Diagnosis first. System next. Capability that stays.
We help established companies in traditional sectors discover exactly where they're losing value. Then we build, on demand, whatever the diagnosis requires. In your infrastructure, with your team in control.
AI doesn't fail. The way work is designed around it does.
The problem isn't the technology. It's that most companies are automating the wrong thing: tasks inside broken workflows, instead of redesigning the workflows themselves.
The signals exist. The system to act on them doesn't.
Demand spikes, pricing windows, maintenance alerts, staffing gaps: your operation produces these signals every day. But without a system that surfaces them automatically, your team decides by instinct or last year's spreadsheet. The data was always there. The tool to act on it wasn't.
Expert time lost to work that shouldn't be manual
In most service operations, the highest-cost bottleneck isn't strategy. It's assembly. Documents gathered from multiple sources. Reports written by hand from research that took days to organize. Approvals that require someone to manually pull together information that already exists in the system.
When the project ends, the system stops, because the organization never changed around it.
Most AI projects fail after the proof of concept, not because the technology broke, but because leaders kept operating the same way. The vendor built the tool. Nobody redesigned the decisions the tool was supposed to inform. When the project closed, the system went dark. You were back to spreadsheets, with nothing to show for the investment.
Lead communication still depends on someone coordinating every step.
Many teams still manage lead relationships by hand, with tools that demand constant operation, training, and a human decision at every step. When the key person leaves or gets overloaded, communication stops. The next stage isn't a better tool: it's a system that runs without someone driving it, with the team watching outcomes instead of managing steps.
We build systems your team owns and runs.
- Runs inside your infrastructure, not ours
- No SaaS pricing tied to your operational logic
- Fully documented and transferable code
When the project ends, you own the system completely. Not a license. Not a dashboard you operate: agents run the flow, with your team supervising. The system itself: documented, transferable, and built to evolve with your team.
What we build
Workflow Redesign Diagnosis
We map your operation to find where work is still built around humans coordinating the steps. You get a prioritized list of where redesigning the workflow around AI (not just automating tasks inside it) creates the highest measurable impact.
Custom AI Systems
Systems with AI agents that run the workflow end to end: they decide the next step, call your existing systems, handle exceptions, and escalate to a human only when needed. We design and build for your exact context: demand forecasting, document research automation, revenue analytics, predictive maintenance, compliance reporting, marketing triggers, CRM and WhatsApp outreach automation, or anything else your operation needs.
Capability Transfer: Your Team Runs It
Every project ends with your team able to operate, modify, and evolve what was built, without us. A transfer of how to think about, maintain, and grow the system as your operation changes.
Training and Support on Claude
For teams that already use AI unevenly. We sit with each person, find where AI solves their own bottleneck, and leave them with working flows of their own. Listening and a hands-on workshop first, then twelve weeks alongside the team.
Ver o programaWhat these systems look like in practice
Concrete AI systems designed for the operational problems that repeat most often in service businesses.
- Revenue & Demand
- Dynamic pricing tied to demand, seasonality, and external events
- Demand forecasting with automatic staffing and supply triggers
- Revenue anomaly detection with real-time alerts
- Booking and conversion recovery based on behavioral signals
- Operations & Knowledge Work
- Document research and report generation: multi-day workflows down to hours
- Predictive maintenance alerts from equipment logs and service history
- Per-asset cost monitoring with anomaly flagging
- Compliance and audit report automation
- Marketing & Growth
- Automated marketing triggers based on demand forecasting
- Channel attribution analysis for marketing spend
- Review and feedback analysis to detect recurring service problems
- Customer segmentation and reactivation based on behavior patterns
- Compliance & Legal
- Regulatory obligation monitoring and automated alerts
- AI-powered legal document classification and analysis
- Anomaly detection in contracts and audits
- AI-assisted due diligence for M&A and partnerships
- Law Firms
- Case law and doctrine research with every source cited, so the responsible lawyer can check it
- Contract and litigation document analysis and classification, with mandatory human review before anything is filed
- Workflows that run inside the firm's own environment, so material covered by professional privilege never reaches third-party services
- LGPD controls and usage records aligned with Recommendation 001/2024 of the Brazilian Bar Association's Federal Council
- CRM & Communication
- AI agents that run the full lead cadence over WhatsApp, from first contact to close, with no manual operation
- CRM with dynamic segmentation by profile, history, and engagement level, updated in real time by the agents
- Mass outreach at scale with an observability dashboard: your team monitors, the system executes
- Autonomous communication operation for campaigns, firms, and sales teams, with optional human intervention
Founders who sign the diagnosis and lead the build.
River Labs was founded by three partners with experience across infrastructure, technology, and operations at global firms. The founders personally conduct every engagement, from diagnosis to production systems, and activate a network of specialists by project: market analysts, data engineers, designers, and sector experts.
- Leonardo WernerFounder · Governance, Adoption & Culture
Leonardo Werner works on corporate governance and on how organizations adopt emerging technologies, at the intersection of ethics, organizational culture, and governance.Full bio
His projects have global reach and focus on helping organizations build initiatives that align culture, strategy, and emerging technologies from an ethical perspective.
