Automate time-consuming manual workflows
200+
AI Projects Delivered
60%
Average Reduction in Manual Workflow Time
4 Weeks
Average Time to First Working Prototype
Trusted By 600+ Brands
What Is Generative AI and Why Businesses Are Adopting It Now
Generative AI is an artificial intelligence approach that can create content, including text, images, code, and structured data, by learning patterns from existing information. Unlike traditional AI built to classify or predict, generative models can produce original outputs at a speed and scale no human team can match. Businesses that use it well automate knowledge work, accelerate product development, and serve customers more effectively.
WHY COMPANIES ARE ADOPTING GENERATIVE AI:
Deliver personalized customer experiences at scale
Accelerate content, code, and product development
Extract insight from unstructured data
Custom Generative AI Development Services
Built for Business Impact
A Trusted Generative AI Development Company Delivering Scalable AI Solutions Across Industries
Generative AI Consulting Services
Many businesses know generative AI can help but are unsure where to start or which use cases to prioritise. Our generative AI consulting services work through your data environment, identify where AI delivers measurable value, and produce a clear implementation roadmap. The outcome is a strategy built around your actual business context, not a template applied from the outside.
LLM Development and Fine-Tuning
Foundation models are built for general use. Most enterprise applications need something more specific. We develop and fine-tune large language models using your own data, aligning model behaviour to your products, terminology, and industry. Whether refining an existing model or building a purpose-specific one, the result is an AI system that performs consistently in your context rather than approximating it.
AI Chatbot and Virtual Assistant Development
Poorly designed AI chatbots frustrate users. Well-designed ones reduce support costs and handle routine interactions without human involvement. We build AI chatbots grounded in your actual product and policy data, with tone consistency and clear escalation paths. The experience feels genuinely useful to users rather than scripted, which makes a measurable difference to adoption and satisfaction.
Retrieval-Augmented Generation Development
Standard generative AI cannot reliably reference your internal documents, databases, or proprietary knowledge. RAG solves this by connecting AI reasoning to your actual data sources in real time. We design RAG architectures that ground AI responses in current, accurate, company-specific information, which is essential for knowledge bases, compliance applications, and customer-facing AI that needs to reflect your reality
AI Agent and Workflow Automation
AI agents go beyond answering questions. They plan, take action, use tools, and complete multi-step tasks autonomously. We build AI agent systems that handle complex business workflows, from document processing and data enrichment to research tasks and approval chains, with the reliability and auditability that production environments require. For knowledge-intensive processes, this is one of the highest-value applications available today.
Enterprise Generative AI Development Services
Enterprise AI development involves requirements consumer-grade tools cannot meet, including data privacy, compliance, access control, and integration with complex internal systems. Our enterprise generative AI development services address these as starting conditions, not afterthoughts. We build secure, governable AI systems that work within your infrastructure and meet the compliance obligations relevant to your industry.
Generative AI Integration into Existing Products
Most organisations need AI capabilities embedded into platforms their teams already use, not a separate tool added alongside them. We integrate generative AI into your existing CRM, CMS, ecommerce platform, or internal applications. The integration adds genuine capability without disrupting established workflows or requiring teams to change how they work, which significantly improves adoption rates.
AI Proof of Concept and MVP Development
Before committing significant budget to a full build, it makes sense to validate that the approach works with real data. Our proof of concept and MVP development gives you a working AI prototype in weeks, built around a real use case. This lets you test the concept with actual users, gather meaningful feedback, and decide whether to scale based on evidence rather than assumption.
Why Commerce Pundit Is the Preferred Generative AI Development Company for 600+ Brands
A Trusted Partner for Building AI That Performs in Production, Not Just in Demos
Business-First AI Strategy
We start every engagement with your business objectives. Our gen AI development approach only recommends solutions justified by a clear, measurable business case.
End-to-End Delivery Capability
From discovery and architecture through model development, integration, and post-launch optimisation, one team manages the full lifecycle without handoffs.
Production-Grade Engineering Standards
We build AI systems for production from day one, including error handling, monitoring, scalability, and security, rather than adding them after the fact.
Deep Integration Experience
Our team connects AI capabilities to complex technology environments, including ecommerce platforms, ERPs, CRMs, and enterprise data infrastructure without creating fragile dependencies.
Data Privacy and Compliance by Design
Privacy and compliance are built into the architecture from the start, covering data handling, access controls, audit trails, and on-premises deployment where required.
Long-Term Optimisation Partnership
Beyond delivery, we monitor AI performance, retrain models as needed, and expand AI capability as your organisation builds confidence and understanding of what works.
Real Results Delivered Across Industries
Retail & eCommerce
AI Knowledge Assistant for a Multi-Brand Ecommerce Operation
Challenge
Customer support agents were manually searching disconnected systems to answer queries, slowing resolution times and making new agent onboarding take weeks.
Solution
We built a RAG-based AI knowledge assistant trained on the company's full documentation library and policy content, giving agents instant, accurate, cited answers without manual searching.
Reduction in Average Query Handling Time
68%
Cut from New Agent Onboarding Timeline
4 Weeks
Decrease in Escalations to Senior Support Staff
41%
B2B SaaS
Generative AI Content Pipeline for a B2B SaaS Marketing Team
Challenge
The marketing team needed to scale content output significantly without growing headcount. Generic AI tools produced off-brand writing that needed near-complete rewrites before publishing.
Solution
We developed a custom generative AI content pipeline fine-tuned on their existing high-performing content, capturing their brand voice and integrating directly into their CMS workflow.
Increase in Monthly Content Output
5x
Reduction in Time Spent per Content Piece
70%
Improvement in Organic Traffic Within 6 Months
32%
Logistics & Supply Chain
AI Document Processing for a Logistics and Freight Company
Challenge
The operations team was manually processing hundreds of shipping documents daily. Data entry errors were causing compliance issues and the team was scaling headcount just to keep pace.
