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

Powered by leading cloud & AI platforms

AWS
Google Cloud
Microsoft Azure
NVIDIA
OpenAI
Anthropic
Gemini
Grok
Perplexity
Google AI
AWS
Google Cloud
Microsoft Azure
NVIDIA
OpenAI
Anthropic
Gemini
Grok
Perplexity
Google AI
Recognition

Award-Winning
AI Engineering

Recognized for enterprise AI delivery and custom artificial intelligence development.

Top AI Development Company by Selected FirmsTop AI Development Company by Selected Firms
Top AI App Developers by C2C ReviewsTop AI App Developers by C2C Reviews
Top IT Consulting, SI & Managed Services Company by ITRateTop IT Consulting, SI & Managed Services Company by ITRate
Top Software Development Company by Selected FirmsTop Software Development Company by Selected Firms

Top AI Solutions Provider for Enterprises — 2025 Read the announcement

Reviewed on Clutch, GoodFirms, Selected Firms and DesignRush.

Capabilities

Custom AI
Development Services

Build AI around your business workflows, data and users rather than forcing your operations into a generic platform.

01 / 08
Solutions

AI Solutions
We Build

The shapes an AI project usually takes once the business problem is clear.

Forecast

Predictive AI

Use historical and operational data to forecast outcomes, identify risk and support better decisions.

Automate

Intelligent Automation

Use AI to classify, extract, analyze or interpret information inside existing business 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

Combine predictive models, business rules and human oversight to support operational decisions.

Embed

AI-Enabled Products

Add artificial intelligence directly into customer-facing software and digital products.

Process

From AI Idea
to Production

AI development should end with a system that works in production not only a prototype.

  1. 01

    Discover

    Define the business problem, users, data and success criteria.

  2. 02

    Design

    Select the appropriate AI approach and define the application architecture.

  3. 03

    Build

    Develop the models, application services and required interfaces.

  4. 04

    Integrate

    Connect the solution with existing systems, data and workflows.

  5. 05

    Evaluate

    Test quality against representative business scenarios.

  6. 06

    Deploy

    Release the application into the required environment.

  7. 07

    Improve

    Monitor performance and refine the system using production evidence.

Architecture

AI Development
Architecture

A production AI application typically combines several layers.

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
by Function

Where AI tends to earn its place first inside an organization.

Operations

Operations

Automate document-heavy tasks, exception handling, classification and operational analysis.

Finance

Finance

Support forecasting, risk analysis, reporting and intelligent document workflows.

Service

Customer Service

Build conversational support, request routing and knowledge-assisted service applications.

Sales

Sales

Support lead qualification, account research, summaries and CRM workflows.

IT

IT

Use AI for internal support, service management and enterprise knowledge access.

Product

Product Teams

Add AI capabilities directly to software products and customer experiences.

By Industry

AI Development
by Industry

Sector patterns we build against most often.

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.

Stack

AI
Technology Stack

Chosen for the workload, not for the newest model on the market.

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
Portfolio

AI Systems
in Production

Real AI systems built around operational workflows and business outcomes.

4 entries · scroll to reveal
01 / 04 Case Study
Document Intelligence SDLC Corp

Global Logistics

An AI document-processing workflow extracts information from invoices and bills of lading, validates important fields and sends approved results into enterprise operations.

  • Unstructured PDFs
  • Key field detection
  • Vendor information
  • Dates and amounts
  • Human-in-the-loop validation
  • ERP integration
Explore the Case Study
Transworld Logistics document processing platform extracting fields from unstructured invoice PDFs
02 / 04 Case Study
AI Virtual Staging SDLC Corp

Interior Design Platform

A generative AI application allows users to visualize furniture, flooring, wall finishes and lighting inside real rooms.

The production pipeline combines image understanding, real-scale visualization and generative models.

  • Image understanding
  • Inch-accurate scale
  • Furniture placement
  • Wall and floor finishes
  • Lighting
  • Usage-based rendering
Explore the Case Study
AI interior staging platform rendering furniture and finishes inside a real room photograph
03 / 04 Product
Natural Language Call Intake SDLC Corp

Pulastya AI

AI understands caller intent, captures relevant context and routes conversations into defined business workflows while preserving human handoff.

  • Natural-language understanding
  • Intent classification
  • Structured request capture
  • Caller-context extraction
  • Workflow routing
  • Human handoff
Explore Pulastya
Pulastya AI call intake showing the detected intent and the matched queue before routing
04 / 04 Product
Document AI Platform SDLC Corp

Data AI Ninja

A document-intelligence platform classifies uploaded files, extracts structured information and provides confidence-based review workflows.

  • Document classification
  • Language detection
  • Field detection
  • Confidence scoring
  • Reviewer validation
  • Audit log
Explore Data AI Ninja
Data AI Ninja document extraction console showing document type, detected language and per-field confidence
Client Stories

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 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 Certified · 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.

Get Started

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

Share a few details about your project, and we’ll get back to you soon.

Let's Talk About Your Project

FAQ

AI Development Services
FAQs

Straight answers on scope, technology choice, integration and how an AI project actually starts.

AI development services cover the design, engineering, integration and deployment of custom artificial intelligence applications.

Projects can include machine learning, language AI, computer vision, generative AI, intelligent automation and other AI capabilities.

We build predictive systems, AI assistants, document intelligence applications, conversational AI, computer vision systems, generative AI products and enterprise AI workflows.

Custom AI development means designing an AI application around a specific business problem, workflow, data environment and user requirement rather than using a generic off-the-shelf tool.

AI consulting helps determine what should be built and why.

AI development turns the approved use case into a working application. See our AI Consulting Services.

AI development focuses on building the AI capability.

AI integration focuses on connecting that capability with existing business systems and production workflows. See our AI Integration & Implementation Services.

No. Many AI projects are better solved with traditional machine learning, computer vision, rules or specialized models.

The technology should follow the business requirement.

Yes. AI applications can use approved enterprise data when the required information is accessible, sufficiently reliable and appropriate for the intended use.

Yes. AI applications can connect with enterprise platforms through supported APIs, application services and native integration mechanisms.

Yes. Depending on the workload and models involved, AI applications can use managed cloud services, private cloud, customer-managed infrastructure or supported on-premise environments.

Evaluation depends on the use case.

Measures can include prediction accuracy, extraction accuracy, response quality, latency, task completion, human escalation and business outcomes.

Yes. We can review model performance, application architecture, data, integration, evaluation, latency, cost and production behavior to identify where improvements are needed.

Start with a clearly defined business problem and measurable outcome.

From there, the project can assess data, choose the appropriate AI approach, define the architecture and validate the solution before production rollout.