Applied AI Engineering

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

From $8,800 per project

Limited Availability

I speak:🇺🇸English●●●●●🇸🇦Arabic●●●●●·🇫🇷French●●●●○🇳🇱Dutch●●●○○🇪🇸Spanish●●○○○

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

From $8,800 per project

Limited Availability

I speak:🇺🇸🇸🇦·🇫🇷🇳🇱🇪🇸

Interrested in a Ready to Launch AI SaaS

A full white-label AI SaaS product, ready to launch. Agents that think and work using your customers' tools. Production-grade source code.

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Full Source Code

White Label License

1 Month Training

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.

Development Packages

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

Discovery

$8,800/ 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

Build & Launch

$20k–$120k/ 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

$7,500–$11k/ 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 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.

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What Others 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

What This Service Covers

We align requirements, define scope, and design an AI-native system that fits your business, data, and team.

AI agent systems, multi-agent orchestration, and autonomous workflows.

RAG pipelines, vector search, and knowledge retrieval systems.

MCP servers, tool-use APIs, and LLM integrations.

Backend services, inference pipelines, and cloud infrastructure.

Agent evaluation, LLM testing, observability, and governance.

Section 02

Why Teams Hire Me

Deep experience building agentic AI systems in production.
Senior ownership with direct communication, no middlemen.
Clear architecture decisions across LLMs, agents, and infrastructure.
Pragmatic delivery with weekly checkpoints and demos.
Documentation and handover for internal teams to own and extend.

Who This Is For

Startups building AI-native products or agent platforms.
Teams adding agentic AI capabilities to existing applications.
Companies building RAG systems, copilots, or autonomous workflows.
Founders who need a senior AI engineer as a delivery partner.
Organizations building MCP servers or LLM-powered tools.

Common Challenges I Solve

01AI architecture is unclear: agents, RAG, or fine-tuning.

02LLM integration is unreliable, slow, or expensive.

03Agent orchestration does not scale or recover from failures.

04No evaluation framework for model quality or agent behavior.

05The team needs a clean handoff of a production AI system.

Engagement Options

Projects start at $5,000 (Discovery package). Work in fixed milestones or a retainer. Discovery sprints are available.

Discovery sprint: AI requirements, architecture, and estimates.

Fixed-scope build: agent systems with clear milestones.

Ongoing development: roadmap execution and iteration.

Rescue and stabilization: AI system audits and recovery.

Section 06

AI Apps & Custom Application Development

Custom AI application development, integration, and deployment from prototype to production-ready system.

Custom AI app development
API integration & architecture
Model fine-tuning
Deployment & scaling
Ongoing maintenance support

Custom AI Assistants

Custom GPT, Claude, or open-source assistants trained on your business documents and workflows. Tuned to your brand voice and team conventions.

Custom AI assistant build
Document training on your knowledge base
Brand voice configuration
Training session for your team
Delivered in your account or self-hosted
Multi-assistant orchestration
Quarterly optimization passes

Custom AI Agents

Production AI agents tailored to your business processes and workflows. Built with your stack, your data, and your guardrails.

01Custom AI agent development

02Integration with your existing tools

03Testing and deployment pipelines

04Training and documentation

0530-day support included

06Ongoing optimization available

AI-Powered App Development

Custom web or mobile applications built around AI features. From prototype to production with full integration into your business workflows.

Custom web or mobile app

AI-powered features built-in

Integration with your tools

Training and documentation

30-day support included

Ongoing maintenance available

Section 10

Tools & Platforms I Integrate With

Production AI systems usually need to talk to the tools your team already uses. I integrate across the most common business platforms.

CRMs: HubSpot, Salesforce, Pipedrive
Workspaces: Google Workspace, Microsoft 365, Notion
Messaging: Slack, Microsoft Teams, Discord
Data: Airtable, Postgres, Snowflake, BigQuery
Automation: Zapier, Make.com, n8n
LLMs & vectors: OpenAI, Anthropic, Pinecone, Weaviate, Qdrant
MCP servers and custom tool APIs

Personal AI System Build

Done-with-you build sessions for founders and operators who want their own AI stack deployed and documented. One client saved 12+ hours per week on email triage and research alone. The system pays for itself, often within the first month.

