You know something needs to change. The question is what to build first.
Custom production systems in 4 to 16 weeks. One operator with AI agents working in parallel, dashboards, automations, assistants built from your operation. You own everything.
An AI Foundation Build is a 4-16 week custom implementation building production-ready systems for your operation. Examples: project dashboards, document search, WhatsApp agents, client portals. First system ships in 4-8 weeks. Phased delivery means you start using working systems while the next phase builds. Fixed-fee, milestone-based.
Fixed-Fee Build•Milestone-Based (you pay when deliverables ship, not by the hour)•First System Ships in 4-8 Weeks
The Diagnostic Comes First
Most companies don't know what to build until they know what's broken.
That's what the diagnostic is for. Two to four weeks where I map your data, your tools, your processes, your team's AI usage. I identify where you're losing time and money. Then I tell you honestly what to build first.
The build scope comes from that diagnosis, not from a service menu. A construction company with scattered project data needs something different than a family office with knowledge trapped in people's inboxes. A law firm using ChatGPT unsafely needs something different than a distribution company running on legacy ERPs.
Most AI projects that fail do so for the same reason. The data was not ready, or the project was not scoped from real operational pain. I have seen it in construction, legal, and financial services. The pattern does not change. That is why the diagnostic comes first.
This is why I don't charge by the hour. I charge by what gets delivered. And what gets delivered depends on your reality.
What Gets Built. Real Examples
The scope is different for every company. Here's what it has looked like in practice.
Argentina's Largest Construction Group
The Diagnosis
Running everything from a 15-tab Excel spreadsheet. No real-time visibility into project costs or schedules. Field teams couldn't report back quickly. Executive team lost 10-15% of margins to processes they'd accepted as normal. CEO couldn't answer "are we making or losing money on this project?" without asking three people and waiting for a spreadsheet update.
What We Built
Capataz, an AI system that consolidated their entire operational data into a live dashboard. Document search across 10+ years of contracts and SOWs. Real-time cost tracking against actuals. Certification automation for compliance. Worker management across sites. Executive dashboard that answers every critical question without a spreadsheet.
The Timeline
9 months. Phases: data consolidation (6 weeks) → cost tracking + dashboard (4 weeks) → certification automation (2 weeks) → worker management (ongoing).
The Result
The CEO now sees every project's profitability in real time. No more waiting on three people to answer "are we making money on this project?" The Monday status meetings disappeared because the dashboard shows the truth automatically. Field teams report faster. Finance catches cost overruns 3 weeks earlier and can course-correct before margins disappear.
A $14B Wealth Advisory Firm
The Diagnosis
12 years of institutional expertise published across a deep library of documents and podcasts, and the firm was invisible when prospective families asked AI tools for wealth management advice. Roughly two orders of magnitude between branded and non-branded search visibility. Six operational gaps that had nothing to do with the website: proposals written from scratch, knowledge trapped in email, tools deployed but unconfigured.
What We Built
Voice DNA Profile from more than a decade of published materials. 60 authority pages designed for AI citation. Then the engagement expanded: proposal automation engine, SharePoint knowledge base, intelligence briefing system, team AI environments, Operational Blueprint. What started as a content project became a 9-month Fractional Head of AI engagement.
The Timeline
6 weeks for AI Visibility (Phase I-III), then 9 months for Fractional Head of AI (Phase IV). 90-day trial. 30 days notice. Everything built belongs to the firm.
The Result
AI visibility: from appearing in 2 of 10 queries to a framework to own all 10. Proposals: 4-8 hours from scratch to 80% first draft in minutes. 12 years of institutional knowledge made searchable in seconds. Team trained, systems documented, no ongoing dependency.
Legal Services Firm
The Diagnosis
$1M in ad spend annually. Zero conversions because every lead had to be manually qualified by the founding attorney. The founding attorney was answering WhatsApp at 11pm trying to book meetings. Most leads fell through cracks.
What We Built
Victoria, a WhatsApp AI agent that qualifies leads in real time. Asks the right questions. Books qualified meetings directly into Calendly. Syncs new leads to the CRM. The AI agent handles the 11pm messages. The attorney only talks to people actually ready to work.
