AI Integration & Implementation
Services
Connect AI with the systems where your business already works.
Our AI integration and implementation services help organizations move AI from isolated prototypes into production by connecting models, applications and automation with ERP, CRM, databases, APIs, document systems and enterprise workflows - including integration architecture, data flow, permissions, validation, deployment, monitoring and operational handoff.
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AI Integration
Services
Connect AI capabilities to real applications, data and workflows without rebuilding your entire technology environment.
Enterprise
Enterprise AI Integration
Integrate AI applications into existing enterprise systems while preserving business rules, permissions and existing systems of record.
Services
AI API Integration
Connect managed AI models, custom AI services and internal applications through secure APIs and application services.
Operations
ERP AI Integration
Add AI capabilities around operational workflows while keeping ERP platforms responsible for authoritative business transactions.
Customers
CRM AI Integration
Connect AI with customer, lead, opportunity and service workflows inside CRM platforms.
Legacy
Legacy System Integration
Add AI capabilities around existing applications that cannot be replaced but still contain important business processes or data.
Process
AI Workflow Integration
Insert AI into existing workflows where it can classify, extract, summarize, predict or assist with defined process steps.
Need a new AI application built from the ground up? Explore AI Development Services.
AI Implementation
Services
Move an approved AI capability from prototype into day-to-day operation.
Deploy
Production Deployment
Deploy AI applications into the required cloud, private, hybrid or supported on-premise environment.
Models
Model Integration
Connect selected commercial or open models to the application layer without turning model selection into a separate development project.
Data
Data Integration
Provide AI applications with the structured information they require through controlled services and existing data platforms.
Apps
Application Integration
Embed AI into web applications, enterprise software, portals and internal tools.
Process
Workflow Implementation
Connect AI outputs with downstream business processes, approvals and existing automation.
Handover
Production Handoff
Deliver configuration, documentation, monitoring and operational ownership to the team responsible for the production system.
Implementation ends when someone other than us can run it on a Monday morning.
AI Systems
We Integrate
The implementation approach depends on what type of AI capability needs to reach production.
Language
LLM Applications
Connect language models with business applications, interfaces and operational workflows. For deeper model engineering, see LLM Development Services.
Retrieval
RAG Applications
Integrate retrieval systems with document repositories, permissions, applications and enterprise knowledge sources. For retrieval engineering, see RAG Development Services.
Acting
AI Agents
Connect agents to approved APIs, tools and enterprise workflows with controlled action boundaries. For agent engineering, see Agentic AI Development Services.
Predictive
Machine Learning Models
Deploy predictive, classification, recommendation and forecasting models into operational applications. For modelling, see Machine Learning Development.
Documents
Document AI
Connect extraction and document-intelligence systems with ERP, finance, logistics and review workflows.
Generative
Generative AI
Embed text, image, video and multimodal generation capabilities into products and business systems. For generation engineering, see Generative AI Development Services.
Each of these reaches production differently, which is why the integration design starts from the capability rather than from a template.
AI Integration
Architecture
Production AI should sit inside a controlled enterprise architecture, not beside it.
Stage 01
Business System
The workflow begins inside the application where employees or customers already work.
Stage 02
Integration Layer
APIs, application services, connectors or event handlers move approved information between systems.
Stage 03
AI Service
The relevant AI model or application performs its defined task.
Stage 04
Validation
Check that the AI response has the expected structure and meets application rules.
Stage 05
Business Rules
Apply deterministic rules before anything changes downstream.
Stage 06
Human Approval
Pause important actions where authorization or judgment is required.
Stage 07
System of Record
Write approved results back to the authoritative enterprise application.
Stage 08
Monitoring
Record operational activity, errors and important workflow outcomes.
Stages 04 to 06 are the ones most prototypes skip, and they are the reason those prototypes never get approved for production.
Where AI Integration
Creates Value
The value is rarely in the model. It is in removing the manual step between the model and the system that runs the business.
Handoffs
Eliminate Copy and Paste
Move information between AI and enterprise applications automatically instead of relying on people to transfer results manually.
Inputs
Use Current Business Data
Connect AI to approved operational information rather than isolated files or manual exports.