Discovery call + custom build plan
Full build session (in-person or remote)
Cloud VPS deployment & configuration
Complete documentation package
Post-launch support window
Optional monthly optimization calls
Priority access to new tool integrations
Dedicated support channel
Quarterly full-system review

Local LLM & Private AI Deployment

For teams with privacy, compliance, or data-sovereignty requirements. Run open-source LLMs on your own hardware. Nothing leaves your machine or your network.

01Local LLM setup on your hardware

02GPU optimization and model selection

03Full data privacy: nothing leaves your machine

04On-prem or air-gapped deployment

05Hardware recommendations and procurement

06Open-source model stack (Llama, Mistral, Qwen, DeepSeek)

07Ongoing optimization and support

08Compliance and governance documentation

The Engineering Discipline Behind Production Agents

Production agents look different from agent demos. The first thing that gets built is not the agent loop but the AI evaluation design: a frozen test set drawn from real user traffic, rubrics for correctness and helpfulness, and a CI step that runs the eval on every prompt or model change. Without that harness, quality regressions are invisible until a customer complains.

The second discipline is observability. Every agent decision (which tool was called, with what arguments, why) gets logged, traced, and aggregated. LangSmith, Langfuse, Braintrust, or a homegrown tracing pipeline lets the team replay any failed trajectory and see exactly where it went off. Durability follows: checkpointing so a halted run resumes, idempotency keys on write tools, bounded retry policies, and hard caps on tokens, wall time, and dollars per session.

Cost discipline closes the loop. Production agents burn money in non-obvious ways: retry storms, oversized contexts, model defaulting to the most expensive option. The same engagement usually folds in AI cost optimization work, with LLM model selection routing decisions, prompt caching, and per-feature cost attribution. The result is a system that holds up under real users, not just in a controlled demo.

Section 14

Experience

Senior delivery ownership with a focus on production quality.

Experience

16+ years building production systems and leading delivery.

Open source

Laradock (2M+ downloads)

Frameworks

Apiato framework used by production teams.

Portfolio

100+ projects across SaaS, AI, and data systems.

Case studies

Real projects with measurable outcomes.

Company

Founder of Sista AI and delivery lead.

Got an AI product idea that needs building?

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

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FAQ

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

Discovery packages start at $8,800 for AI requirements, agent architecture, and a roadmap. Full agent system builds range from $20,000 to $120,000 depending on scope and complexity. Ongoing AI Engineering retainers run $7,500–$11,000 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.

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

Custom AI Agent Development by a Senior Engineer

AI agent development gets booked under a few different names depending on who in the company is opening the door. Engineers searching for an AI agent builder are usually thinking about autonomous workflows. Product teams searching for LLM application development are thinking about the broader category that agents sit inside: classify, summarize, retrieve, generate, route. Operations leads searching for AI automation development are thinking about replacing manual workflows with systems that run themselves with humans in the loop. The underlying engineering is mostly the same.

The work starts with agentic architecture, which means picking the orchestration pattern, the memory model, and the tool-use protocol before any code gets written. Building a single-purpose assistant is a different job than building a multi-agent platform, and most teams discover the difference only after the second or third refactor.

A meaningful share of agent work is actually retrieval work. Most agents need to read your data before they can act on it, and RAG systems are how that happens in production. The integration layer almost always touches MCP servers, since Model Context Protocol is becoming the de facto contract between agents and the tools they call. For teams with privacy constraints, the build often expands to include local LLM deployment so nothing sensitive ever leaves the network.

Underneath all of this is plain old senior software engineering. Production AI systems break in the same places production software has always broken: rate limits, race conditions, schema drift, unreliable third-party APIs. Treating AI engineering as separate from real software engineering is the most common reason demos look great and prod looks broken.

Book an AI Engineering Scoping Session

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

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16+ Years

For Consultation

Worldwide Remote