Enterprise AI
AI implementation that gets adopted, governed and run.
From assessment to operations: we prioritize your use cases, take the first one to production, train your teams with HackLab and run your AI with AI Management.
- adoption levels
- 3
- adoption levels
- hands-on with real cases
- 90%
- hands-on with real cases
- day roadmap
- 90
- day roadmap
- integrated strategy
- 1
- integrated strategy
AI integration framework
How to implement AI in your company: 3 pillars and 4 layers.
Every AI integration rests on governance, processes and training, and each use case falls into one of four layers with its own % of efficiency, time saved and payback. This is how we shape every proposal: the three pillars at once, and each use case in the layer with the highest ROI and the lowest risk.
The three pillars move together or none of them moves: they are not phases, they are a system.
Pillar 01
Governance
What can be done, and who is accountable?
The decision and control framework that makes AI visible, auditable and defensible before your board or an auditor.
- Who is accountable
- CEO · cannot be delegated
- How it is measured
- % of AI tools in use that are approved
What we leave in place
- A one-page AI use policy, signed by leadership
- Data classified in three levels
- Approved tools and who authorizes new ones
- Investment and pilot kill criteria
- An audit trail of AI-assisted decisions
- A policy owner and a review date
Four implementation layers
Each layer raises the % of efficiency and time saved.
Every use case falls into one of these layers. Moving up raises the efficiency and the time you save on the task or process where AI is applied, but also the investment, the risk and the profile of who builds it.
↑ more efficiency · more investment · more risk · more technical profile
Efficiency and time saved in the process where AI is applied, not across the whole company. Layers do not add up.
Assisted chat
A person talks to the model: asks, reviews and decides what to use. Nothing runs without someone reading it.
up to30%
Efficiency and time savedon the tasks where it is used
What your operations gain
- Up to 40% less time on writing and analysis
- Higher quality on the first try and less rework
- Value from the first week, with no integrations
- Investment
- $
- Risk
- Low
- Built by
- Any employee
Examples: Résumé screening · Research and summaries · Document review · Employee onboarding
Which layer is each of your processes in?
In the assessment we place your use cases in the four layers, measure the baseline and build the path: fast returns in layers 1 and 2 while we build the pillars that layers 3 and 4 require.
AI adoption · HackLab
AI adoption: from curiosity to governed operations.
The training pillar, turned into a program: one strategy and three adoption levels. Less theory, more building on your company's real processes.
- ✓Governed, measurable and scalable AI
1 day · executive
AI for Leaders
Vision, priorities and governance.
- Decisions and risks
- Use-case prioritization
- Guardrails
- Human accountability
Every team leaves with
90-day AI adoption and governance roadmap
- adoption levels
- 3
- adoption levels
- hands-on with real cases
- 90%
- hands-on with real cases
- day roadmap
- 90
- day roadmap
- integrated strategy
- 1
- integrated strategy
Testimonials · HackLab AI Edition
Leaders augmented with AI.
The leadership team at Grupo Deportes Martí went through their HackLab AI Edition. This is what they say, in their own words.
Grupo Deportes Martí · Sporting goods retail · HackLab AI Edition
In Spanish, with Spanish captions
Tap a quote to hear it
Bambú, I believe, is playing a hugely important role in this. We are all truly very happy, and the team is genuinely excited about what we are doing.
They already took the step. Bring HackLab to your leaders and C-Level.
AI operations · AI Management
Once AI is adopted, someone has to run it.
AI Management runs your agents, measures usage, costs and the ROI of AI in every process, and keeps governance up to date.
Agent management
Configuration, monitoring and evolution of AI agents.
Infrastructure and tokens
AI usage measured, optimized and with costs under control.
Feasibility and ROI
Technical and business feasibility before you invest.
Governance and guardrails
Permissions, traceability and human validation.
AI automation
End-to-end processes that run on their own.
Adoption and training
Teams that build with AI from day one.
Success stories
AI that is already in production.
Pharmaceutical

3languages with voice
AI virtual assistant in Japanese, English and Portuguese
Terumo
- Data & AI
- Software
Security · 911
3languages: French, Japanese and English
Translator for 911 operators
C5 Baja California Sur
- Data & AI
- Software
Fashion · Generative AI
0photo shoots needed
Generative AI avatars that transform campaigns
Fashion brand
- Data & AI
Mining · Field data
7operational workflow steps digitized
Smart mine: centralized operations with OCR and biometrics
Mining operation
- Data & AI
- Software
- Infra & Cloud
Banking · Security
+20Malerts processed per month
Intelligent monitoring middleware for 1,400 bank branches
Bank with 1,400 branches
- Infra & Cloud
- Data & AI
- Cybersecurity
Public safety
+43Kreports handled by chatbot
Comprehensive police management platform with chatbots
Policía Municipal de Morelia
- Software
- Data & AI
- Infra & Cloud
- Cybersecurity
Frequently asked questions
Questions about implementing AI.
How do I implement AI in my company?
In five steps: an assessment that prioritizes use cases by value and feasibility, a quick win in production, your teams' adoption through HackLab, governance, and operations with AI Management. It all rests on three pillars (governance, processes and training) and starts with the AI layers that pay back fastest.
How do you measure the ROI of AI?
Every use case starts with a baseline, KPIs and ROI defined. The expected efficiency and time saved depend on the layer: up to 30% on tasks done with assisted chat and up to 80% of routine cases resolved by agents. Once in operation, AI Management measures usage, costs and results so you can decide what to scale.
What is AI Management?
It is the service that runs AI once it is adopted: management of agents, infrastructure and token usage, governance and guardrails, and feasibility and ROI assessment.
Can you implement the AI use cases that come out of HackLab?
Yes. The same team that facilitates HackLab designs and implements agents, automations and assistants, and then runs them with AI Management.
Enterprise AI services
- AI governancePermissions, traceability, human validation and costs under control.
- AI trainingHackLab on three levels: C-Level, managers and IT teams.
- AI agentsAgents and chatbots connected to your systems, from pilot to production.
- Claude for businessClaude and Claude Code implementation with a Claude partner.
Where should AI start in your company?
Book an assessment and we'll prioritize your use cases, your HackLab or your first agent in production.