Continuity
Keep Existing Systems
Add intelligence around the systems already running the business.
Speed
Improve Workflow Speed
Use AI at the exact step where interpretation, extraction or prediction currently slows the process.
Control
Maintain Human Control
Insert validation and approval before important AI outputs change authoritative business records.
Operations
Create Production Visibility
Track requests, failures, latency and workflow outcomes once AI is operating inside production processes.
Every one of these is measured against the workflow, not against the model.
Connect AI With
Enterprise Systems
Built to work with the platforms businesses already use.
ERP
ERP Platforms
Odoo, SAP, Oracle, Microsoft Dynamics, NetSuite and custom ERP systems - for document processing, operational queries, order workflows, inventory information, accounting support and transaction preparation.
CRM
CRM Platforms
Salesforce, HubSpot, Zoho, Microsoft Dynamics CRM and custom CRM platforms - for enquiry classification, lead scoring, customer summaries, follow-up preparation, service routing and record enrichment.
Service
Service Platforms
ServiceNow, ticketing platforms, customer-support systems and internal service portals.
Data
Databases
PostgreSQL, SQL Server, MySQL, MongoDB, Snowflake, Databricks and other enterprise data platforms, reached through controlled application services.
Content
Document Platforms
Approved document stores, enterprise file systems, knowledge platforms, SharePoint and content repositories.
APIs
Business APIs
REST, GraphQL, webhooks and event-driven integrations connecting AI with existing applications.
Where a platform has a native extension framework, we use it rather than bolting an external service onto the side of it.
Keep Systems of Record
in Control
AI should not silently become the authoritative source for important enterprise data.
ERP
ERP Remains the ERP
AI can prepare, classify or validate information before approved data reaches the ERP.
CRM
CRM Remains the CRM
AI can interpret interactions and prepare updates while the CRM remains the authoritative customer record.
Data
Data Platforms Remain Authoritative
AI applications should access approved data through controlled interfaces rather than creating disconnected copies unnecessarily.
People
Human Authority Remains Defined
Important decisions and transactions should follow existing approval requirements.
This architecture reduces the risk of implementing AI as an isolated parallel system that quietly diverges from the business.
AI Integration
Patterns
Different systems require different integration approaches.
Direct
API Integration
Use supported APIs when enterprise applications already expose the required functionality.
Reactive
Event-Driven Integration
Trigger AI workflows from events such as a new document, new enquiry, ticket created, order update or status change.
Callback
Webhook Integration
Use application events to initiate AI processing or downstream actions.
Coordinated
Middleware Integration
Use a service layer when several systems need coordinated access to AI capabilities.
Native
Native Platform Integration
Integrate through the native extension mechanisms offered by ERP, CRM and service platforms.
Scheduled
Batch Integration
Process scheduled groups of documents or records where real-time execution is unnecessary.
The pattern is chosen by what the receiving system actually supports, not by preference.
Human-in-the-Loop
Implementation
Not every AI result should move directly into another system.
Threshold
Confidence-Based Review
Route low-confidence outputs to a reviewer.
Authorize
Approval Workflows
Require approval before important records are created or modified.
Verify
Validation Queues
Let users verify extracted or generated information before it reaches the system of record.
Unusual
Exception Handling
Send unusual or unsupported scenarios to the appropriate team.
Learn
Correction Capture
Record human corrections where they are useful for improving future performance.
Scope
Proportionate Review
Review sits where the consequence justifies it, so throughput is not spent on outputs nobody needed to check.
Review capacity is usually the real constraint on how much of a workflow can be automated.
Secure Enterprise
AI Integration
AI integration expands the number of systems and data flows involved in an application, so security has to cover the entire integration path.
Identity
Authentication
Use approved identities for users, services and system-to-system connections.
Permissions
Authorization
Apply application and business permissions before AI accesses information.
Endpoints
API Security
Protect service endpoints, tokens and integration credentials.
Credentials
Secrets Management
Keep credentials outside prompts, application logs and exposed client code.
Transit
Data Encryption
Protect business information in transit and at rest where required.
