AI Application Development Services You Can Rely On
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How Kanerika Delivers AI Application Development Services
Kanerika handles the full scope of AI application development services, from defining the right use case to keeping the application accurate and governed in production.
Data Maturity Assessment
- Score data infrastructure, pipelines, and governance maturity
- Benchmark current state against industry maturity baselines
- Surface gaps across data, tooling, and skills
Discovery and Use Case Definition
- Map AI to business outcomes
- Prioritize by value and risk
- Scope the right problem first
Data Architecture and Preparation
- Connect and clean source data
- Build pipelines for AI readiness
- Ensure data quality at ingestion
AI Model Development and Training
- Build and fine-tune AI models
- Validate against real business data
- Test for accuracy before deployment
Application Design and Development
- Design workflows around users
- Build APIs and application layers
- Integrate with existing enterprise systems
Governance and Security Setup
- Apply role-based access controls
- Log every decision for audit
- Map controls to compliance requirements
AI Application Development, Matched to Your Scope and Scale
Not every AI application starts the same way. Pick the engagement model that matches where you are and what you need delivered.
Advisory and Architecture
- Map AI use cases to business goals
- Map AI use cases to business goals
- Deliver an implementation roadmap
End-to-End Development
- Build, test, and deploy your AI application
- Integrate with existing data systems
- Ensure governance and security
Managed AI Operations
- Monitor application performance
- Retrain and refine models as data evolves
- Handle incidents, updates, and support
AI Application Development Services Delivering Real Outcomes
See how enterprises across regulated industries use Kanerika-built AI applications to cut costs, reduce risk, and move faster.
AI/ML & Gen AI
Enhancing Brand Compliance and Approval Workflows with Conversational AI
Impact:
- 35% Higher Brand Compliance Rate
- 60% Lower Approval Turnaround Time
- 70% Less Manual Effort Per Brand Query
AI/ML & Gen AI
Enhancing Compliance Oversight with an AI-Powered Regulatory Management Platform
Impact:
- 40% Faster Regulatory Response
- 60% Less Manual Compliance Work
- 5X Better Audit Traceability
AI/ML & Gen AI
60% Faster Invoice Processing with Intelligent Automation by FLIP
Impact:
- 75% Reduction in Manual Effort
- 90% Data Extraction Accuracy
- 55% Faster Invoice Processing
Our IMPACT Methodology for AI Application Development
A structured delivery framework that takes AI applications from initial scoping through deployment with governance, integration, and compliance built into every phase.
Technologies We Work With
AI applications built on the platforms your enterprise already runs, from Azure and Databricks to TensorFlow, PyTorch, and Microsoft Fabric.
INNOVATE
Data Strategy Consulting Tuned to Your Industry
Why Choose Kanerika for AI Application Development?
A decade of enterprise AI delivery, several production agents live, and partner-level platform expertise across every major data stack.
Several AI agents deployed across regulated industries. Real deployments, not proof-of-concept builds.
Certified expertise across Microsoft’s Data and AI stack means your AI applications are built on enterprise-grade architecture.
A structured delivery model that takes AI applications from use case to live deployment without the typical delays.
Empowering Alliances
Our Strategic Partnerships
The pivotal partnerships with technology leaders that amplify our capabilities, ensuring you benefit from the most advanced and reliable solutions.
Frequently Asked Questions (FAQs)
01What is custom AI application development?
It is the process of designing and building AI-powered software around a specific organization’s workflows, data, and objectives, as opposed to configuring an off-the-shelf product. The distinction matters because enterprise environments involve integration complexity, governance requirements, and data conditions that generic tools are not built to handle.
02What data do we need before starting?
It depends on what you are building. LLM-based applications require accessible, structured knowledge data, not necessarily large volumes. Predictive ML models require sufficient labeled historical data. Computer vision systems require annotated image or video data relevant to the detection task. Kanerika’s discovery process includes a data readiness assessment to identify what is present, what is missing, and what needs to be addressed before development begins
03When should we use RAG versus fine-tuning?
RAG is faster to implement, easier to update as your data changes, and appropriate for most enterprise knowledge retrieval use cases. Fine-tuning is better suited to applications requiring specific output formats, deep domain terminology, or tone consistency that cannot be achieved through prompting alone. For most enterprise deployments, RAG is the right starting point. Kanerika advises on the decision based on the specific use case requirements.
04What is AI copilot development?
An AI copilot is a role-specific assistant built to work within a defined business context, a finance copilot that interprets financial data, a sales copilot that surfaces deal intelligence, an HR copilot that handles employee queries. Kanerika builds copilots on Microsoft Copilot Studio and custom architectures, connected to your enterprise data and deployed within your existing tooling.
05How long does AI application development take?
A PoC or MVP typically takes four to eight weeks. A production-ready application with integrations and MLOps setup generally takes three to six months, depending on complexity, data readiness, and integration scope.
06Can Kanerika integrate AI into our existing ERP or CRM?
Yes. Integration into Dynamics 365, Salesforce, SAP, and other enterprise platforms is a standard part of Kanerika’s development practice. The objective is to bring AI capability into the systems your teams already use, not require adoption of parallel tooling.
07What happens after deployment?
We establish MLOps and LLMOps pipelines covering performance monitoring, drift detection, and model retraining schedules. AI systems that are not actively managed degrade over time. Post-deployment operations are a defined part of the engagement, not an optional add-on.
08How does Kanerika handle security and compliance in AI development?
Security and compliance requirements are captured in discovery and designed into the architecture from the outset. For organizations on Azure, this includes Microsoft’s enterprise security framework covering data residency, access controls, and audit logging. Kanerika holds ISO 27001, ISO 27701, SOC 2, and GDPR compliance certifications.
09What is AI model validation and when is it needed?
Model validation is an independent assessment of an existing AI system’s accuracy, fairness, and compliance — evaluating whether the model performs as intended across real-world conditions and meets current regulatory standards. Organizations need it when deploying AI in regulated environments, when inheriting AI systems through acquisition, or when existing models have not been formally reviewed against current governance requirements.
09What engagement model works best for a first AI project?
For a first engagement, project-based is typically the right structure, a defined scope, a PoC or MVP milestone, and a clear handover point. It limits commitment while generating real evidence of what AI can deliver in your environment. Once that foundation exists, a dedicated team or augmentation model becomes easier to justify internally.
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