AI with judgment, from diagnosis to production
We help you decide where AI creates real value, which architecture to choose, and how to move from pilot to production — with guardrails, eval, and grounding. We don't build AI chatbots: we do engineering.
The problem we solve
There are two common ways to get AI wrong. The first is hype: buying the promise, building a pretty pilot, and never reaching production — the eternal PoC, which consumes budget and changes nothing in the operation. The second is paralysis: not knowing where to start, which model to choose, what can run self-hosted, what is a compliance risk.
There is a third, subtler and more expensive: shipping a RAG that looks fine in the demo and, in the real world, invents answers, leaks context, and leaves no trace of how it decided. ConsoliDados AI consulting exists to solve all three — from the standpoint of people who treat AI as engineering, not as a trends fair.
How we work
We start by diagnosing where AI creates real value in your context — and, with the same honesty, where it does not. From there, we design the reference architecture: when it is a case for RAG over the knowledge base, when it is an agent with tools, when it is deterministic automation with a touch of AI, and when the right answer is not to use AI.
The edge is in what we call the Harness — a RAG alone is not enough. We define the guardrail strategy (what the AI can and cannot assert), the eval strategy (how to measure quality before and after deploy), the grounding strategy (ensuring every answer comes from the correct sources), and the audit trail (being able to attest how each decision was made). That approach is what sustained AI in production at a clinic in a regulated environment: self-hosted Llama with LangChain, answers restricted to the approved guidelines.
Model choice and the build-vs-buy decision are calibrated to cost, privacy, and latency, not fashion. For sensitive data, we assess self-hosted options and the impact of LGPD/GDPR. The deliverable is a pilot-to-production roadmap with measurable success criteria — because a pilot with no exit criterion is just a recurring cost in disguise.
What we deliver
An honest diagnosis, a justified reference architecture, a concrete verification strategy, and a clear path to production. The entry point is the Discovery Pack, whose fee is credited if implementation moves forward — with the same team.
- Diagnosis of where AI creates value (and where it does not)
- Reference architecture: RAG, agents, fine-tuning, or none of it
- Harness strategy: guardrails, eval, grounding, and audit trail
- Build-vs-buy decision calibrated to cost, privacy, and latency
- A pilot-to-production roadmap with measurable success criteria
- Privacy and compliance assessment (LGPD/GDPR), including self-hosted
Investment ranges
Discovery Light
Focused technical consult: decide between stacks, evaluate an RFP, or code review.
$2,000 – $4,000
- 5–10 business days
- 1h kickoff + analysis
- 8–15 page document
- 1h presentation
Discovery Standard
Deep project analysis: scope, architecture, stack, and 3 priced alternatives.
$4,000 – $9,000
- 2–3 weeks
- Kickoff + 1–2 sessions
- Code/infra review
- 20–35 page document
Discovery Deep
Multi-stakeholder: workshops, process mapping, phased roadmap, change management.
$8,000 – $15,000
- 3–4 weeks
- 2–3 kickoff sessions
- Workshops
- 35–60 page document
- 2 presentations
Qualitative ranges. The exact figure comes out of Discovery, and is 100% credited toward the project.
FAQ
Are you just another consultancy that ships a trends deck?
No. We are a senior-led boutique for companies that want direct technical judgment, without management layers. You talk to the person who decides and implements — and the deliverable is executable architecture, not a slide.
What is the Harness you mention?
It is the difference between a RAG that returns anything plausible and AI you can trust: data verification, guardrails, grounding in the correct sources, and an audit trail. The AI is constrained to what it can assert, and every answer is attestable. That is how we put AI into production at a regulated clinic.
Do you implement or only recommend?
Both. The consulting can end in an architecture report or move into implementation — agents, automation, custom software. We do not recommend what we could not execute.
How does it start?
With the Discovery Pack, which produces a diagnosis, a reference architecture, and a qualitative range estimate. The fee is credited if implementation moves forward.