Discover
Define the business problem, users, data and success criteria.
AI Engineering Enterprise Ready
AI Development
Services
Build AI applications that solve real business problems and work inside your existing technology environment.
Our AI development services cover machine learning, language AI, generative AI, intelligent automation, computer vision, document intelligence and enterprise AI applications from solution design through production deployment.
Proven AI Delivery
300+AI Deployments
50+AI Projects Delivered
400+AI Specialists
98%On Time Delivery
Trusted by Fortune Global 500 leaders, governments & top universities across 30+ countries
Recognition
Award-Winning
AI Engineering
Recognized for enterprise AI delivery and custom artificial intelligence development.
Top AI Development Company by Selected Firms
Top AI App Developers by C2C Reviews
Top IT Consulting, SI & Managed Services Company by ITRate
Top Software Development Company by Selected FirmsTop AI Solutions Provider for Enterprises — 2025 Read the announcement
Reviewed on Clutch, GoodFirms, Selected Firms and DesignRush.
CapabilitiesCustom AI
Custom AI
Development Services
Build AI around your business workflows, data and users rather than forcing your operations into a generic platform.
Solutions AI Solutions
AI Solutions
We Build
AI applications designed around the decisions, information and workflows that matter to your business.
Forecast
Predictive AI
Use historical and operational data to forecast outcomes, identify risks and support better decisions.
Automate
Intelligent Automation
Automate repetitive business tasks, route requests and manage exceptions within existing workflows.
Assist
AI Assistants
Build customer-facing and internal assistants that help users work with information and software through natural language.
Extract
Document Intelligence
Extract, classify and validate information from invoices, forms, contracts and other business documents.
Decide
Decision Intelligence
Build decision-support tools that prioritize cases, recommend actions and flag exceptions for review.
Embed
AI-Enabled Products
Embed intelligent search, recommendations and automation directly into customer-facing software and digital products.
ProcessFrom AI Idea
From AI Idea
to Production
AI development should end with a system that works in production not only a prototype.
- 01
- 02
Design
Select the appropriate AI approach and define the application architecture.
- 03
Build
Develop the models, application services and required interfaces.
- 04
Integrate
Connect the solution with existing systems, data and workflows.
- 05
Evaluate
Test quality against representative business scenarios.
- 06
Deploy
Release the application into the required environment.
- 07
Improve
Monitor performance and refine the system using production evidence.
ArchitectureAI Development
AI Development
Architecture
A production AI application typically combines several layers.
Business Application Integration Layer AI Capability Data Validation Workflow Monitoring
Application Layer
Deliver AI through websites, enterprise software, mobile applications, internal portals or APIs.
AI Layer
Use the appropriate model or AI service for the specific workload.
Data Layer
Provide approved business information required by the application.
Integration Layer
Connect AI with CRM, ERP, databases, APIs and other operational systems.
Validation
Apply deterministic rules, confidence thresholds and human review where needed.
Monitoring
Track operational quality, latency, failures, usage and business outcomes.
By Function AI Development
AI Development
by Function
Apply AI to high-value workflows across business teams, from operations and finance to customer-facing products.
Optimize
Operations
Identify process bottlenecks, prioritize operational exceptions and improve resource planning.
Analyze
Finance
Support cash-flow forecasting, anomaly detection, financial reporting and invoice review.
Support
Customer Service
Assist with customer inquiries, classify support tickets and route complex requests to the right teams.
Convert
Sales
Improve lead qualification, account research, opportunity scoring and CRM updates.
Resolve
IT
Support IT ticket triage, incident summaries, service desk workflows and internal knowledge access.
Innovate
Product Teams
Deliver personalized recommendations, intelligent search and AI-assisted features within digital products.
By IndustryAI Development
AI Development
by Industry
Build AI for the operating requirements of your industry.
Finance
Financial Services
Fraud detection, risk scoring, document intelligence and controlled AI workflows. Payment workflows can be designed around applicable PCI DSS requirements.
Health
Healthcare
Administrative automation, document processing, intelligent assistance and operational AI. HIPAA-aligned controls can be applied where US healthcare data is involved.
Logistics
Logistics
Shipment intelligence, document automation, forecasting and operational decision support.
Industry
Manufacturing
Visual inspection, predictive maintenance, technical assistance and process intelligence.
Commerce
Retail and E-Commerce
Recommendations, forecasting, customer experience and generative content.
