Artificial intelligence
We integrate Claude and GPT where they actually move a metric: an assistant that answers from your own documents, an agent that closes a manual process, a 24/7 WhatsApp assistant or automatic reading of invoices and contracts. With quality evaluation, cost control and security from day one. No demos that never reach production.
Explore a use caseSix project types that combine depending on the problem you want to solve.
Conversational assistants for support, sales and operations inside your app, your website or WhatsApp Business.
Agents that run complete workflows: read, decide, call your systems and deliver a verifiable result.
Answers grounded in your documents, manuals, contracts and databases, with citations to the source.
Automatic reading of invoices, IDs, contracts and forms, with validation rules and human review when confidence is low.
Intelligent features on iOS and Android that improve the experience and unlock new use cases.
What separates a pilot from a production system: measured quality, contained risk and controlled cost per use.
High-volume processes with clear rules and data that already exists. Each one is measured against a metric agreed before we start.
The same questions arrive by chat, WhatsApp and email around the clock, and the team cannot keep up.
Invoices, receipts and vendor documents are keyed into the accounting system by hand.
Manuals, contracts and procedures exist, but nobody finds the answer in time.
Building a quote means checking price lists, inventory and terms across several systems.
Inspections, deliveries and visits are reported with photos and notes that someone transcribes later.
Your app competes with alternatives that already offer smart search, recommendations or assistants.
We identify the highest-impact process, the data available and how we will measure the outcome. We define what AI does and what a person keeps doing.
In 2 to 4 weeks we build a working prototype on your data to validate quality, cost and adoption before investing in the full product.
We connect the solution to your app, website or internal systems, with access control, prompt-injection defenses and monitoring of cost and quality.
We evaluate answers against test sets, tune prompts, models and data, and report impact on the metric defined at the start.
Customer support, document processing, quoting or back-office work that consumes people's hours every day.
Apps and platforms that need intelligent features to retain users and compete.
With a pilot or a generic chatbot that needs to reach production with quality, security and costs under control.
Secure AI
An assistant with access to your documents or an agent that takes actions is also a new attack surface. We test for prompt injection, data leakage and excessive permissions before your AI reaches production.
A prototype on real data typically starts at $4,000 and takes 2 to 4 weeks. Full production integration depends on the systems involved and usage volume. Beyond development, we estimate the monthly model cost from the start based on your expected volume.
We work with Claude by Anthropic, GPT by OpenAI and open-source models such as Llama or Mistral depending on the case. The choice depends on required quality, cost per use, latency and privacy requirements. We design the solution so the model can be swapped without rewriting the product.
A chatbot converses and answers questions. An agent also takes actions: it queries your ERP, opens a ticket, drafts a quote or updates a record, following rules and with human approval on critical steps. That is why agents need more care around permissions, traceability and security.
Yes. We use enterprise APIs that do not train on your data, apply per-user access control in RAG solutions, and can deploy models on your own infrastructure when data sensitivity requires it. Our cybersecurity team reviews the solution against the OWASP Top 10 for LLM Applications before it goes to production.
We build a set of real questions and cases with the expected answer and use it to evaluate every change of prompt, model or data. In production we track accuracy, escalations, satisfaction and cost per conversation, and review samples with your team.
Not as a requirement. Part of discovery is assessing what data exists, its condition and what can be achieved with it. Many use cases start with the documents and systems you already have.
Yes. We integrate AI features into existing apps and systems, built by us or by third parties, through APIs and SDKs. There is no need to rebuild the product.
You get senior, English-fluent engineers in US time zones (UTC-5) at LatAm rates, working in real time with your team. We combine AI integration with the mobile, backend and security experience needed to ship a complete product, not just a model call.
What to know before you invest: real costs, architecture, model choice and how to measure results.
A practical guide to AI agents for business: how they differ from chatbots and RPA, use cases, architecture, human approval, ROI and when not to use them.
What enterprise RAG is, how it works step by step, how to enforce document permissions at retrieval time and what you need for a reliable AI assistant.
What it costs to implement AI in business: development, token usage, maintenance, indicative ranges by project type and practical ways to control spend.
Tell us how you operate today and we will propose a use case with metric, scope and estimated cost within 48 hours.
Talk to the AI team