Our verdict
NaNLABS has built production software for more than 13 years and has worked with more than 150 US companies, and its recent work centers on AI agents, retrieval systems, and data engineering that holds up under security reviews (homepage; culture page). It suits a US product company that wants a senior Argentine team in a nearby time zone to own data-heavy or AI features, and it is less suited to a buyer that wants AI strategy consulting without engineering.
The right buyer
- Data-heavy and real-time products. It built an observability platform for a US electric vehicle charging network with more than 30,000 stations (case studies).
- Teams rebuilding a system that cannot go down. It moved HyreCar from a monolith to microservices over five years without interrupting the live product and helped double engineering velocity (case studies).
- Security-minded buyers. It rebuilt WootCloud’s IoT security platform as serverless and cut costs by 44%, and it builds AI systems with audit trails and SOC 2 controls in mind (case studies; homepage).
Reasons to look elsewhere
- Its clients are mainly US product companies, so European buyers will find fewer references.
- With about 70 people, it is sized for product teams, not for large enterprise programs.
NaNLABS, At A High Level
Four developers founded NaNLABS in 2013 with the idea that people are “Not a Number,” which gave the firm its name; it has grown from 4 to about 70 people (culture page). Matías Alvarez Durán is its co-founder and chief executive (CTO Colloquium).
- Headquartered in La Plata, Argentina, with a presence in the United States and France (contact page; case studies).
- About 70 people and more than 150 US clients by its own count; we estimate annual revenue at $3.5 million to $4.5 million (culture page).
- Services cover AI-native product development and system modernization, with depth in agentic workflows, multi-format retrieval, agent identity and authorization, applied machine learning, and data engineering (homepage).
- Works through its own NaNLABS Method, which combines delivery playbooks, a project-specific AI harness, and delivery metrics shared with client leadership (homepage).
Scores by criterion
We scored NaNLABS from its homepage, culture, case study, and contact pages and its chief executive’s writing. Production track record, technical depth, and delivery score highest; commercial clarity scores lowest because it publishes no terms.
| Criterion | Weight | Score | Basis |
|---|---|---|---|
| Production track record | 20% | 6.5 | 13 years of production software; published cases for HyreCar, WootCloud, Equinix, and an EV charging network. |
| Technical depth | 15% | 6.5 | AI agents, retrieval, real-time data engineering, and cloud-native systems. |
| Industry depth | 10% | 6.0 | Insurance, cybersecurity, e-mobility, and finance cases. |
| Delivery model and handoff | 15% | 6.5 | Senior embedded teams that own delivery, with shared playbooks and metrics. |
| Commercial clarity | 10% | 5.0 | No published rates or minimums. |
| Platform independence | 10% | 6.5 | Model-portable AI harness; cloud-native on major providers. |
| Stability and scale | 10% | 5.5 | Founded 2013; about 70 people. |
| Independent evidence | 10% | 5.5 | Attributed client quotes and conference speaking. |
| Overall | 100% | 6.1 | Weighted average |
Where NaNLABS fits
NaNLABS is used by US startups and growth companies that want a nearshore team to build data platforms, AI features, and cloud systems while working in US hours. On price it sits in the middle of Latin American nearshore firms, below US agencies. Competitors on this site include Arionkoder and Mutt Data for Argentine AI and data teams, and Rootstrap for nearshore product engineering.
| Need | Fit | Why |
|---|---|---|
| A real-time data or IoT platform | Strong | It built observability for more than 30,000 EV charging stations and real-time IoT security processing. |
| AI agents or retrieval inside a product | Good | Agentic workflows and multi-format retrieval are core services. |
| Modernizing a live system | Strong | It moved HyreCar to microservices without stopping the product. |
| AI strategy without a build | Weak | Its model is embedded engineering teams. |
What clients and leaders say
NaNLABS’s leaders and clients describe its work in published announcements. One buyer concern is written from public facts about the firm.
“After a poor experience with our previous agency, we came into this project from a very skeptical place with extremely high expectations - and NaNLABS exceeded them in every way.”
“The team’s been very quick to get up to speed and has already implemented some fairly transformative capabilities for us.”
“From an early stage, the team was asking a lot of the right questions, giving pushback when necessary, and trying to better understand the product.”
“The engineers pushed back and owned the work, but their published cases lean toward data and cloud projects, so we asked to see AI agent work in production before expanding.”
Where customer reviews couldn’t be found, our analysts attempted to give a voice to buyer sentiment, drawing on real information from review sites, forums and product documentation. Based on: NaNLABS’s case studies and the services list on its homepage.
Who leads NaNLABS
Matías Alvarez Durán co-founded NaNLABS and is its chief executive (CTO Colloquium).