The Timeline
60 days.
The Result
On the same $1M ad spend, the meeting booking rate moved from near zero to 42% of inbound WhatsApp conversations. That is nearly every qualified lead getting routed instead of falling through the cracks. The founding attorney got his nights back. The CRM became useful because every lead now flows through the same qualification system.
Nonprofit Consulting Practice
The Diagnosis
Grant writing process takes 6-8 hours per grant, repeating the same research, the same structure, the same funder cultivation. A VA was handling blueprints, but at $25-30/hour it cost more than the value delivered. The business owner was spending more time on process work than on strategy.
What We Built
A complete AI system configured for her specific work. Claude workspace with grant research tools, funder database connections, and turned her existing grant templates into AI-powered workflows. The VA now spends 1-2 hours per grant instead of 6-8. The business owner reclaimed strategic hours.
The Timeline
2-3 weeks.
The Result
The VA's productivity doubled. The business owner went from drowning in process work to actual strategy. The practice now writes grants 75% faster. That means more grants out the door per quarter with the same resources.
Coaching Platform
The Diagnosis
Running a membership business with separate tools for everything. Website on Squarespace. Membership on Memberful. CRM scattered across email. Content hub didn't exist. Booking was through Calendly with manual CRM entry.
What We Built
A unified platform. Website + membership integration + CRM + content hub + booking system. Leads come in through the site, get qualified and routed automatically by the AI system, booked into meetings, and tracked through the CRM without manual re-entry.
The Timeline
3 months.
The Result
The business owner now manages everything from one place. No more switching between six tools to answer "who are my members?" or "what bookings do we have this week?" No more manual data entry between systems. The AI routes leads based on fit, so the business owner only talks to people ready to buy.
What Every Build Includes
Every build, regardless of scope, sits on the same foundation. The 6 layers of the AI Operating System.
Data
I consolidate your scattered data into a single source of truth. Connects Excel, QuickBooks, Airtable, Google Drive, CRMs, whatever you're using. Standardizes the schema. Removes duplicates. Builds the foundation that AI actually needs. You can't have a live dashboard without a clean data layer. You can't have an AI agent without trustworthy data. I start here.
Command Center
A live dashboard that answers your critical business questions without meetings or spreadsheet updates. Real-time cost tracking. Project status. Sales pipeline. Financial health. Whatever matters to your CEO. The command center is what executives see when they open their browser.
Private AI
An AI system that knows your business. Trained on your data (not public ChatGPT). Your customer documents, your playbooks, your historical decisions. Your team asks it questions and gets answers rooted in your knowledge, not hallucinations.
Governed Automation
Workflows that run without human intervention, but with oversight. Lead qualification. Report generation. Invoice reconciliation. Document routing. The automation doesn't guess. It follows rules you define. You see what it did and why.
Governance
An AI use policy. Shadow AI monitoring. Approved tool list. Data exposure assessment. You're not banning AI. You're controlling it.
AI Visibility
Your company shows up when prospects ask AI about your industry. Your answers appear in LLM responses. Your thought leadership is searchable. This is the 9-month strategic layer that comes after the foundation is solid.
The Build Process
Weeks 1-2: Kickoff & Design
Discovery call with your leadership. I review the diagnostic findings. We nail down the scope: what's being built, what's phase 2, what's your responsibility vs. mine. I design the data model, the workflow architecture, the dashboard layout. You approve before any code ships.
Weeks 2-4: First System Ships
By week 4, you have a working system. Not a prototype. Not a pilot. A production system in your hands that starts solving a real problem. Usually this is the data layer + command center. You can start using it immediately.
Weeks 5-8: Expansion
The next phase ships. Could be the AI agent. Could be automation. Could be the private AI layer. Depends on what the diagnostic revealed you needed first.
Weeks 9-16: Depth & Governance
New automations. Team training. Policy documentation. Optimization based on real usage data. If you go to a retainer, this is where that conversation happens. If you're done, everything you own ships cleanly with full documentation.
