AI systems that
ship to production,
not just demos.
Why AI projects fail in production.
The gap between a working demo and a production AI system is where most projects stall.
Most AI projects never reach production
Proofs of concept are easy. Integrating an LLM into a live system with real users, latency requirements, and error handling is a fundamentally different engineering problem, one that most teams aren't staffed to solve.
Generic dev shops don't understand AI constraints
Building an AI product requires understanding model behavior, prompt engineering tradeoffs, retrieval architecture, evaluation pipelines, and cost management. Agencies that learned AI last quarter can't architect it properly.
LLM integration debt accumulates fast
Rushing an AI feature to market without proper observability, fallback logic, and cost controls creates a system that works in demos and fails at scale. Technical debt in AI compounds differently than in traditional software.
Vague requirements produce unusable models
AI systems require a different kind of scoping: defining success criteria, evaluation sets, and behavioral guardrails before a single model call is made. Teams that skip this ship something they can't measure or improve.
Every AI capability. Production-grade.
We build across the full AI stack, from data pipelines to deployed models to the infrastructure that keeps them running.
Large language model systems
Production RAG pipelines, fine-tuning workflows, multi-agent architectures, and prompt engineering frameworks built to behave reliably at scale.
Custom machine learning
End-to-end model development: data pipelines, feature engineering, model training, evaluation, and deployment to serving infrastructure.
Vision AI systems
Object detection, classification, segmentation, and real-time video analytics, from prototype to production inference endpoints.
AI-ready data infrastructure
Vector databases, data lakes, real-time feature stores, and ETL pipelines that give your models the clean, structured input they need to perform.
Production ML operations
CI/CD for models, drift detection, automated retraining, cost monitoring, and observability dashboards, so your AI keeps improving after launch.
AI built to survive contact with real users.
We treat production deployment as the starting line, not the finish line.
We scope AI before we build it
Every engagement starts with an AI readiness assessment, mapping your data, defining success criteria, establishing evaluation sets, and identifying failure modes. We don't write a line of model code until the problem is well-defined.
Senior AI engineers, not prompt wrappers
Our engineers have shipped production ML systems, not just called APIs. They understand model architecture, training dynamics, inference optimization, and the operational concerns that make AI sustainable at scale.
LATAM nearshore velocity
Full time zone alignment with US teams. No async bottlenecks, no end-of-day surprises. Your AI engineers are in your stand-up, your Slack, and your sprint from day one.
Built to last, not to demo
We design for maintainability: observability hooks, cost guardrails, fallback logic, and evaluation automation. You get an AI system you can operate, monitor, and improve, not one you're afraid to touch.
From problem to production AI.
AI readiness assessment
We map your data quality, define measurable success criteria, identify failure modes, and scope the MVP before any model work begins.
Data pipeline and infrastructure
Clean, structured, AI-ready data. We build the ingestion, transformation, and storage layer your models need to train and serve reliably.
Model development and integration
Training, fine-tuning, prompt engineering, RAG pipeline design, and API integration built against real evaluation sets, not vibes.
Production deployment
Containerized serving, auto-scaling, fallback logic, cost controls, and latency optimization, shipped to production with observability from day one.
Continuous improvement
Drift detection, automated retraining triggers, A/B evaluation of model versions, and ongoing performance tuning. Your AI improves with every interaction.
AI that ships. Not AI that impresses in a slide deck.
A Series B healthtech company came to us with a half-built ML pipeline and a hard launch deadline. Their previous agency had delivered a clean notebook and a broken staging environment. We assessed the codebase, rebuilt the evaluation framework, and shipped a production inference endpoint with monitoring in place, on time.
Ready to build
Your AI system built to last.
Tell us what you're building. We'll assess your AI readiness and map a path to production — no generic pitch deck, no wasted time.
Request AI Assessment