Skip to main content
Zalt Logo

Applied AI Engineering

AI-native applications, agentic systems, and custom software, from architecture to production.

Pricing: From $5,000 per project

Who it is for
Founders and teams building AI agents, LLM applications, or custom platforms.
Outcome
Production-ready AI system with clean architecture and handoff docs.
Engagement
Fixed-scope builds or milestone-based delivery.
How to start
Book session or take a free call to scope.

What This Service Covers

  • Agent Systems: Multi-agent orchestration, tool-use APIs, and autonomous workflows.
  • RAG & Retrieval: Vector search, knowledge pipelines, and retrieval-augmented generation.
  • MCP & Integrations: MCP servers, function-calling, and cross-provider AI tool integrations.
  • LLM Backends: Inference pipelines, model serving, prompt engineering, and evaluation.
  • Observability & Governance: Agent tracing, LLM evaluation, cost tracking, and compliance.
  • Smooth Handover: Documentation and handover so your team owns and extends the system.

My AI Engineering Process

A structured approach to designing, building, and launching AI-native systems.

  1. Discovery & Requirements: We clarify the problem, map AI use cases, and define agent scope, data sources, and success criteria.
  2. Architecture & Planning: I design the agent architecture, define data pipelines and integrations, and select LLMs and infrastructure.
  3. Implementation & Iteration: We build the AI system in iterative cycles, delivering working agents and incorporating evaluation feedback.
  4. Launch & Handover: We deploy to production, monitor usage, and stabilize the system with documentation and handover.
All Services
Applied AI Engineering service icon

Applied AI Engineering

AI-native applications, agentic systems, and custom software, from architecture to production.

From $5,000 per project
Available
I speak:🇺🇸🇸🇦·🇫🇷🇳🇱🇪🇸
Product work session at Viralstyler

Quick Summary

Who it is for

Founders and teams building AI agents, LLM applications, or custom platforms.

Outcome

Production-ready AI system with clean architecture and handoff docs.

Engagement

Fixed-scope builds or milestone-based delivery.

How to start

Book session or take a free call to scope.

What This Service Covers

AI-native systems with senior-level ownership of agent architecture, LLM integration, and delivery.

Agent Systems

Multi-agent orchestration, tool-use APIs, and autonomous workflows.

RAG & Retrieval

Vector search, knowledge pipelines, and retrieval-augmented generation.

MCP & Integrations

MCP servers, function-calling, and cross-provider AI tool integrations.

LLM Backends

Inference pipelines, model serving, prompt engineering, and evaluation.

Observability & Governance

Agent tracing, LLM evaluation, cost tracking, and compliance.

Smooth Handover

Documentation and handover so your team owns and extends the system.

If you need an AI-native product built with senior ownership, I take responsibility for agent architecture, delivery, and launch.

You get production-grade AI systems, clean boundaries, and documentation your team can maintain and evolve.

Ready to scope your AI build?

Book a session or take a free call to map scope and architecture.

Book Session
Get In Touch
Speaking at WorldSkills
Speaking at ITkonekt

Development Packages

From AI architecture blueprints to full agent system delivery with handover.

Discovery

$5,000/ fixed

Get a clear roadmap, AI architecture, and scope before you build.

Start discovery

What's included

  • Product & AI requirements document
  • System & agent architecture design
  • Data model, API & tool-use design
  • LLM & infrastructure stack proposal
  • Risk & complexity assessment
  • Implementation roadmap
  • Budget & timeline estimate

Engagement: 2–3 weeks

Most popular

Build & Launch

$9,000–$72,000/ project

Full implementation from AI architecture to production deployment.

Discuss project

What's included

  • Agent systems & LLM backends
  • RAG pipelines & vector search
  • MCP servers & tool integrations
  • Frontend, API & cloud infrastructure
  • Agent evaluation & testing
  • Deployment & go-live support
  • Technical documentation
  • Handover & team onboarding

Engagement: 2–4 months

Growth

$3,000–$6,000/ month

Ongoing AI Engineering and support for fast-growing products.

