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Kamran Code

Service

AI Application Development

Practical AI application development: language-model features, retrieval systems, agents and automation wired into your real data and workflows.

Build an AI Application

Useful AI is an engineering problem more than a model problem. The model is one component; the value comes from the application layer around it — retrieval over your data, tools it can call, validation of what it returns and the workflow it sits inside.

I build AI applications end to end: from choosing where AI genuinely helps, through prompt and retrieval design, to evaluation, cost control, deployment and monitoring.

Typical use cases for AI application development

  • Knowledge assistants grounded in internal documents
  • Document extraction and classification pipelines
  • AI-assisted support, triage and drafting
  • Agents that complete multi-step tasks against your APIs
  • Agent harnesses: tools, context, memory, permissions, budgets and evals around a model

What a AI application development engagement delivers

  1. 01AI feasibility and architecture assessment
  2. 02Retrieval pipeline and data preparation
  3. 03Application with evaluation and guardrails
  4. 04Deployment with observability and cost tracking
  5. 05MLOps: self-hosted embeddings and reranking, local model serving, vector index operations, evaluation passes and budget controls
  6. 06Harness engineering for agents: tool contracts, context management, approval tiers, budgets and traces

How a AI application development engagement runs

  1. 01Understand

    Who uses the system, what they need to do, what the current process looks like and what actually has to change.

  2. 02Architect

    Data model, boundaries, integrations, where AI genuinely helps and where it doesn't — decided before code is written.

  3. 03Build

    Backend, frontend and data layers built incrementally, with working software reviewed early rather than a big reveal at the end.

  4. 04Integrate

    Payment providers, email, CRMs, model providers and internal services connected with clear contracts and error handling.

  5. 05Deploy

    Containers, CI/CD, environments, backups and monitoring so the system is operable — not just running.

  6. 06Improve

    Usage, performance and errors observed after launch and fed back into the next iteration.

Turn an AI idea into working software.

From retrieval and agents to document intelligence, I build AI systems that plug into real business data and workflows.

Build an AI ApplicationAll services