AI in products and processes

Applied artificial intelligence in Argentina

We embed generative AI, agents, and automation into real products and workflows — with evaluation, security, and production rollout.

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Beyond the demo

This page covers implementing AI in product and operations. If you already have a fragile demo, the right path may be our AI Stabilization Sprint.

•Generative AI embedded in product flows (assistants, generation, classification).
•Agents and automation with clear tools and guardrails.
•RAG and knowledge integrations when they provide reliable context.
•Evaluation, observability, and security controls before production.

What we implement (real capabilities)

AI in the product

AI features users touch: versioned prompts, UX, and cost limits.

Automation with AI

Workflows combining rules, APIs, and AI where human judgment does not scale.

RAG and integrations

Context retrieval from documents or internal systems with citations and access control.

Production and governance

Offline/online evaluation, logging, rate limits, and risk review.

From use case to production

Use case and risks
We define value, available data, tolerable failures, and success criteria.
Evaluable prototype
We build a measurable slice — not a slideware demo.
Hardening and release
Observability, security, and deployment with rollback and monitoring.

Technologies we use

Ruby on Rails
Next.js / React
TypeScript
Flutter
React Native / Expo
Swift (native iOS)
Kotlin (native Android)
PostgreSQL / Redis
Vercel
Railway
Render
GCP / Docker / CI/CD

Industries and contexts

•Startups and scale-ups that need speed with engineering quality.
•Companies modernizing processes with proprietary software or integrations.
•Product teams that need senior reinforcement without hiring overhead.
•Technical and non-technical founders looking for a reliable execution partner.

Different from AI Stabilization

AI Stabilization fixes fragile demos. This landing covers adding AI where it is not yet — or expanding it with product rigor.

•We do not claim capabilities we do not offer.
•We link to stabilization when the problem is reliability of an existing prototype.
•We combine with custom software and MVP when AI is a product layer.
•Production focus: cost, latency, quality, and security.

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Frequently asked questions

Do you do classic ML / model training?

Our current focus is generative AI, agents, RAG, and product integration. Classic ML is evaluated case by case.

Do you use OpenAI or other providers?

Yes — we integrate model APIs based on cost, latency, and data requirements.

What if I already have an unstable prototype?

You likely want the AI Stabilization Sprint: audit, refactor, and stable deploy on a fixed timeline.

Can you automate internal processes with AI?

Yes, often combined with deterministic automation. See also our automation landing.

Related services

Want AI in production, not on a slide?

Tell us the use case. We propose implementation, stabilization, or a scoped MVP.

Talk to Fixe See reference pricing

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Let’s align on the next step

Tell us what you need to build or stabilize and where things stand today.

AI in Products & Processes | Fixe Argentina