Milestone-Based Billing
You don't pay for hours. You pay when deliverables ship.
- •Phase 1 ships → you pay Phase 1
- •Phase 2 ships → you pay Phase 2
No surprises. No scope creep. If the scope changes, we agree on the new price before moving forward. Most clients see measurable time savings within the first phase. One wealth advisory firm cut quarterly reporting from 15 days to 2-3 days in Phase 1 alone.
Complimentary Training, The Train + Retain Bundle
Every build includes a half-day AI Quick Start workshop for your team.
Two to three hours where I train your team on the system I built. How to use the dashboard. How to prompt the AI correctly. How to spot when an automation should be adjusted. How AI governance works in your company. You walk away with people who can actually operate what was built, not a system that depends on me.
Companies that pair implementation with structured training see 3x higher adoption rates in the first 90 days (BCG, 2024). That is why training is not an upsell. It is included. If your team needs deeper training beyond the half-day session, standalone AI workshops are available for teams up to 25.
The Train + Retain path: Most companies that go through the build move to a Fractional Head of AI retainer. That covers ongoing governance, new capabilities every month, and quarterly team training. The build gives you the infrastructure. The retainer makes sure it keeps evolving.
What You Own
Everything I build is yours. No proprietary platforms. No monthly licensing fees. No vendor lock-in.
The dashboard is built on open infrastructure. The AI is Claude, which you access through your own API key. The automation runs on platforms you choose. The data is in your database. If you fire me tomorrow, you can hand the system to another engineer and they'll understand it immediately.
That's the difference between a build and a platform. Platforms lock you in. Builds set you free.
Is This Right Now?
You should do a build if:
- 01
The diagnostic gave you a clear picture and a specific recommendation. Don't build just to build. Build because you know why.
- 02
Your team is spending 10+ hours per week on manual processes that could be automated. One construction group was spending 60+ minutes on daily status updates. That is a real problem worth solving.
- 03
Your data is scattered and your executive team can't answer basic questions without asking people. If your CEO doesn't know project margins in real time, that's an AI Foundation Build problem to solve.
- 04
You've already tried buying tools and they didn't work because your data wasn't ready. The build includes the data foundation. Tools will work after.
- 05
You have 20-650 employees and a clear budget. I work with mid-market operators, not enterprises (too many stakeholders) and not solopreneurs (wrong pricing model).
Pricing & Timeline
Fixed-fee build
Milestone-based. You pay as deliverables ship. The price depends on scope, which comes from the diagnostic. A single AI agent is a smaller engagement. A full data consolidation, dashboard, and automation layer is a larger one. A 9-month build spreads over multiple phases. You know the build price before we start. Pricing is quoted per engagement after the diagnostic.
- •Production-ready system (handling real work, real data, real users)
- •All 5 layers of the AI Operating System (Data through Governance)
- •Half-day AI Quick Start workshop for your team
- •Full documentation, your team or another engineer can take it forward
- •No lock-in, you own everything, including API keys and infrastructure
What's not included:
- Infrastructure costs (cloud hosting, databases, API keys)
- Team time, your IT/ops person: 2-3 hours per week
- Third-party software licenses not included with the build
- Any scope beyond what is agreed in the build specification
Timeline
4-16 weeks depending on scope. First working system ships in 4-8 weeks. Full build (all 5 layers + optimization) takes 6-9 months, usually billed in phases.
Your Build May Cost 20-40% Less After Tax Offsets
AI Foundation Build engagements generally qualify for the IRC §41 R&D Tax Credit. Up to 65% of every dollar you pay can count as a Qualified Research Expense, because the IRS treats custom AI work as contract research.
Add Section 174A immediate expensing (restored in 2025, retroactive to 2022) and the effective cost drops further. C-Corps typically see a 18-23% offset after credits and deductions. Pass-through entities that stack the Florida IWT training grant on top can see 30-40% total offset.
The deadline matters: Section 174A immediate expensing requires filing by July 6, 2026 for retroactive claims. If your company has been capitalizing AI and software expenses since 2022, there may be recovery available beyond the current engagement.