Impact
Nonprofits and NGOs
AI-enabled ERP, workflow automation, reporting and organizational productivity.
StackAI
AI
Technology Stack
Choose models, frameworks and infrastructure to fit the workload and deployment environment.
AI and Machine Learning
Where the modelling happens
PythonPyTorchTensorFlowscikit-learnHugging Face
Language AI
Model families we build on
OpenAIClaudeGeminiLlamaMistral
AI Engineering
How capability becomes an application
LangChainLangGraphFastAPINode.jsREST APIs
Data
Where business information lives
PostgreSQLMongoDBSnowflakeDatabricks
Infrastructure
Where it runs
AWSMicrosoft AzureGoogle CloudDockerKubernetes
Need to check security and compliance first?
Share your hosting, data and governance requirements and we will plan the AI work around them.
Review My Requirements Portfolio
AI Systems in Production
Explore AI projects delivered by SDLC Corp, from document intelligence and virtual staging to call routing and structured data extraction.
4 projects · scroll to reveal
01 / 04 Case Study
Document Intelligence SDLC Corp
Global Logistics
For Transworld Logistics, an AI document-processing workflow extracts invoice and shipping-document data, flags fields for human validation and sends approved records into enterprise systems. The published case study reports processing time falling from 48 hours to 4 hours.
Document Extraction Validation Enterprise Operations
- Unstructured PDFs
- Key field detection
- Vendor information
- Dates and amounts
- Human-in-the-loop validation
- ERP integration
02 / 04 Case Study
AI Virtual Staging SDLC Corp
Interior Design Platform
A generative AI virtual-staging platform lets users explore furniture, flooring, wall finishes and lighting in photographs of real rooms. The workflow combines scene understanding, scale-aware placement and image generation.
Room Photo Scene Understanding Real-Scale Placement Generated View
- Image understanding
- Inch-accurate scale
- Furniture placement
- Wall and floor finishes
- Lighting
- Usage-based rendering
03 / 04 Case Study
Natural Language Call Intake SDLC Corp
Pulastya AI
In a call-intake deployment, Pulastya AI interprets caller intent, answers using client-approved documents and routes requests to the appropriate team. Human transfers include the conversation transcript and summary.
Conversation Intent Structured Capture Routing Handoff
- Natural-language understanding
- Intent classification
- Structured request capture
- Caller-context extraction
- Workflow routing
- Human handoff
04 / 04 Case Study
Document AI Platform SDLC Corp
Data AI Ninja
Data AI Ninja classifies uploaded documents, extracts structured fields and uses confidence scores to identify results that may require human review before downstream use.
Upload Classification Extraction Confidence Review Structured Output
- Document classification
- Language detection
- Field detection
- Confidence scoring
- Reviewer validation
- Audit log
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 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.
What clients say
01 / 05
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.
By the Numbers
AI Engineering
at Scale.
Delivery capacity behind the AI work.
Drag to spin
3,400+
Projects Delivered
across 12 industries
400+
AI Specialists
Top 1% global talent
1,200+
Global Engineers
across 6 continents
30+
Countries Served
global regulatory regimes
300+
AI Deployments
in production
10+ Years
of Experience
in AI and software
Why SDLC CorpWhy Choose SDLC Corp
Why Choose SDLC Corp
for AI Development
One team for the model, the application and the systems it has to live inside.
End to End
End-to-End AI Engineering
Build the model, application, workflow and integration as one production system.
Depth
Specialist AI Teams
Use dedicated expertise across machine learning, language AI, RAG, agents, vision and generative AI.
Connected
Enterprise Integration
Connect AI with the software and systems already running your business.
Right-Sized
Model Flexibility
Select commercial, open or custom models according to the workload.
Measured
Production Evaluation
Measure AI against representative business scenarios before and after deployment.
Oversight
Human Oversight
Keep people involved where judgment, authorization or accountability is required.
Security, Privacy and Responsible AI
Company AssuranceSOC 2 Type II · ISO 27001 Certified · ISO 9001 Certified
Privacy and Industry RequirementsGDPR · HIPAA · PCI DSS, where applicable
AI Governance FrameworksNIST AI RMF · ISO/IEC 42001 principles · ISO/IEC 23894 guidance
Applicability depends on geography, industry, the data processed, the deployment model, the use case and your own legal and regulatory obligations.