He is a senior consultant at Principia Advisory and visiting professor at PUC-Rio and EAE Business School in Barcelona, where he teaches business ethics in undergraduate and graduate programs. He is also a fellow at FreedomLab, a Dutch think tank, where he writes about the impact of digital technologies on individuals and society.
Before dedicating himself to ethics and governance consulting, he worked in corporate intelligence, compliance (KYC/AML), and reputational due diligence, with a focus on clients operating in Latin America.
- Antonio RapozoFounder · Solution Architecture
Antonio Rapozo is a senior full-stack engineer with over 8 years of experience building mission-critical systems for the government, transportation, and energy sectors.Full bio
Over the past 4 years, he has developed bridge inspection and infrastructure asset management platforms used by more than 15 state Departments of Transportation in the US, processing data related to over $500 million in annual investments.
He currently works on a browser security platform for managed service providers (MSPs), focused on protection against emerging AI threats, phishing, malware, and DNS filtering. His work transforms the browser (today the most vulnerable access point in organizations) into a secure, manageable workspace with advanced security controls, credential analysis, and productivity monitoring.
His technical expertise spans Vue.js, React, C#, .NET, TypeScript, and AWS, with the AWS Solutions Architect Professional certification. He integrates AI into production systems using OpenAI, LangChain, and Semantic Kernel, and is the creator of MemoryKit, an open-source .NET library implementing a neuroscience-inspired memory architecture for AI applications.
With a background in mechanical engineering from CEFET/RJ, a double-degree program in Germany (Deggendorf/IIK Düsseldorf), and a Machine Learning certification from Stanford, He is Brazilian, multilingual (Portuguese, English, German, Spanish), and has worked remotely with distributed teams since 2020.
His focus is on building scalable solutions that connect high-level software engineering with real business needs, from American government infrastructure to the next generation of intelligent agents.
- Enrique IbarraFounder · Agent Engineering & Product
Enrique Ibarra builds and implements AI agent systems: flows that run end to end inside the client's own infrastructure, with the team supervising.Full bio
At River Labs he leads that work and the training of client teams on Claude, from the first hands-on workshop to everyday use.
His projects have international reach, including initiatives in Europe, Latin America, and the Middle East. The focus is applying AI strategically, helping organizations adopt new technologies without unnecessary complexity. His approach combines structured thinking with a practical mindset: the right questions first, then solutions that actually work day to day across different industries.
He works at the intersection of artificial intelligence and digital development, with earlier experience in Web3 and application development, digital products, and new forms of technological infrastructure. The criterion is the same throughout: systems that improve processes, reduce costs, and increase efficiency, aligned with real business needs.
Partners
Our Approach
Three principles that don't change, regardless of what we build.
with the team running the operation, from the first conversation. Not behind a slide deck. Not handed over at the end.
your team learns to operate, modify, and evolve what was built. No permanent dependency on River Labs to keep the system running.
documented code inside your own infrastructure. You own the system. Not a license to use it. The system itself.
How we work
- Map your processes, data sources, and operational constraints
- Identify where AI creates the highest measurable value
- Define architecture and agree on success metrics before building
- Build a working system and validate it with your team in production
- Ship to production and transfer full ownership to your team
Common questions
What does the free AI Opportunity Diagnosis actually deliver?
It is a short questionnaire: 9 questions, about 4 minutes. The written diagnosis comes back by email within 48 hours, with three opportunities and action points that can improve operational flow in your area or organization.
Do we need to replace our current systems?
No. We integrate with what you already have: ticketing platforms, ERPs, document management systems, custom APIs, legacy databases. The goal is to add automated decision-making to your current infrastructure, not force a migration.
What happens after delivery? Do we need River Labs to maintain it?
No, and that's a core design principle of every project we take on. Every system includes full documentation and team training so your operation can maintain and evolve it independently. We don't design for dependency.
How do you handle AI errors and quality control?
Every system we build includes validation layers specific to your context, not generic guardrails. When the system flags a decision for human review, your team reviews it. Confidence thresholds are set with your team during the pilot. We design for appropriate human oversight, not full automation of decisions that require judgment.
Do you build automations or AI agents? What's the difference?
Automation runs a predefined step. An agent conducts the flow: it decides the next step, calls your systems, handles exceptions, and escalates to a human when the case needs one. We build systems where agents do the work end to end, your team supervises, and steps in when it chooses to. It's the difference between speeding up a task and delegating the operation.
Do you train our team or build for us?
Both, and the diagnosis decides which comes first. Sometimes the answer is training your team to work with AI day to day, with no new system at all. Other times we build the system and train your team to run and evolve it without us.
Find out exactly where your operation is leaving value on the table.
Nine questions, about four minutes. We map your operation and identify the three highest-value opportunities in your context, specific to your workflows, your data, and your team. The written diagnosis comes back by email within 48 hours.
No commitment. No SaaS. Your team owns what we build.