Evidence
Audit Logging
Record relevant requests, system changes and approvals.
Boundary
Network Controls
Deploy integration services within appropriate enterprise network boundaries.
Failure
Safe Failure Modes
Decide in advance what happens when a provider, API or downstream system is unavailable, rather than discovering it in production.
Credentials leaking into prompts or logs is the single most common finding when we audit an existing AI integration.
Security, Privacy and Responsible AI
Applicability depends on geography, industry, the data processed, the deployment model, the use case and your own legal and regulatory obligations.
AI Implementation
Governance
Governance should remain part of implementation rather than becoming a separate documentation exercise.
Models
Model Approval
Use approved models and providers.
Data
Data Approval
Confirm which data sources the application may access.
Change
Change Management
Review material changes before production rollout.
Oversight
Human Oversight
Define when a person must review AI output.
Ownership
Operational Ownership
Assign responsibility for monitoring and production support.
Response
Incident Handling
Define what happens when AI or an integration behaves unexpectedly.
For organization-wide governance design, see our AI Governance Consulting Services.
AI Integration
by Industry
The integration pattern is shaped by which systems already hold the work.
Logistics
Logistics
Connect AI document processing, operational systems, shipment workflows and enterprise data.
Industry
Manufacturing
Integrate AI with machine information, sales workflows, maintenance processes and operational reporting.
Finance
Financial Services
Connect AI applications with controlled data, review workflows and enterprise systems. Payment workflows can be designed around applicable PCI DSS requirements.
Health
Healthcare
Integrate approved AI capabilities into administrative and operational systems while maintaining defined access controls. HIPAA-aligned controls can be applied where US healthcare data is involved.
Retail
Retail and E-commerce
Connect AI with product data, inventory, CRM, customer service and commerce workflows.
Enterprise
Enterprise Operations
Integrate AI across finance, HR, sales, support, service management and internal applications.
Three of the four implementations below are logistics, manufacturing and service operations, for exactly this reason.
AI Integration
Technology Stack
Chosen for what the receiving systems support, not for what is newest.
Where the business already works
Selected per workload, not per vendor
How systems actually talk
The deterministic half of the workflow
Where approved information lives
What it runs on
AI Integration
in Production
Real implementations where AI was connected to existing applications and business workflows.
Transworld Logistics
A logistics operation needed AI-assisted document processing connected to the ERP responsible for fleet, warehouse, accounting and shipment workflows. The enterprise workflow was established first, then an AI extraction and validation layer was placed in front of the ERP.
Invoices and bills of lading enter the AI layer, required fields are extracted, values are validated against operational records, and only approved information moves downstream. Pattern: shipment documents to AI extraction to validation to human review to ERP workflow.
Best proof foran AI layer in front of the ERP, not instead of it
- Odoo-based ERP
- Document extraction
- Invoice processing
- Bills of lading
- Operational validation
- Human review
- ERP integration
- Shared operational records
Featured image from the Transworld Logistics case study.
Odoo AI Assistant
A multi-department business needed employees to retrieve current ERP information without manual report requests or technical queries, so the assistant was implemented directly around the existing Odoo environment.
It uses Odoo's native ORM and role permissions, so users ask questions in plain English while existing ERP access controls stay in place. Pattern: user question to assistant to Odoo ORM to permission check to current records to structured response.
Best proof forAI inside the ERP's own permission model, not beside it
- Native Odoo ORM
- Role-based permissions
- Natural-language queries
- Current ERP records
- Structured responses
- Export options
- CRM, sales, inventory
- HR and accounting
Featured image from the Odoo AI Assistant case study.
QuantusTechnik
QuantusTechnik needed AI operating across website enquiries, sales workflows, machine information and management reporting rather than functioning as a separate chatbot or analytics tool.
The implementation connected GPT-4o, Python, LangChain, PostgreSQL, pgvector and n8n with daily business workflows. Pattern: forms, CRM and machine data to PostgreSQL to Python rules to AI processing to n8n to team actions and reports.
Best proof forone integration chain across enquiries, machines and reporting
- GPT-4o
- Python
- LangChain
- PostgreSQL
- pgvector
- n8n
- Workflow routing
- Automated alerts
Featured image from the QuantusTechnik case study.
