United States:
2457 Kane Lane, Batavia, Illinois
60510
Integration Engineering ERP, CRM, Data and API Integration
AI Integration and Implementation Services
AI integration services connect AI models and applications to the systems a business already runs, including ERP, CRM, databases and APIs. AI implementation services take that connected capability into production: validated, secured, monitored and handed to the team that owns it. SDLC Corp delivers both as one engagement.
50+Clients
400+Specialists
98%On-time
10+Years
Trusted by enterprises, government bodies and universities worldwide
Powered by leading cloud & AI platforms
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Google AIAWSGoogle CloudMicrosoft AzureNVIDIAOpenAIAnthropicGeminiGrokPerplexityGoogle AI What We Deliver
AI Integration and Implementation Services
AI integration services connect AI models and applications to your ERP, CRM, databases and APIs. AI implementation services take that capability into production and hand it to your team.
Integration
ERP AI Integration
Add AI around operational workflows while the ERP stays responsible for authoritative business transactions.
Integration
CRM AI Integration
Connect AI with customer, lead, opportunity and service workflows inside your CRM platform.
Integration
AI API and Model Integration
Connect commercial or open models, custom AI services and internal applications through secure APIs.
Integration
Data Integration
Give AI applications the structured information they need through controlled services and your existing data platforms.
Integration
Application and Legacy System Integration
Embed AI into web apps, portals and enterprise software, including older systems that cannot be replaced.
Integration
AI Workflow Integration
Insert AI into defined process steps to classify, extract, summarize, predict or assist.
Implementation
Production Deployment
Deploy into the cloud, private, hybrid or supported on-premise environment your policies require.
Implementation
Testing and Validation
Test accuracy, permissions and failure behavior against real business data before go-live.
Implementation
Production Handoff
Deliver configuration, documentation, monitoring and operational ownership to the team that runs the system.
Capabilities and Platforms
AI Systems and Enterprise Platforms We Integrate
Six types of AI capability, connected to the ERP, CRM, data and service platforms your business already runs.
Language
LLM Applications
Connect language models with business applications and workflows. See LLM development services.
Retrieval
RAG Applications
Integrate retrieval with document repositories and permissions. See RAG development services.
Acting
AI Agents
Connect agents to approved APIs and tools with controlled action boundaries. See agentic AI development.
Predictive
Machine Learning Models
Deploy prediction, classification and forecasting models into live applications. See machine learning development.
Documents
Document AI
Connect extraction and document intelligence with ERP, finance and logistics workflows.
Generative
Generative AI
Embed text, image and multimodal generation into products. See generative AI development.
Platforms we connect to
OdooSAPOracleNetSuiteMicrosoft DynamicsSalesforceHubSpotZohoServiceNowSharePointPostgreSQLSQL ServerMySQLMongoDBSnowflakeDatabricksREST and GraphQL APIsWebhooksMCP-compatible interfacesn8n
Stack
AI Integration Technology Stack
Chosen for what the receiving systems support, not for what is newest.
Enterprise Applications
Where the business already works
OdooSalesforceSAPOracleMicrosoft DynamicsServiceNow
AI Models
Selected per workload, not per vendor
OpenAIClaudeGeminiLlamaMistral
Integration
How systems actually talk
REST APIsGraphQLWebhooksMCP-Compatible InterfacesEvent-Driven Services
Automation
The deterministic half of the workflow
n8nWorkflow ServicesPython AutomationNative Platform Workflows
Data
Where approved information lives
PostgreSQLMongoDBSnowflakeDatabricksSQL Serverpgvector
Engineering and Infrastructure
What it runs on
PythonNode.jsMicroservicesAWSAzureGoogle CloudDockerKubernetes
Architecture
Enterprise AI Integration Architecture
Production AI sits inside a controlled enterprise architecture, not beside it. Every result passes validation, business rules and, where needed, human approval before it reaches your system of record.
