer.
Do you handle EU AI Act, HIPAA, and GDPR compliance as part of the build?
Yes. Compliance requirements shape the architecture from the audit phase, not as a review added after the build is done.
Our first AI pilot already failed. Can you fix what's there instead of starting over?
Often, yes. We audit what exists, keep what's usable, and rebuild the parts causing the failure instead of scrapping the whole project by default.