I walk every client through the qualifying criteria, connect them with R&D credit-experienced CPAs, and provide the documentation the IRS requires, including the 4-part test evidence, contemporaneous records, and contract allocation.
See all tax credits, grants, and deductions →The Honest Answer
A custom AI build isn't cheap. Here's how to think about it.
Big consulting firm
A strategy deck and a 6-month sales cycle. You get a PDF. Nobody builds anything.
Team-based fractional AI firms
Premium diagnostic plus team-based monthly retainers. Most are advisory-focused, they recommend what to build, then you find someone else to build it. If they do build, the team rotates and you manage the handoffs.
Automation agencies
Fixed fee for building one tool's workflow. If you need something across multiple tools, you pay again.
Work-Smart
A production-ready system you own, built specifically for your operation, with team training included, and no lock-in.
The difference is what actually ships. You don't get a deck. You get a system. You don't get an advisor. You get a builder. You don't get a platform contract. You get ownership.
What Comes Next
After the build ships, you have three options.
You're Done
The system is yours. You own it. You hand it off to your team or to another engineer if you need support. No ongoing payment. No relationship. Clean handoff.
Retainer (Ongoing Operations)
Monthly engagement where I maintain the system, deploy new capabilities, optimize based on real usage, monitor governance, and keep pace with your business evolution. Fixed-fee monthly retainer scoped to your needs. This is what the construction group above does. They are on retainer now, continuously improving the system.
Mini-Builds
You come back for additional phases. Layer 6 (AI visibility). New departments' automations. Next-phase capabilities. We scope and quote them separately.
Most companies who go deep end up on a retainer. The system starts working, they see the value, they want to keep expanding it. But it's entirely your call.
Frequently Asked Questions
4-16 weeks depending on scope. A WhatsApp AI agent took 60 days. A full data consolidation + dashboard + automation layer took 9-12 weeks. The diagnostic defines the timeline before we start. First working system ships in 4-8 weeks, you don't wait for everything to be done.
You pay when deliverables ship, not by the hour. Phase 1 ships → you pay Phase 1. Phase 2 ships → you pay Phase 2. No scope creep surprises. If the scope changes, we agree on the new price before moving forward. You have measurable outcomes before the next payment.
Almost never. The build connects to what you already use. QuickBooks, Monday, HubSpot, Google Workspace, whatever you have. It adds a layer of intelligence and automation on top. No rip-and-replace. No forcing you to switch everything over.
That's expected. The data layer includes cleaning, validation, and structure. A construction company had a 15-tab Excel that nobody trusted, the build fixed the model, validated the logic, and automated the data ingestion. Data being messy is why you need the build in the first place.
Me. Ignacio. The founder. You work directly with me from diagnostic through build through retainer. No handoffs to junior staff. No rotating team. One person, start to finish, for consistency.
Fixed-fee, scope-based pricing. The diagnostic defines the exact number before we start. A single AI agent was built and deployed in weeks. A full build ran across 9 months in phases. You know the price and timeline before you commit.
Because I build on your data first, not on top of a tool. Most failed AI projects start with a platform purchase. The platform can't read your data because nobody cleaned it. I start with data consolidation, the foundation. When the dashboard or AI agent goes live, it works because the data underneath is solid. That's why adoption happens naturally.
Yes. Custom AI builds, dashboards, AI agents, automation workflows, qualify as contract research under IRC Section 41. Up to 65% of contractor payments count as Qualified Research Expenses. On a typical $50K AI build, that translates to roughly $4,550 in federal R&D credit (Year 3+ ASC rate) plus Section 174A deduction value, together offsetting 18-23% of the build cost. I connect every client with an R&D tax specialist who handles the full application. Not tax advice, verify with your CPA.
Most companies don't know what to build until the diagnostic shows them what's broken.
If you already had a diagnostic and you know what needs to be built, let's talk about the build. If you haven't done the diagnostic yet, we should start there. I'll be honest about which makes sense.