Solution
We built an AI document processing system using generative AI and OCR to extract, validate, and push structured data directly into the company's TMS and ERP, eliminating manual entry entirely.
Reduction in Manual Document Processing Time
85%
Data Extraction Accuracy on Standard Documents
99.2%
Estimated Annual Saving in Operations Labour
$420K
Our Generative AI Development Process
A Structured Approach That Takes You from Use Case Identification to Production AI Without the Guesswork
Discovery and Use Case Definition
We begin by understanding your business, your data environment, and the problems you want AI to solve. The goal is identifying use cases where generative AI will genuinely help, not just where it could theoretically be applied. The outcome is a prioritised list with a clear rationale for each.
AI Strategy and Architecture Design
We design the technical architecture, selecting appropriate foundation models, determining whether fine-tuning is required, and planning the data pipeline and integration points with your existing systems. The architecture is reviewed and agreed with your team before any development begins.
Proof of Concept Development
Before investing in a full production build, we develop a working proof of concept using real data. This validates the technical approach, gives stakeholders something concrete to evaluate, and surfaces obstacles early when they are significantly easier to address.
Full Development and Integration
Development runs in structured sprints with regular reviews. We build the AI system, integrate it with your platforms and data sources, and iterate based on your feedback throughout. Scope can be adjusted as understanding of the application develops through testing.
Testing, Safety, and Quality Assurance
We test for output accuracy, consistency, edge case behaviour, and the failure modes specific to AI systems. For regulated industries, we also validate against compliance and data handling requirements before any deployment to users or production systems.
Deployment, Monitoring, and Optimisation
Post-launch, we deploy monitoring to track AI performance over time, identify output quality issues, and collect data for retraining. Most AI systems improve noticeably in the weeks after launch as real usage patterns inform further refinement of the models.
AI Models, Frameworks, and Infrastructure We Work With
From Foundation Model Selection to Production Deployment Across the Full Generative AI Stack
Foundation Models and LLMs
LLM Orchestration Frameworks
Vector Databases and RAG Infrastructure
Model Fine-Tuning and Training
AI Observability and Evaluation
Cloud and Deployment Platforms
Integration and Automation
Document and Data Processing
Frequently Asked Questions About
Our Generative AI Development Services
What are generative AI development services?
Generative AI development services cover the end-to-end work of designing, building, and deploying AI systems that create content, automate workflows, and interact intelligently with data. This includes strategy, model selection, fine-tuning, system integration, and ongoing optimisation. A gen AI development company like Commerce Pundit manages this full process, helping businesses move from an AI idea to a reliable, production-ready system.
How is generative AI different from traditional machine learning?
Traditional machine learning models classify, predict, or rank within defined categories, such as flagging spam or forecasting demand. Generative AI produces original outputs, including written content, code, answers, and structured data. This makes it applicable to a far wider range of business tasks, particularly those involving knowledge work, communication, and content production that were previously difficult to automate.
What types of businesses benefit most from generative AI development solutions?
Generative AI adds practical value in almost any business dealing with large volumes of text, documents, data, or customer communication. It is particularly impactful for ecommerce operators, SaaS companies building AI product features, professional services firms with document-heavy workflows, logistics and operations teams processing high data volumes, and any organisation where content creation or knowledge management currently consumes significant manual time.
What is the difference between a generic AI tool and a custom generative AI solution?
Generic tools like ChatGPT are designed for broad, general use. They do not know your products, policies, or business logic. Custom generative AI development services produce systems built around your actual data and workflows, so outputs are more accurate, more consistent, and more useful in your context. They also integrate with your existing systems rather than requiring manual data transfer to be useful.
What is RAG and when is it needed?
RAG, or retrieval-augmented generation, connects a language model to your own data sources, allowing it to generate responses grounded in your current, company-specific content. You need it when your AI application must reference internal documents, product databases, or proprietary knowledge. Without RAG, AI responses draw from general training data and cannot accurately reflect your specific products, policies, or operational context.
How long does a generative AI project typically take?
A proof of concept for a single, well-defined use case generally takes four to six weeks. A full production application takes three to six months depending on complexity and integrations required. Enterprise generative AI development projects with multiple use cases and compliance requirements are structured in phases. We provide a detailed timeline after the discovery phase, once scope is clearly defined.
How do you handle data privacy and security?
Privacy and security are built into the architecture from the start. Depending on your requirements, this includes private cloud or on-premises deployment, strict access controls, audit logging, and compliance with relevant data protection regulations. For healthcare, finance, and other regulated sectors, we design specifically around your compliance obligations rather than treating them as constraints to work around after the fact.
What is generative AI consulting and when should I consider it?
Generative AI consulting is the process of identifying where AI can create business value, assessing technical feasibility given your current systems, and building a structured roadmap for implementation. It is most useful before development begins, particularly if leadership needs a clear picture of what is realistic and what outcomes are achievable before committing budget. Our generative AI consultancy engagements are designed to give you that clarity efficiently.
Can generative AI integrate with our existing software?
Yes. Integration is one of our core capabilities. We connect generative AI systems with Shopify, Magento, Salesforce, HubSpot, SAP, custom ERPs, and a wide range of internal platforms. The goal is to make AI a natural extension of the tools your team already uses, not a separate system requiring additional context switching or manual data transfer to be useful in practice.
How do we get started with Commerce Pundit's generative AI development services?
The process begins with a consultation where we discuss your business objectives, where AI can help, and your current data and systems. From there, we recommend a starting point, whether a focused consulting engagement, a proof of concept, or a full build, with a realistic scope and timeline. There is no obligation at this stage, and many clients find the initial conversation clarifying before any work begins.