Pulastya
A service business needed AI call handling without changing the phone number or removing existing routing responsibilities, so Pulastya was connected to the client's existing Twilio numbers and business workflows.
It classifies intent, answers supported requests from approved business information, and transfers unsupported calls to the relevant team with transcript and summary context. Pattern: existing phone number to Pulastya to intent classification to knowledge and routing logic to the human team.
Best proof forAI added around existing communication infrastructure, not replacing it
- Existing Twilio numbers
- Natural conversation
- Intent classification
- Approved knowledge
- Deterministic routing
- Transcript transfer
- AI-generated summary
- Human handoff
Featured image from the Pulastya case study.
Each panel names the integration pattern actually used, and every figure is the one published on that case study.
What Clients Say
About SDLC Corp
Founders, CEOs, and operating leaders share what it's like to build with SDLC Corp.
Eric Leist
CEO, Edgerton Strategies
Doug Schmidt
CEO, Roofaid USA
Reyzal Razmi
All Star Influencers
They approached our Salesforce discovery with real technical depth, uncovered structural gaps others missed, and delivered a solution that worked exactly as promised.
SDLC CORP built a mobile application that met our strategic requirements with strong technical execution. The solution performs reliably and has become an important operational asset.
They saw inefficiencies in our Salesforce workflow and redesigned our entire quote-to-cash system. We now operate faster, cleaner, and with better accuracy.
From planning to post-launch, SDLC Corp guided us every step of the way. Their support makes them more than a vendor. They're a trusted partner.
The SDLC Corp team scaled our platform with impressive technical expertise, ensuring it's secure, robust, and ready for future growth.
The SDLC Corp team scaled our platform with impressive technical expertise, ensuring it's secure, robust, and ready for future growth.
From planning to post-launch, SDLC Corp guided us every step of the way. Their support makes them more than a vendor. They're a trusted partner.
They saw inefficiencies in our Salesforce workflow and redesigned our entire quote-to-cash system. We now operate faster, cleaner, and with better accuracy.
SDLC CORP built a mobile application that met our strategic requirements with strong technical execution. The solution performs reliably and has become an important operational asset.
They approached our Salesforce discovery with real technical depth, uncovered structural gaps others missed, and delivered a solution that worked exactly as promised.
Enterprise Engineering
at Scale.
The systems AI has to integrate with are the systems we already build and operate.
Our AI
Implementation Process
Six stages, ending with someone on your side owning it.
Assess
Understand the target AI capability, existing applications, system ownership, data flows, APIs, security requirements, users and production constraints.
Design
Define the integration architecture, data flow, validation, permissions, workflow rules and operational boundaries.
Connect
Build APIs, connectors, application services and the required enterprise integrations.
Validate
Test AI outputs, data mapping, permissions, workflow behaviour, failure scenarios, human approvals and system changes.
Deploy
Release into the required environment using controlled rollout and production configuration.
Operate
Establish monitoring, documentation, ownership and improvement workflows.
Production
Rollout Strategy
AI implementations do not need to move from prototype to full enterprise rollout in one step.
Stage 01
Controlled Pilot
Launch with a defined group of users or workflow volume.
Stage 02
Parallel Operation
Run the AI process alongside the existing workflow where appropriate.
Stage 03
Human Validation
Review production outputs before increasing automation.
Stage 04
Gradual Expansion
Increase users, systems or transaction volume after quality is demonstrated.
Stage 05
Operational Handoff
Transfer ownership with documentation, dashboards and escalation procedures.
Parallel operation is the cheapest way to find out whether the AI result would actually have been accepted.
Monitor AI
in Production
Integration success is not finished at deployment.
Uptime
Availability
Monitor whether AI and integration services are operational.
Speed
Latency
Track how long end-to-end workflow execution takes.
Errors
Failure Rate
Measure model, API and downstream integration failures.
People
Human Escalations
Track cases requiring review.
Outcome
Workflow Completion
Measure whether AI-enabled processes reach the intended business outcome.
