Airline · legal
−80%
cloud infrastructure
Lower cloud consumption on a legal platform with AI assistants, by redesigning the data and search architecture.
Agentic AI for enterprises
Artificial intelligence agents that answer with your company's data. And prove it before going to production.
We design and implement agents connected to GCP, Snowflake, AWS and your ERP, with RAG architecture and automated evaluation. They analyze, report and run processes with answers that cite their source.
- First call at no cost
- With an architect, not sales
- NDA from the first call
Team engineering proven at
- Dow Jones
- LATAM Airlines
- TelevisaUnivision
- Deloitte
- Google Cloud
Team experience
We don't experiment with your business: we apply the engineering multinationals use
What leadership cares about: lower cloud costs, lower operating spend and more productive teams. Measured on production systems our team designed and operated.
Digital subscriptions · AI
−90%
AI model compute
Less compute to train and run AI models on a subscription platform, with reusable MLOps components.
Digital platform · data
3 h20 min
business-critical data process
A data process the business depends on went from hours to minutes by redesigning how it joins and aggregates information.
Media · financial data
2 min16 s
retrieval and response
An agent on Snowflake: teams get answers from real data almost instantly.
Where it applies
If a process lives in data, it can be delegated
Industry doesn't matter: B2B or B2C, with a website, CRM or ERP. We look for repetitive tasks that take days of your team's time and turn them into workflows an agent runs under supervision.
Finance
Reconciliations, closings and reports that take days today.
Legal and compliance
Querying contracts, case files and regulations with source citations.
B2B sales
Lead qualification, CRM follow-up and pipeline reports.
Marketing and B2C
Web traffic, campaign, segment and retention analysis.
Operations
Inventory, logistics and alerts when an indicator goes out of range.
Leadership
Natural-language questions about your data, with charts on the spot.
Services
Three stages, one technical lead
Process and data audit
We assess your current workflows to identify which repetitive processes (financial, legal or operational) can be safely delegated to an agent.
Deliverables
- Inventory of processes and sources
- Risk matrix per process
- Estimated hours saved
Connectivity and RAG integration
We connect agents to your data sources in GCP, Snowflake, AWS, ERP and internal systems. Every answer is based on verifiable data from your operation and cites its source.
Deliverables
- Secure connectors to your sources
- Retrieval with per-user permissions
- Agents with scoped tools
Evaluation and control dashboards
We implement automated evaluation and observability to ensure accuracy before and after going to production.
Deliverables
- Set of verified answers
- Traceability for every answer
- Cost control per query
The hallucination filter
If it doesn't pass evaluation, it doesn't reach your team
Before an agent is activated, we put it through technical tests built from real cases in your operation. Every answer is checked against verified company information.
End users only get access once accuracy clears the threshold we agree on with you, always above 90%. The evaluation runs again before every update.
Evaluation reportexample
Technical testscompleted ✓
Factual accuracyabove 90%
Answers citing their sourceabove 90%
Out-of-scope questions declinedabove 90%
Consistency across runsabove 90%
Minimum threshold: 90%Approved for production
Security and compliance
Your data never leaves your infrastructure
We design with your IT and security teams from day one. Every agent action is logged and auditable.
Deployed in your cloud
GCP, AWS or Snowflake, inside your perimeter and with your credentials.
Least privilege
Zero-trust access and roles tailored to each agent.
Auditable log
Every query, source and action is logged and available for review.
Data protection
Designed in line with data protection law and your company's internal policies.
Implementation
A first agent in production in 4 to 6 weeks
- 1
Audit
Week 1Mapping of processes, sources and success criteria.
- 2
Build
Weeks 2 to 4Connectors, retrieval and agent in a controlled environment.
- 3
Evaluation and pilot
Weeks 5 and 6Technical tests and use by a small group.
- 4
Production
OngoingMonitoring, support and month-by-month accuracy improvements.
Commercial model
Specialized hourly consulting, no fixed packages
The first call is at no cost. Each project is estimated in hours after the audit: you approve the scope before we start and see the hours used week by week.
01 · Audit1 to 2 weeks
Process and data audit
On request
Billed hourly
- Inventory of processes and sources
- Risk matrix per process
- Estimated hours and return
- Credited if you continue to implementation
02 · ConsultingPer project
Architecture and implementation
On request
Block of hours based on the audit
- RAG architecture and agents
- Connection to GCP, Snowflake, AWS or ERP
- Evaluation before production
- Weekly report of hours used
03 · OperationsMonthly
Support and continuous improvement
On request
Monthly block of hours
- Engineer assigned to your account
- Evaluation monitoring and tuning
- Exception handling
- Infrastructure maintenance
Prices in USD, before VAT · Cloud and API usage is paid by the client, with their own keys · Implementation: 50% upfront and 50% on delivery.
CTO and CISO questions
What they usually ask first
Does our data leave our infrastructure?
No. The agent is deployed in your cloud (GCP, AWS or Snowflake) with your own credentials and least-privilege access. Your information is never used to train public models.
What if the agent makes something up?
It doesn't answer from memory: it queries the source and cites where each answer comes from. If it finds no evidence, it says so. And before production it must pass evaluation at the threshold you set, always above 90%.
Which AI models do you use?
We choose based on your case and your cloud: Claude on Vertex AI, Gemini, Snowflake Cortex or others. The architecture doesn't depend on a single vendor.
Can it take actions without approval?
Only the ones you authorize. Everything else stays as a proposal for a person to approve, and it's all recorded in the audit log.
Do we need clean data?
It's not a requirement to start. In the audit we assess your data quality and, if needed, the pilot includes the preparation work.
How much does an AI project cost?
The first call is at no cost. The audit is billed hourly and ends with a scope estimate; with it you approve a block of hours for implementation and, if needed, a monthly block for operations.
Let's find which processes you can safely delegate
On the first call, at no cost, we review your case. If it makes sense, the process audit follows: you get a map of 2 or 3 eligible processes, the risk level of each and a scope estimate.
- First call at no cost
- Reply within 1 business day
- NDA before reviewing data