We come in where the problem is.
Sometimes it is a diagnosis. Sometimes it is a system that is already running and needs to be assessed, a proposal of ours, or the implementation of something already decided. Each piece of work stands on its own: the result carries on with us or with whoever you choose.
Assessing what is already running
An AI system is already in use and nobody can say how far to trust it. We assess it with the four questions we ask of every technology: can it be audited, reproduced, verified and explained? And we tell you what to change.
Workflow diagnosis
We look at the workflows that already exist, with or without AI, from the technology side and from the side of value and risk. We assess, suggest what to change and follow the change through.
Proposal
Where AI has not come in yet and should. We bring the opportunities and help the company set the criteria for choosing what to do first: value, risk, effort and the maturity of the people who will use it.
Work design
The whole workflow, redesigned with the people who run it: who does what, where AI comes in, where the decision stays with a person, how learning continues and what gets measured, in the business and in the work.
System architecture
The computational design behind the workflow: which models, which data and which rules, how the agents are chained together, where each piece of data lives and how each answer is checked.
Implementation
We build it and put it into production, side by side with the company's team:
- semantic layer and ontologies on top of the data that already exists
- knowledge engines, with deterministic rules and a trail for every claim
- multi-model agentic chains, with a person at the decision point
- integration with the company's systems and tools
- continuous evaluation and cost control
- the handover to the people who will keep the system alive
The technical questions we answer.
We don't sell a technology. We know the terrain and choose with you. The names are examples of what comes up in these conversations, not a closed list.
Multi-model architecture: each step with the model that fits it, continuous comparison between providers, and switching models without rebuilding the system.
Each step with a model of the right size, caching what repeats, lean context, calculation done outside the model and spending measured per workflow. Whoever calculates does not write, and that saves money too.
Context engineering: the model gets what it needs to know, at the right time and with a source. Semantic layer, ontologies, knowledge graph and GraphRAG.
A semantic layer on top of the data platform: every metric and every definition in one place, agreed with the departments, and the AI reads from there.
Bitemporality: recording when a fact was true and when the company learned about it, so you can reconstruct what was known on any date. Data lineage, from the source to the answer.
Deterministic rules and symbolic AI before the model: whoever calculates does not write, and every claim has its own trail.
Agentic chains with explicit state, verification at every step and a person at the point where the decision is theirs to make.
Almost always, context and retrieval come first. Fine-tuning teaches behavior; the facts live in the graph, where they can be checked and updated.
Open models running inside the company or in a cloud in the right region. Only what is allowed to leave, leaves.
An official path faster than the shortcut: a model gateway, usage rules, a record of what each team does and what it costs.
Continuous evaluation: every step measured, in business results and in the work of the people who use it.
Roles, risks, records and reviews, written down and practiced, aligned with the standards and laws of each country where the company operates.
Four questions for every technology, before it goes into a project.
Auditability
Can we reconstruct afterward what the system did, and why?
Reproducibility
Does the same question, under the same conditions, get the same answer?
Verifiability
Can every answer be checked against a source?
Explainability
Can we say, in the user's own language, why the answer is what it is?
The terrain the network knows, uses, evaluates or studies.
The list changes with the market. The network evaluates new technologies and gets certified in them all the time.
OpenAI, Anthropic, Google Gemini and Vertex AI, Meta Llama, Mistral, DeepSeek, Qwen, AWS Bedrock, Azure AI Foundry
MCP, LangGraph, LlamaIndex, LangChain, Temporal
Databricks and Unity Catalog, Snowflake, dbt Semantic Layer, Cube, AtScale, LookML
Neo4j and Cypher, GraphRAG, RDF, OWL, SPARQL, SHACL, Protégé, Stardog, Ontotext GraphDB, Amazon Neptune, TypeDB, Apache Jena
XTDB, Datomic, OpenLineage
rule engines, Datalog, OWL reasoners
pgvector, Qdrant, Weaviate
vLLM, Ollama, LoRA and QLoRA, Unsloth, TRL
Langfuse, Arize Phoenix, evaluation sets
ISO/IEC 42001, LGPD, GDPR, EU AI Act
Two real problems, solved.
Auditable analytical intelligence
For a performance agency in Brazil, Mexico and the United States: whoever calculates does not write. A deterministic layer checks every fact before the AI writes, and every claim in the report has a traceable origin.
Read the case →BLIU: creative memory
A knowledge graph that holds years of notes and references from comedy writers, in use in their creative sessions. The AI provokes, questions and shifts perspective; the decision and the authorship remain human.