Spend
Cost
Monitor model and infrastructure usage around completed workflows.
Drift
Changes
Track important modifications to providers, models and enterprise interfaces.
Records
Downstream Integrity
Confirm that what the workflow wrote into the system of record is what the business expected to see there.
Workflow completion is the metric that matters; model accuracy on its own does not tell you whether the business process finished.
Improve Existing
AI Implementations
Already have AI in production but struggling with reliability or integration issues? We review the complete implementation.
Signs an AI implementation is not actually integrated
We evaluate the complete operating workflow rather than treating every problem as a model issue.
If the use case itself is still undefined, start with AI consulting services instead.
Why Choose SDLC Corp
for AI Integration
Integration work is judged by whether the business process finished, not by whether the model responded.
Both Sides
AI and Enterprise Systems
Our teams work across AI engineering and the ERP, CRM, data and business platforms AI needs to connect with.
Architecture
Integration-First Architecture
Design AI around the existing operating environment rather than forcing businesses to rebuild everything around the model.
Authority
Systems of Record Stay in Control
Maintain clear ownership of business information and transactions.
Oversight
Human Validation Where Needed
Keep approval and exception handling inside important workflows.
Production
Production Engineering
Design for security, integration failures, monitoring and operational handoff from the start.
AI Implementation
vs AI Development
Build the capability, or connect it. Most programmes need both, in that order.
Choose AI development when the primary requirement is to build a new AI capability or application.
Choose this service when the primary requirement is to connect an AI capability to existing systems and make it work in production.
Question
What Should It Do?
Development answers the capability.
Question
How Does It Connect?
Implementation answers the environment.
Output
A Working Capability
A model or application that performs the task.
Output
A Running Workflow
A business process that completes end to end.
Together
Often the Same Programme
A project can involve both; they are separate disciplines, not competing ones.
Explore AI Development Services when the capability itself still has to be built.
AI Implementation
vs AI Consulting
One decides what is worth doing. The other makes it run.
Answers what should we build, where should AI be used, what is the roadmap, and which use cases have value.
Answers how it connects, how data moves, which system stays authoritative, what happens when AI fails, where human review occurs, and how it is deployed and operated.
Stage
Before the Decision
Consulting reduces uncertainty about what to fund.
Stage
After the Decision
Implementation delivers it into the environment.
Risk
Choosing the Wrong Problem
The failure consulting prevents.
Risk
A Prototype That Never Ships
The failure implementation prevents.
Signal
Which One You Need
If the objective is already clear, you need implementation.
Explore AI Consulting Services when the use case still has to be chosen.
AI Integration
vs Workflow Automation
The strongest enterprise workflows usually combine both.
Use deterministic automation where the process follows known rules.
Use AI inside the workflow where a step requires judgment rather than a rule.
Rule
Known and Fixed
Deterministic software handles it, faster and cheaper.
Judgment
Uncertain or Language-Heavy
AI handles the step a rule cannot express.
Design
Split the Workflow
AI for the uncertain step, deterministic software for the rest.
Cost
Do Not Pay for Certainty
Sending a rule-shaped step to a model costs money and adds variance.
Both
Usually Combined
Most production workflows we build use both in the same chain.
Explore Workflow Automation Services for the deterministic half.
AI Integration
Resources
Background reading on connecting AI to the systems that already run the business.
GuideIntegrating AI Into Enterprise Systems
How AI connects with ERP, CRM, data, identity and workflow systems while maintaining permissions, validation and ownership.
Read the integration guide
StrategyHow to Move AI Pilots into Production
What has to be true before a working pilot can be trusted as a production system.
Read the production guide
ArchitectureModern Enterprise Data Architecture
How models, integrations, data systems and enterprise applications fit together.
Read the architecture guide
DataEnterprise Data Integration Strategy
How to give AI applications controlled access to approved operational data.
Read the data integration guideImplement AI
Where Work Happens
Move AI out of isolated prototypes and into the systems your employees and customers already use.
From ERP and CRM integration to document workflows, APIs, enterprise applications and production rollout, our AI implementation team can connect your approved AI capability with the rest of your technology environment.
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