Agentic AI development services for real workflows

Agentic AI application development

Build a focused application that can retrieve context, call approved tools, keep the right state, and hand decisions back to a person when required.

Typical work includes backend APIs, tool contracts, model integration, approval paths, tests, and deployment. The agent receives only the access needed for its job.

Typical output: one working workflow, documented integration boundaries, evaluation cases, and an operational handoff.

RAG and knowledge systems

Turn documents and changing business information into context an application can search, inspect, and cite.

The work can cover extraction, chunking, metadata, retrieval, reranking, source display, access controls, and quality checks. A fluent answer should never hide a broken data path.

Typical output: an ingestion and retrieval slice with test questions, source evidence, and a clear update path.

AI integration and model routing

Connect models, search, APIs, databases, and existing backend services without making one vendor the permanent center of the application.

I define stable interfaces around model calls and tools, keep provider-specific behavior behind replaceable boundaries, and use deterministic code where an agent would add risk without value.

Typical output: integration contracts, routing decisions, error handling, and a working API or backend slice.

Evaluation and safe operations

Add the evidence needed to see whether an AI workflow is improving or quietly failing.

That can include representative cases, regression checks, traces, cost and latency visibility, approval queues, failure categories, and rollback paths. The evaluation follows the workflow, not a generic model leaderboard.

Typical output: an evaluation set, operational checks, a review process, and a prioritized failure backlog.

Document and multimodal workflows

Build reviewable paths for PDFs, scans, images, structured files, audio, or video before those inputs reach retrieval or reasoning.

Extraction stays separate from interpretation. People can inspect what the system read, correct it, and reject poor input instead of debugging only the final answer.

Typical output: an extraction and review pipeline with explicit data-handling and retention boundaries.

Pilot rescue and delivery support

Find out why an AI pilot does not survive real use, then help the team ship the smallest credible production slice.

I can support a product team directly or work behind the scenes with an agency or consultancy when delivery gets technically deep.

Typical output: a production-risk assessment, implementation plan, and focused architecture support for an agreed period.

What is agentic AI application development?

It is the engineering behind an AI system that can choose steps, use approved tools, retrieve context, and maintain workflow state. Production work usually centers on permissions, integration, evaluation, failure handling, and human review.

Do I need an AI agent or a RAG system?

Use RAG when the main job is finding information and answering with sources. Add agent behavior when the workflow must choose tools, perform dependent steps, or prepare an action. Some projects only need a normal backend service with one model call.

Can you work with an existing prototype?

Yes. I first map the real workflow and find where the prototype fails, such as data quality, retrieval, tool access, state, evaluation, latency, cost, deployment, or ownership.

Do you require a specific model or framework?

No. The workflow and operating constraints come first. Existing choices can stay when they fit. I keep provider-specific behavior behind replaceable boundaries where that is practical.

How does a project start?

Send a short description of one workflow, the systems it touches, and what currently fails, takes too much manual work, or needs to be built. I will tell you whether an audit, pilot rescue, build slice, or another route makes sense.