Artificial intelligence
AI development and integration
Models, agents, RAG, and computer vision wired into your operation — not loose demos.
The problem
Many teams have already tried a chatbot or a notebook. The hard part is putting AI in a real flow: data, permissions, cost, evaluation, and a human who can take over.
How we approach it
We start from the use case and the data you actually have. We define usage limits, quality evals, and a plan to watch cost and latency. We integrate models (ours or third-party) with your APIs, documents, and channels — web, WhatsApp, or internal systems.
Typical deliverables
- 01Use-case and data-risk discovery
- 02AI architecture (RAG, agents, CV, or classical ML)
- 03Integration into existing products, APIs, or channels
- 04Guardrails, evaluation, and an operations plan
- 05Documentation and handover to your team
Related cases
HELIX-AI
A ViT tokenizer with residual spherical quantization (R-BSQ) and a causal Transformer prior, implemented in Rust with Burn.
LTNN
A network that combines Logical Neural Networks with transformers: logical masking in attention, bidirectional reasoning, and more explainability for NLP.
SeizureX
Applied research to assist epileptic-seizure detection from EEG signals.
NeuroAlpha
Extracting patterns from noisy neurophysiological signals, with signal methods and machine learning.
EktenAI
Applied AI in the workflow: from a proof of concept to an integration a team can actually use.
Dalton
A Mixture of Experts architecture with multi-head latent attention, aimed at more efficient inference.
Quote AI integration
Tell us the problem. We scope phases and investment in MXN or USD — no improvised proposal.