AI Engineering
AI systems that survive production
We build AI into software that people depend on: retrieval systems grounded in your own documents, agents that carry a workflow end to end, and model-backed features inside products you already ship. The work is done by senior engineers in Buenos Aires, in your time zone.
Most AI pilots demo well and stall on the way to production — not because the model is weak, but because the integration, the evaluation and the failure paths were never built. That gap is the part we treat as the actual engineering work.
What you get
- AI opportunity assessment
- An honest read on where AI would pay for itself in your business and where it would not. Some of what gets pitched as an AI problem is a data problem, a process problem, or already solved by a query — and we would rather say so than build it.
- RAG and knowledge systems
- Retrieval-augmented systems that answer from your documents, tickets and databases instead of from a model's memory — with the chunking, retrieval quality and citation paths that decide whether the answers are trustworthy.
- AI agents and workflow automation
- Agents that actually complete work: reading from your systems, calling your tools and APIs, and handing off to a person at the points where a person should decide. Scoped to the workflows where autonomy is safe to grant.
- LLM features in existing products
- Model-backed capability added to software you already run — drafting, extraction, classification, search, summarisation — built to your existing architecture rather than bolted alongside it.
- Evaluation and guardrails
- Test sets, scoring and regression checks so you know when a prompt or model change makes things worse. Plus the unglamorous half: input validation, output constraints, fallbacks, and audit trails for what the system did and why.
- LLMOps and cost control
- Deployment, monitoring, tracing and spend management for systems whose running cost scales with usage. Token spend is an architecture decision, and it is cheaper to make it deliberately than to discover it on an invoice.
- Data foundations for AI
- The pipelines, storage and permissions an AI feature needs before it can be built — including making sure a retrieval system cannot surface something the person asking is not allowed to see.
Technologies we work in
What comes up most often — not a boundary. Teams are assembled per engagement, so we staff for the stack your project actually uses.
Models
Frameworks
Retrieval
Evaluation and ops
Serving
How we work
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Find the case worth building
We start from a workflow with a measurable cost, not from a technology. If nothing in scope clears that bar, that is a useful and much cheaper answer than a pilot.
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Define what good looks like
Before building, we agree how the system will be judged — accuracy on what, refusal on what, and what an unacceptable failure looks like. Without this you cannot tell improvement from noise.
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Prototype against real data
Prototypes run on your actual messy inputs, not a curated sample. Most of what kills a deployment is visible at this stage if the data is honest.
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Harden for production
Integration with your systems, guardrails, human-in-the-loop checkpoints, observability and cost controls — the work that separates a demo from something you can put in front of customers.
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Measure and improve
Once live, evaluation keeps running. Models change underneath you, and so does your data, so a system that was correct in March is not automatically correct in September.
Senior engineers, in your time zone
There is no junior bench here, so there is nobody to rotate onto your work to keep a seat warm. And because the team works from Argentina, you get a full working-day overlap with North America — questions get answered the same day, not the next one.
Common questions
We are not sure AI is right for our problem. Can you tell us?
That is a fair place to start, and the assessment is part of the work. Plenty of problems presented as AI problems are better solved by fixing a data pipeline, a process or a search index — all cheaper to build and cheaper to run. We would rather tell you that early than sell you a model you did not need.
Do you use our data to train models?
Not unless you ask us to and we agree the terms in writing. Standard engagements use your data for retrieval and evaluation within your own environment. Where a third-party model provider is involved, we tell you which one, what it receives, and what its retention terms are before anything is sent.
How do you stop it from making things up?
You reduce it and you detect it; anyone promising elimination is selling something. In practice that means grounding answers in retrieved sources with citations, constraining outputs where the format matters, evaluating against test sets that include the hard cases, and keeping a person in the loop wherever a wrong answer would be expensive.
Which model do you use?
Whichever fits the task, the cost profile and your data constraints — and we build so that answer can change. The frontier moves faster than any engagement lasts, so tying an architecture to one provider is a risk we design against rather than accept.
Can this run on our own infrastructure?
Yes, where the requirement calls for it. Open-weight models can be self-hosted in your cloud or on-premises, which changes the cost and quality trade-offs — we will be straight with you about what that costs and where the capability gap sits.
Who actually works on my project?
Senior engineers, every time. WizardsLabs has no junior bench to keep busy, so there is no one to rotate onto your work to fill a seat. Each engagement is staffed with hand-picked engineers matched to what you are building.
How does the time-zone overlap work?
The team works from Argentina, which sits within one to three hours of US Eastern for most of the year and keeps a full working-day overlap with every North American time zone. Stand-ups, pairing and same-day answers all happen in normal business hours for both sides — not at the edges of the day.