AI Application and Generative AI Development
Put large language models and machine learning to work inside your applications, grounded in your own data, tested against real cases and governed for production.
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Also known as Generative AI development, LLM application development, AI agent development
- Use-case selection
- RAG on your content
- AI agents
- Document AI
- AI in existing apps
- Evaluation and LLMOps
- Generative AI development
- LLM application development
- AI agent development
- Retrieval-augmented generation (RAG)
- AI copilots
- Machine learning integration
- Software Development ConsultingAdvisory
- Enterprise Application DevelopmentApplication
- API Development and Application IntegrationEngineering
What is AI application development?
AI application development builds software that uses artificial intelligence, such as large language models (LLMs) and machine learning, to answer questions, draft content, extract data from documents, make predictions or take actions. Common patterns include retrieval-augmented generation (RAG), which grounds answers in an organization's own documents, AI agents that call tools and APIs to complete tasks, and copilots embedded in existing applications. TechTiera builds AI applications with evaluation, security and human oversight designed in.
Reviewed 2026-10-27 by TechTiera's Custom Software Development practiceAI Application Development at a glance
- Patterns
- Retrieval-augmented generation (RAG), AI agents and tool calling, copilots, document extraction, classification, prediction
- Models
- Commercial models through Azure AI Foundry, Amazon Bedrock and Google Vertex AI, plus open-weight models
- Data
- Content in SharePoint, OpenText, Documentum, Alfresco, databases and data platforms
- Quality
- Evaluation test sets, accuracy and groundedness scoring, red-team testing, monitoring in production
- Governance
- Access control at retrieval, data protection, prompt-injection defenses, human review and audit logs
What does TechTiera's AI application development service include?
Consulting, design, engineering, testing and support from one accountable team.
Use-case selection
AI opportunities ranked by value, data readiness and risk, with a baseline to beat.
RAG on your content
Assistants that answer from your documents and records, with citations and permissions respected.
AI agents
Agents that call your APIs to complete tasks, with clear limits and hand-off to people.
Document AI
Extraction and classification of invoices, forms and contracts, built with TechTiera's capture practice.
AI in existing apps
Search, summaries, drafting and predictions added to applications you already run.
Evaluation and LLMOps
Test sets, scoring, monitoring and cost control so quality holds after launch.
How TechTiera approaches AI application development
Getting AI from pilot to production
- Pick a narrow job with a measurable result, such as answering policy questions or extracting invoice fields.
- Prepare the data: clean, current content with permissions that carry into retrieval.
- Build an evaluation set of real questions and correct answers before choosing a model.
- Design guardrails: what the AI may and may not do, and when a person takes over.
- Monitor in production: accuracy, cost, latency and user feedback reviewed every release.
Why content expertise matters for AI
Most enterprise knowledge sits in document systems. TechTiera has long run content management practices for OpenText Documentum, Microsoft 365 and SharePoint, Alfresco and capture platforms, so AI assistants are built on well-structured, well-governed content rather than whatever can be scraped together.
Typical use cases
- Policy and knowledge assistants
- Contract and document review
- Customer service copilots
- Invoice and form extraction
- Sales and proposal drafting
- Engineering document search
How is the work delivered?
Agile and Design Thinking combined: short sprints, working software every two weeks and users involved throughout.
- DiscoverRequirements gathering with users and owners.
- MapAnalysis and journey and process mapping.
- AssessTechnology capability assessment and architecture.
- Design and buildDesign Thinking and Agile sprints with demos every two weeks.
- ReleaseAutomated testing, CI/CD and controlled launch.
- Run and improveMonitoring, maintenance and a prioritized backlog.
Why choose TechTiera for AI application development?
Architects and consultants with large-program experience frame the problem before developers write code.
TechTiera delivers custom code, low-code (Mendix, Power Platform, Appian) and packaged platforms, so the recommendation fits your need.
Every build ships with source code in your repository, tests, pipelines and documentation.
In-house SAP, Microsoft, CRM, integration and content management practices, so custom apps fit the systems around them.
Which engagement models are available?
Onsite, offshore or remote delivery for clients in North America, Europe, the Middle East and Asia Pacific.
Defined scope, deliverables and milestones, usually after a discovery phase.
Flexible capacity for evolving requirements, MVPs and proofs of concept.
A named, cross-functional team working as an extension of yours.
Individual developers, architects and testers added to your team.
SLA-based maintenance, support and enhancements on monthly or annual terms.
AI Application Development FAQs
What is retrieval-augmented generation (RAG)?
RAG is a pattern in which an AI application first retrieves relevant passages from your own documents or data, then asks a language model to answer using only those passages, with citations. It reduces made-up answers and keeps responses current.
What is an AI agent?
An AI agent is software that uses a language model to plan steps and call tools or APIs to complete a task, such as looking up an order and issuing a refund, within limits you set.
How do you stop an AI application giving wrong answers?
By grounding it in trusted data, testing it against a set of real questions with known answers, adding guardrails and human review for risky actions, and monitoring accuracy after launch.
Is our data safe when we use generative AI?
It can be. TechTiera uses enterprise model services that do not train on your data, enforces your permissions at retrieval, and logs and reviews AI activity.
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