Ready to start your AI project?
Talk to our AI team about scope, timelines and the right first step.
Get a Free Consultation Project Planning AI Development Cost
AI Development Cost
and Timeline
AI development budgets and timelines depend on project scope, data readiness and integration needs. Compare common delivery stages to plan your investment.
AI Proof of Concept
Validate one AI use case using representative business data before committing to a larger build.
- Data feasibility assessment
- Working AI prototype
- Initial performance evaluation
Production AI MVP
Deploy a focused AI application with essential workflows, integrations and operational controls.
- AI application development
- Core business integrations
- Testing and deployment controls
Enterprise AI Platform
Scale AI across departments with enterprise integrations, governance and production monitoring.
- Multi-system AI integration
- Enterprise security and governance
- Monitoring and lifecycle management
What Affects AI Development Cost?
Data quality and availability
Model type and complexity
Integration requirements
Security and compliance scope
Infrastructure and usage
Maintenance and monitoring
Cost and timeline ranges are illustrative planning estimates, not fixed SDLC Corp quotations. Actual scope and pricing require project assessment. Infrastructure, model usage and ongoing support may incur additional costs.
Before You BuildNeed AI
Need AI
Strategy First?
If the use case, architecture or investment priority is not yet clear, start with consulting before committing to implementation.
Strategy
AI Consulting Services
Assess opportunities, readiness and priorities before committing to a build.
Explore AI Consulting ServicesProgramme
Enterprise AI Development
For organization-wide AI programs involving multiple teams and systems.
Explore Enterprise AI DevelopmentConnect
AI Integration & Implementation
For an already-defined capability that needs connecting to existing systems.
Explore AI Integration & Implementation ResourcesAI Development
Explore all articlesAI Development
Resources
Plan data readiness, production deployment and ongoing model monitoring.
ProductionHow to Move AI Pilots into Production
A practical sequence for taking a working pilot through to a system that runs reliably in production.
Read Article
Data ReadinessEnterprise AI Data Readiness and Production Planning
How to judge whether your data, governance and integration are ready before an AI system goes live.
Read Article
MonitoringAI Model Monitoring in Production: Drift, Bias & Alerts
What to watch once a model is serving real traffic, from drift and bias to alerting thresholds.
Read Article Get StartedBuild Your
Build Your
AI Solution
Turn a business problem into a production AI application that works with your users, data and existing systems.
Whether you need predictive models, language AI, intelligent automation, computer vision or a custom AI product, our engineering team can take the project from architecture through deployment.
- 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
FAQ AI Development Services
AI Development Services
FAQs
Answers about AI development costs, timelines, technology, integration and deployment.
AI development services involve designing, building, integrating and deploying artificial intelligence applications for specific business needs. They can include machine learning, generative AI, computer vision and intelligent automation.
AI development costs vary by project scope, data readiness, model complexity, integrations and deployment requirements. A proof of concept generally requires less investment than an enterprise platform. Accurate pricing requires a project assessment.
A focused proof of concept may take several weeks, while production applications and enterprise platforms often require months. Timelines depend on data access, development scope, integration complexity, testing and security reviews.
AI consulting helps organizations evaluate opportunities, feasibility and technology choices. AI development turns a defined use case into a working application through engineering, testing and deployment. Explore our AI Consulting Services.
AI development creates or customizes an AI capability and its application. AI integration connects that capability to existing software, data sources and business workflows. Many enterprise projects require both.
No. Depending on the task, traditional machine learning, computer vision, specialized models or rule-based systems may be more suitable. Technology selection should follow the business requirements.
Yes, provided the required data is accessible, suitable for the intended use and approved for processing. Data quality, permissions and security requirements are assessed before implementation.
Yes. AI applications can connect with ERP, CRM and other enterprise systems through supported APIs and integration services. Available functionality depends on each platform's interfaces and access permissions.
Yes. Depending on the selected models and infrastructure, AI applications may run in private cloud, customer-managed or supported on-premises environments.
Evaluation uses metrics relevant to the task, such as prediction accuracy, extraction quality, response correctness, latency, task completion and human escalation. Testing should include representative data and failure cases.
Yes. Existing AI applications can be assessed for model quality, latency, operating costs, integrations and production reliability. Improvements depend on the findings.
Begin with a defined business problem, measurable goals and available data. The next steps are feasibility assessment, solution design, development and validation before deployment.