Business System Integration Layer AI Service Validation Business Rules Human Approval System of Record Monitoring
Control
Systems of Record Stay in Control
Your ERP, CRM and data platforms stay authoritative. AI prepares and validates, and only approved data reaches them.
Patterns
Integration Patterns
APIs, events, webhooks, middleware, native extensions or batch, chosen by what the receiving system supports.
Review
Human Review Where It Matters
Low-confidence outputs and important changes go to a person for approval before anything is written.
Security and Governance
Secure Enterprise AI Integration and Governance
AI integration adds systems and data flows to an application, so security covers the whole integration path and governance is part of implementation, not a separate document.
Identity and access
Authentication and Authorization
Approved identities for users, services and system-to-system connections, with application and business permissions applied before AI touches any information.
Endpoints and credentials
API Security and Secrets
Service endpoints, tokens and integration credentials are protected, and credentials stay out of prompts, application logs and client code.
Data and network
Encryption and Network Controls
Business information is encrypted in transit and at rest where required, and integration services run inside your enterprise network boundaries.
Evidence
Audit Logging
Relevant requests, system changes and approvals are recorded so every AI action can be traced.
Resilience
Safe Failure Modes
What happens when a provider, API or downstream system is unavailable is decided in advance, not found out in production.
Governance
Governance Built Into Implementation
Approved models and data sources, reviewed changes before rollout, defined human oversight, named operational owners and an incident process. See AI governance consulting services.
Company assurance
ISO 27001ISO 9001
Privacy and industry, where applicable
GDPRHIPAAPCI DSS
AI governance framework
NIST AI RMF
PortfolioAI Integration
AI Integration
in Production
Real implementations where AI was connected to existing applications and business workflows.
3 case studies
01 / 03 Case Study
ERP and AI Modernization SDLC Corp
Transworld Logistics
AI-assisted document processing placed in front of the ERP for fleet, warehouse, accounting and shipment workflows. Invoices and bills of lading are extracted, validated against operational records and reviewed before approved data moves into the ERP.
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.
48hr → 4hrDocument Turnaround
2,000+Documents Handled Per Day
−98%Processing Error Rate
100%Validated Before ERP Entry
02 / 03 Case Study
AI Inside ERP SDLC Corp
Odoo AI Assistant
An assistant built directly around the existing Odoo environment so employees get current ERP information in plain English. It uses Odoo's native ORM and role permissions, so existing access controls stay in place.
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.
4x FasterAccess to Odoo Records
65%Reduction in Manual Report Requests
18+Odoo Data Areas Connected
Odoo 18 & 19Supported Versions
03 / 03 Case Study
Connected AI Workflow Automation SDLC Corp
QuantusTechnik
One integration chain connecting website enquiries, sales workflows, machine information and management reporting. GPT-4o, Python, LangChain, PostgreSQL, pgvector and n8n run inside daily business workflows rather than as a separate chatbot.
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.
20 → 5 minLead Review Time
4hr → 45 minManager Report Prep
60% → 95%Follow-Up Visibility
3x FasterCampaign Segmentation
Client StoriesWhat Clients Say
What Clients Say
About SDLC Corp
Founders, CEOs, and operating leaders share what it's like to build with SDLC Corp.
Client story
Eric Leist
CEO, Edgerton Strategies
Client story
Doug Schmidt
CEO, Roofaid USA
Client story
Reyzal Razmi
All Star Influencers
What clients say
01 / 05
They've quickly understood our business context, moved fast, and brought recommendations rather than questions.
What stood out was their ability to translate our broad operational challenges into clear system features without overcomplicating the solution.
SDLC CORP's Odoo Developers delivered custom modules that fit our business like a glove. Efficient, responsive, and highly capable.
The predictive shipping models they implemented have saved us both time and cost while improving our accuracy and customer satisfaction.
Their data-driven methodology and exceptional command of artificial intelligence made a significant impact on our business operations.