Discuss partnership

What's included

  • Feature & agent development
  • Bug fixes & production support
  • Dedicated development time
  • Code reviews & agent evaluation
  • Monthly strategy calls
  • Regular progress updates
  • Security & model governance
  • Scaling & inference optimization

Engagement: Monthly retainer

What Clients Say

Feedback from people who have worked with me.

0/5.0
0 reviews

"The first version was deliberately simple. Because of that, iterating afterward was straightforward instead of painful. We avoided a lot of the technical debt that usually accumulates in early product phases."

Carlos

Product Owner

Software Building

Experience

Senior delivery ownership with a focus on production quality.

Experience

16+ years building production systems and leading delivery.

Team session at Sociatagr
Focused work session at Vinelaber
Remote work session on the go

Got an AI product idea that needs building?

Let's turn your vision into a production AI system with the right architecture.

Book Session
Get In Touch

FAQ

How much does it cost to build a custom AI agent system?

Discovery packages start at $5,000 for AI requirements, agent architecture, and a roadmap. Full agent system builds range from $10,000 to $70,000 depending on scope and complexity. Ongoing AI Engineering retainers run $3,000–$6,000 per month.

Can you build an AI agent or multi-agent system from scratch?

Yes. I build production-grade agent systems including orchestration, tool-use APIs, memory management, and execution durability. From single-purpose agents to multi-agent platforms with governance, observability, and multi-tenant isolation.

Can you build a RAG system or knowledge retrieval pipeline?

Yes. I design and implement RAG pipelines with vector databases like Pinecone, chunking strategies, embedding models, and retrieval evaluation. Systems are built for accuracy, latency, and cost efficiency in production.

Do you build MCP servers and AI tool integrations?

Yes. I build MCP servers using FastMCP and the MCP SDK that let AI agents interact with external services, APIs, and data sources. I also build tool-use APIs, function-calling interfaces, and cross-provider agent integrations.

What tech stack do you use for AI and agentic systems?

Common choices include Python, TypeScript, LangChain, FastMCP, FastAPI, Temporal for durable execution, Pinecone and PostgreSQL with pgvector for retrieval, Langfuse for tracing, and OpenAI, Anthropic, or self-hosted models via vLLM.

Can you add AI agents to an existing application?

Yes. I integrate agentic AI capabilities into existing products, including copilots, autonomous workflows, conversational agents, semantic search, and intelligent automation. I evaluate where agents create real value versus where simpler solutions work better.

How long does it take to build an AI agent application?

A discovery phase takes 2–3 weeks. A typical agent system build runs 2–4 months from architecture to production. Timeline depends on agent complexity, integrations, evaluation requirements, and iteration cycles.

Do you provide support after launching an AI system?

Yes. I offer monthly retainers for ongoing agent development, model upgrades, evaluation tuning, prompt optimization, and scaling support. Every project includes handover with documentation so your team can maintain and extend the system.

Remote work session at Recharger

My AI Engineering Process

A structured approach to designing, building, and launching AI-native systems.

Step 1

Discovery & Requirements

1-2 weeks

We clarify the problem, map AI use cases, and define agent scope, data sources, and success criteria.

Deliverables

  • AI requirements and scope document
  • Agent workflows and tool-use mapping
  • Data source and retrieval strategy
  • Initial delivery and release plan
Step 2

Architecture & Planning

1 week

I design the agent architecture, define data pipelines and integrations, and select LLMs and infrastructure.

Deliverables

  • Agent and system architecture overview
  • RAG pipeline and data model design
  • LLM and infrastructure stack selection
  • Milestones and implementation plan
Step 3

Implementation & Iteration

4-12 weeks (varies)

We build the AI system in iterative cycles, delivering working agents and incorporating evaluation feedback.

Deliverables

  • Working agent system increments
  • Codebase with tests and evaluation suites
  • Regular demos and status updates
  • Issue tracking and progress reports
Step 4

Launch & Handover

2-4 weeks

We deploy to production, monitor usage, and stabilize the system with documentation and handover.

Deliverables

  • Production deployment
  • Operational documentation and runbooks
  • Team onboarding sessions
  • Post-launch support window

Book an AI Engineering Scoping Session

Start with a focused session or a free call to define scope and architecture.

Book Session
Get In Touch
16+ Years
For Consultation
Worldwide Remote