Service · Enterprise AI development
AI that solves real problems. Not pretty demos.
We build production AI systems on the full stack — Dell, NVIDIA, Canonical and Claude: agents built with NVIDIA NIM on top of open Nemotron models, enterprise RAG, computer vision, digital twins and predictive ML. From a POC in 3 weeks to 24/7 operation.
Summary
Tribucorp builds custom production AI systems: agents and RAG built with NVIDIA NIM and open Nemotron models, computer vision on NVIDIA Metropolis, digital twins and predictive ML. We work with clients in healthcare, water, construction, agtech and media from Chile across LATAM. We deliver a POC in 2–4 weeks and operate the infrastructure — Dell, NVIDIA and Canonical — on-prem or hybrid with enterprise SLAs.
Technical capabilities
The full modern AI stack, operated in production.
We choose the technique based on the problem, not the other way around. Every vertical in the group combines several capabilities — these are the ones you'll find working with us.
- 01
LLMs & agents
We build agents with NVIDIA NIM microservices on top of open Nemotron models, on-prem when data can’t leave. Enterprise RAG, NeMo for training and fine-tuning, and Claude Code adoption to speed up your engineering team.
NVIDIA NIMNemotronNeMoLangGraphClaude Code - 02
Computer Vision
Medical segmentation, agricultural detection and real-time video analysis with NVIDIA Metropolis.
TAO ToolkitDeepStreamYOLOSAMMetropolis - 03
Digital Twins
Digital twins of operations to simulate, predict and optimize — AGUA.AI reports reductions of up to 42% in non-revenue water in specific deployments.
OmniverseIsaac SimModelicaFMU - 04
Multi-agent systems
Agents built with NVIDIA NIM and Nemotron models that cross-check each other to reduce hallucinations in critical domains like healthcare and compliance.
NVIDIA NIMNemotronCrewAIAutoGenLangGraph - 05
Voice & Audio AI
STT, TTS, voice cloning and AI-driven music generation.
WhisperCoquiElevenLabsRivaBark - 06
Predictive ML
Predictive models for crop yield estimation, anomaly detection and financial scoring.
XGBoostProphetPyTorchTensorFlowTriton
Cases in production
The group's verticals are our best portfolio.
Every in-house AI product we operate is proof of what we can build for you.
How we work
From the first conversation to AI running 24/7.
- 01
Discovery
We understand your domain, data and constraints. We define success in business metrics, not model metrics.
- 02
Rapid prototype
We build a working POC in 2–4 weeks with real data — no demos with synthetic data.
- 03
User validation
The team that will use the AI validates it before moving to production. We iterate until it is genuinely useful.
- 04
Productionization
Scalable architecture on the full stack — Dell, NVIDIA, Canonical — with evaluations, guardrails and observability from the first deployment, especially for agents built with NVIDIA NIM in production.
- 05
Continuous operation
Drift monitoring, scheduled retraining and model evolution alongside the business.
The full stack
An official partner at every layer.
From silicon to agent: Dell (hardware), NVIDIA (acceleration and agents), Canonical (platform) and Claude (language models). This is how we keep your project from stalling between layers.
Frequently asked questions
What people ask before signing.
How long until I have a working POC?
Between 2 and 4 weeks. We work with your company’s real data from day one and deliver a prototype that already produces value — not a demo with synthetic data.
Do you work with on-premise or cloud data?
Both. For on-prem we deploy Dell infrastructure with the Canonical platform (Ubuntu Pro, Canonical Kubernetes) when data cannot leave the premises, such as healthcare, banking or government. For hybrid environments we use NVIDIA AI Enterprise and Claude via Amazon Bedrock or Google Vertex AI when it fits.
Which AI models do you use to build agents?
We build agents with NVIDIA NIM microservices on top of open Nemotron models, including on-prem when data cannot leave the environment. We always evaluate by total cost, data privacy and quality in the specific domain. Claude, for its part, we adopt as a language model for our own teams and our clients’, through the Claude Partner Network.
Do you offer an accuracy guarantee?
We do not offer a generic accuracy guarantee. For each project we can contractually agree on thresholds for jointly defined business metrics (for example, minimum precision or recall for a use case), measured through continuous evaluations. In critical domains we use multi-agent architectures that reduce hallucinations, and AI supports, rather than replaces, professional judgment.
Do you train our internal team?
Yes. It includes training on Claude Code and the agent patterns we build, not just documentation — we don’t want you to depend on us forever.
How much does an AI project cost?
It depends on scope. A POC starts at USD 15K–30K. A production system with infrastructure typically runs between USD 80K and 350K depending on the case. Free quote after the discovery session.
What role does Claude play in development with Tribucorp?
We are members of Anthropic’s Claude Partner Network, and in that role we support Claude adoption: Claude for your business teams and Claude Code for your engineering team, with access via API or through the Amazon Bedrock and Google Vertex AI clouds. The agents we build — enterprise RAG, document analysis, internal copilots and operations agents — are developed with NVIDIA NIM microservices on top of open Nemotron models, including on-prem when data cannot leave the environment.
Got a concrete problem that AI can solve?
30 minutes of conversation is enough to know if it makes sense. We bring a technical team — not salespeople.