What clients say
01 / 05
Their data-driven methodology and exceptional command of artificial intelligence made a significant impact on our business operations.
The predictive shipping models they implemented have saved us both time and cost while improving our accuracy and customer satisfaction.
SDLC CORP's Odoo Developers delivered custom modules that fit our business like a glove. Efficient, responsive, and highly capable.
What stood out was their ability to translate our broad operational challenges into clear system features without overcomplicating the solution.
They've quickly understood our business context, moved fast, and brought recommendations rather than questions.
Process
AI Implementation Process
Six stages that connect AI to your systems and end with someone on your side owning it.
01
Assess
Understand the target AI capability, existing applications, system ownership, data flows, APIs, security requirements, users and production constraints.
OutputSystem map and integration scope
02
Design
Define the integration architecture, data flow, validation, permissions, workflow rules and operational boundaries.
OutputArchitecture, data flow and rules
03
Connect
Build APIs, connectors, application services and the required enterprise integrations.
OutputWorking APIs and connectors
04
Validate
Test AI outputs, data mapping, permissions, workflow behavior, failure scenarios, human approvals and system changes.
OutputTest results and sign-off
05
Deploy
Release in stages: a controlled pilot, parallel operation with the existing workflow, human validation of outputs, then gradual expansion.
OutputLive production release
06
Operate
Monitor availability, latency, failure rate, human escalations, workflow completion, cost and downstream record integrity, then hand over with documentation and escalation procedures.
OutputDashboards, documentation and a named owner
Cost and Timeline
AI Integration Cost and Timeline
Cost depends on the number of systems, data quality, approval rules, security requirements and rollout scope. Figures are from our Clutch and GoodFirms profiles.
Project Size
$10,000 to $49,999
Most common size
The most common project size in Clutch reviews. The minimum project size is $10,000.
SourceClutch profile and reviews
Hourly Rate
$25 to $49
Per hour, on average
The average hourly rate listed on our Clutch profile.
SourceClutch profile
Timelines
9 to 34 weeks
For AI, ERP and platform work
Examples of delivered projects listed on our GoodFirms profile, from the shortest to the longest.
SourceGoodFirms profile
Remediation
Improve Existing AI Implementations
Already have AI in production but struggling with reliability or integration? We review the whole operating workflow, not only the model, then fix what is failing. If the use case itself is still unclear, define it first through AI consulting.
Symptom
Outside the Workflow
AI sits beside the core process. Staff copy and paste results, and business data is duplicated.
Our fixConnect it to the system of record.
Symptom
Fragile Connections
APIs break, retries are missing and downstream writes fail.
Our fixHarden the APIs, retries and write handling.
Symptom
Missing Controls
AI bypasses permissions, with no validation layer and no human approval.
Our fixAdd permissions, validation and approval steps.
Symptom
Poor Exception Handling
Edge cases fail, nothing monitors production, and model changes break workflows.
Our fixAdd exception paths, monitoring and change checks.
Symptom
Unclear Ownership
No one clearly owns the system, and support responsibility is unclear.
Our fixDefine ownership, documentation and escalation.
Symptom
Latency and Cost
Responses are too slow and costs are difficult to attribute.
Our fixTrace latency and cost to each workflow.
Why SDLC Corp
Why Choose SDLC Corp
For AI 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 connects to, including Odoo, Salesforce and Oracle NetSuite.
Proof
Products Running in Production
We build and operate our own products, including DAN for document processing, Pulastya AI voice agents and 100+ ERP marketplace connectors.
Assurance
ISO 9001 and ISO 27001 Certified
Certified to ISO 9001:2015 for quality management and ISO/IEC 27001:2022 for information security.
Ownership
You Own the Code
Clients keep 100% ownership of the source code and IP developed for their engagement, as set out in the project agreement.
Scope
Which AI Service Do You Need?
