"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."
C
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.
04
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 $6.1K (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
08
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
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
12
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.
How much does it cost to build a custom AI agent system?
Discovery packages start at $6.1K for AI requirements, agent architecture, and a roadmap. Full agent system builds run $7K–$72K depending on scope and complexity. Ongoing AI Engineering retainers run $4.5K–$6.6K 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.
Why do so many agentic AI projects get shelved after the pilot?
Industry research on agentic AI rollouts points to governance and unclear business value as the top reasons pilots get canceled before they reach production, not model quality. That is why evaluation harnesses, cost caps, and named risk assessments are built in from discovery, not bolted on after something breaks in front of a customer.
Who owns the agent, the code, and the infrastructure after handover?
You do, fully. Source code, prompts, evaluation suites, and infrastructure-as-code transfer to your accounts and repos. There is no dependency on my involvement to keep the system running, and documentation is written for your team to operate it independently.
What happens if the agent behaves unpredictably or makes a costly mistake in production?
Production agents get bounded retry policies, hard caps on tokens, wall time, and dollars per session, plus tracing so any failed run can be replayed and diagnosed. Unbounded autonomy is where agentic systems get expensive and risky, so guardrails are part of the initial build, not an afterthought.
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.
