Put your AI to work.
We rebuild corporate processes with native AI. And we operate them.
*Free consultation
The Challenge
Only 7% of organizations scale at a corporate level.
The remaining 93% face the same difficulty: the gap between experimenting with technology and integrating it as a stable operating system across the organization.
60% of companies are not capturing material value from their GenAI investments.
Fewer than 60% of employees with access to these tools use them in their daily workflow. The remaining 40% is cultural resistance.
“70% of the potential value is in the core of the business, yet companies keep aiming at the periphery.”
— Boston Consulting Group (BCG)
The impact is in the core business processes, not in isolated initiatives. Our work starts where operational improvement has real consequences.
The question few ask
Your processes were designed before any of this existed.
Every coordination meeting, every manual approval, every report built by hand, every process that depends on one specific person being available. All of them were designed in a world that no longer exists.
The question isn't how to add technology to what you already do. It's how to redesign those processes from scratch , knowing what exists today.
That approach led us to build alliances with those who set the standard.
How we work
Two ways to work.
Both include Discovery and projects. The difference is whether we add workshops in parallel to also work on culture and future processes.
We audit existing processes, prioritize by impact and build. Focused on fixing what no longer scales.
Discovery deliverable: a transformation roadmap with estimated ROI per process.
Projects solve what isn't working today. Workshops, in parallel, train the team to design new processes with AI integrated from the start.
Recommended when, beyond immediate results, you want the team to internalize new ways of working.
Projects
We build what has the most impact. Real systems that run in production and that your team uses every day.
Inside every project
Deliverable
Process map with prioritized opportunities.
Deliverable
Approved system architecture. Technical scope defined.
Deliverable
System in production. Dedicated support during launch.
Deliverable
Optimization, monitoring and impact reporting.
Cases & Products
Impact measured in production.
Automated creative analysis. 43% lower CPV.
Before
Video campaigns ran at scale with no way to grade creatives first. Every asset was reviewed by hand, hours at a time, with no read on how it would perform and no cross-platform frequency control.
What we built
We built ABCD Detector, a pipeline running on Vertex AI and Google Video Intelligence that scores every creative against YouTube's ABCD framework (Attract, Brand, Connect, Direct) before it goes live. The system produces attention heatmaps, logo exposure scoring, emotional impact detection and recall potential per asset. In parallel, we consolidated media activation in DV360 for frequency control across CTV and YouTube Reserve.
The impact
- +54% VTR100 versus the previous campaign
- 43% reduction in CPV100
- 30+ hours a month saved on manual creative review
- Embedded into the client's creative QA process for future campaigns
60% of the digital budget sat in suboptimal channels.
Before
Last-click attribution drove overspend on direct conversion and quietly undervalued awareness and mid-funnel channels.
What we built
We implemented Google Meridian on two years of historical ad spend, SMB product sign-ups, macroeconomic variables and sector seasonality. The model revealed that 60% of the digital budget was concentrated in channels with high apparent ROAS but low real incrementality.
The impact
- 60% of the digital budget identified as low-incrementality
- Reallocation lifted SMB product activations by 28%
- Model refreshed monthly with fresh media and business data
- Budget scenario simulator for annual planning
From manual exports and scattered data to a centralized data warehouse with operational AI.
Before
Each team pulled its own data, in its own format, with no automation or structure. There was no single source of truth, and reports were assembled by hand, with inconsistencies and weekly delays.
What we built
We centralized the entire data infrastructure on GCP. We automated the ingestion pipelines, structured the data warehouse with a clean dimensional model and deployed a conversational agent in Slack that answers business questions in natural language directly against live data.
The impact
- 100% of data sources integrated and automated on GCP
- Manual exports and ad hoc consolidations fully eliminated
- Conversational agent live in Slack, answering in seconds on fresh data
- Real-time, data-driven decisions across the whole team
Brand reputation and conversation monitoring, in real time.
Before
Generic social listening tools with no customization, no integration with internal data stacks and no semantic analysis tuned for Latin American Spanish.
What we built
We built Social Listener, our own social media monitoring platform with real-time natural language processing. It detects mentions, analyzes sentiment, spots emerging trends and alerts automatically when negative or positive conversation spikes. It integrates natively with BigQuery and Slack.
The impact
- Continuous 24/7 monitoring of multiple brands and competitors
- Sentiment analysis in Spanish, tuned for LATAM
- Automatic alerts for emerging reputational crises
- Direct integration with executive dashboards and BI systems
Any AI model. Connected to any internal data source.
Before
Teams using public AI and exposing sensitive information, with no governance, no traceability and no connection to internal data.
What we built
We deployed a private platform interconnected with the leading models (GPT-4, Gemini, Claude, Llama) and with the client's internal systems through RAG: documents, databases, CRM, ERP. Everything runs on the client's own infrastructure, with full control and complete auditing.
The impact
- Client data stays 100% private and never leaves their infrastructure
- Access to GPT-4, Gemini, Claude and open-source models from a single interface
- Connection to internal documents, databases and systems through RAG
- Full traceability by user, team and business case
From 1 creative to 50 variations. In minutes.
Before
Every creative adaptation was produced by hand. Changes to copy, format and segment meant hours per piece. A campaign with 10 variations took days.
What we built
Re-Adapt is a generative AI platform that takes a base creative, interprets the brief in natural language and automatically generates every variation: different formats (stories, feed, banner), copy adapted by segment and distinct visual compositions. The designer reviews and approves instead of producing.
The impact
- 10x more pieces produced with the same team
- Adaptation time per piece drops from hours to minutes
- Visual quality maintained with a final human review
- Ready to plug into any creative production workflow
Commercial Structure
We charge a percentage of the savings or improvement generated.
The percentage is agreed before we start, not after.
We define together what gets measured: process hours, cost per operation, volume, margin. Compensation is calculated on that, and the number is documented before the project begins.
If we don't generate the agreed result, we don't charge the variable part.
There's no interpretation at the end. There are real metrics against a concrete number we agreed on at the start. Either we hit it or we don't.
Identify where the biggest impact is.
A 45-minute conversation to identify the biggest opportunity for impact in your organization's operations.
Book a diagnostic