Consulting decides what is worth doing, development builds it, implementation connects it and workflow automation runs the rule-based steps. Most programs combine several. See AI consulting, AI development and workflow automation.
| Aspect | AI Consulting | AI Development | AI Integration and Implementation | Workflow Automation |
|---|---|---|---|---|
| Choose it when | The use case, roadmap or priorities still have to be decided. | The main requirement is to build a new AI capability or application. | The main requirement is to connect an AI capability to existing systems and make it work in production. | The process follows known rules. |
| Question it answers | What should we build, and where should AI be used? | What should it do? | How does it connect, and how is it deployed and operated? | Which steps follow fixed rules? |
| What you get | A roadmap and a view of which use cases have value. | A model or application that performs the task. | A business process that completes end to end. | Steps that run the same way every time. |
| Typical work | Use case selection, AI roadmap, value assessment. | Custom AI applications, predictive models, AI products, intelligent features, computer vision, custom automation. | API, ERP and CRM integration, workflow integration, validation, deployment, enterprise rollout, production monitoring. | Moving files, updating fields, notifications, fixed approvals, system synchronization. |
ResourcesAI Integration
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
DataEnterprise Data Integration Strategy
How to give AI applications controlled access to approved operational data.
Read the data integration guide FAQ
AI Integration FAQs
Answers on integration, deployment, systems of record, failure behavior, cost and timelines.
AI integration services connect artificial intelligence capabilities with existing software, data and business workflows.
This can include ERP, CRM, APIs, databases, document platforms, service systems and custom enterprise applications.
AI implementation services take an approved AI capability from prototype or design into a working production environment.
Implementation can include integration, configuration, validation, deployment, monitoring and operational handoff.
Yes. AI applications can connect with ERP systems, including Odoo, SAP, Oracle and Microsoft Dynamics, through APIs, native platform frameworks and controlled integration services. The approach depends on the platform, modules and available APIs.
The ERP should normally remain the authoritative system for important business transactions.
Yes. AI can support workflows such as enquiry classification, summarization, lead analysis, service routing and CRM record preparation, including in Salesforce through supported APIs.
The CRM remains the authoritative customer record. AI prepares the update rather than replacing the system.
Yes, when the existing system exposes a workable integration path.
Approaches may include APIs, middleware, database services, file-based interfaces or controlled automation, depending on the legacy environment.
Yes. Commercial and open models can be integrated through an application service layer that manages prompts, authentication, validation, business rules and downstream workflows.
Yes. Our Odoo AI Assistant uses native Odoo ORM access and role-based permissions to give plain-English access to current ERP records.
It connects 18+ Odoo data areas and reports 4x faster access to Odoo records and a 65% reduction in manual report requests.
Production integrations can use validation, deterministic business rules, confidence thresholds, permission checks, human approval and transaction controls before an AI result changes an authoritative system.
In the Transworld Logistics implementation, for example, extracted values are validated against operational records and only approved information moves downstream into the ERP.
Cost depends on the number of systems involved, data quality, validation and approval rules, security requirements and rollout scope.
Most Clutch reviews of SDLC Corp report a project size of $10,000 to $49,999, and the minimum project size is $10,000. A scoped estimate follows the assessment of your systems and workflow.
Timelines depend on the same factors as cost. Examples listed on our GoodFirms profile for AI, ERP and platform work range from 9 to 34 weeks.
The assessment stage confirms the timeline for your systems.
Get StartedImplement AI
Implement AI
Where Work Happens
Move AI out of isolated prototypes and into the ERP, CRM, data and applications your teams already use. Tell us the system and the workflow, and our AI implementation team will scope the integration.
- Contact Us
Let's Talk About Your Project
Get expert guidance on architecture, scope, timelines, and delivery approach so you can move forward with confidence.
What happens next?
- We review your requirements
- Strategy call with experts
- Clear roadmap & estimate
- NDA Protected
- Enterprise Grade Delivery
- Global Clients
