AI in production, not in presentation.
We apply artificial intelligence to enterprise operations with discovery, proof of value on real data, governance and traceability — from first pulse to production SLA.
AI applied where it delivers measurable ROI
Five fronts we apply to real operations — all auditable, all integrable with the systems your company already runs.
Corporate Assistants
Internal chat connected to the company's knowledge base. Answers with cited sources, respects permissions and keeps a log of every query.
Executor Agents
Agents with tools that execute real actions on internal systems — open tickets, query APIs, update records — with human approval where needed.
RAG / Knowledge Ops
Semantic search over internal bases: contracts, technical norms, manuals, historical tickets. Continuous indexing, tenant isolation.
Decision Analytics
Classification, forecast and anomaly detection on operational data. Models tuned to your domain, with explainability in the result.
Intelligent Automation
Structured extraction from non-standard documents — reports, invoices, contracts, emails. JSON output ready to integrate with your ERP.
From discovery to production SLA
Five phases we apply to every enterprise AI project — from NDA alignment to ongoing support.
Discovery under NDA
Mapping of the real problem, available data, legal constraints and success metrics. All under NDA, before any line of code.
Proof of Value
PoV on real data (anonymized when applicable) in a short window. Measures precision, latency and operational gain before production commitment.
Legacy integration
Connection to ERPs, DBs, queues and existing APIs. We treat legacy systems as first-class citizens, not obstacles.
Hardening + LGPD
Tenant isolation, prompt minimization, controlled retention, auditable logs, threat modeling. LGPD applied by default, not optional.
Support
Continuous operation with SLA, model observability, retraining when justified and metric-driven evolution — not driven by hype.
Discovery under NDA
Mapping of the real problem, available data, legal constraints and success metrics. All under NDA, before any line of code.
Proof of Value
PoV on real data (anonymized when applicable) in a short window. Measures precision, latency and operational gain before production commitment.
Legacy integration
Connection to ERPs, DBs, queues and existing APIs. We treat legacy systems as first-class citizens, not obstacles.
Hardening + LGPD
Tenant isolation, prompt minimization, controlled retention, auditable logs, threat modeling. LGPD applied by default, not optional.
Support
Continuous operation with SLA, model observability, retraining when justified and metric-driven evolution — not driven by hype.
Auditable trust, not promises
LGPD applied in architecture, not in a page of terms.
Confidential under NDA
Applied to 100% of engagements
No training on client data
Prompts and usage data do not feed back into shared models. Contractually guaranteed.
Tenant isolation
Vector bases, logs and contexts separated per client — no possible cross-leakage.
Prompt minimization
We send the model the minimum data necessary — PII redacted by default when not essential.
Auditable logs and controlled retention
Every interaction recorded with identifier, context and result. Retention policy defined with you.
Designated DPO
Active data protection officer with a direct channel for data subjects and authorities.
See the full policy in LGPD & Privacy.
View LGPDCases where AI was the central piece
Anonymous cases under NDA — sector, problem, approach and metric measured in production.
- Problem
- Manual classification of non-conformities in technical reports consuming days of engineering.
- Approach
- Extraction pipeline + supervised classification with active human review on low-confidence cases.
- Metric
- 84% precision validated vs human review, average latency 1.8s per report.
- Problem
- Daily reception of fiscal documents in PDF, image and email, with no structural standard.
- Approach
- IDP with OCR + LLM with schema enforcement, human fallback for critical fields.
- Metric
- 92% of documents processed without human touch, review cycle down from 8h to about 1h.
- Problem
- Compliance team spending hours looking up precedents in internal and regulatory norms.
- Approach
- RAG over internal base + agent with search tools on official sources, with mandatory citation.
- Metric
- Average response time to internal queries dropped from 45min to 3min, with 100% cited answers.
Serious tooling, no fanaticism
We select model and infra by the problem, not the branding. Current production stack:
Frequently asked by CTO/CIO
Will my prompt data train any model?
No. We work with model settings and contracts that forbid using your data for training. Both in proprietary models (via contractual opt-out) and in isolated open-source deployments.
How do you isolate data between clients?
Each client operates in a logically isolated tenant: separate vector base, isolated prompt context, segregated logs and credentials. There is no context reuse across clients under any circumstance.
Do you use proprietary or open-source AI?
Depends on the problem. When precision and evolution speed matter, we use Claude or GPT-4. When sovereignty, cost or privacy matter, we deploy Llama or Mistral on the client's infra. The choice is technical, not ideological.
How do you measure AI ROI?
We define the baseline metric with you BEFORE the PoV — cycle time, cost per operation, current precision — and measure the same metric in production. Without that baseline, the project does not start.
What happens when the model is wrong?
Every critical flow has a defined fallback: route to human, deterministic rule, or explicit rejection. Errors are logged with full context for analysis and continuous tuning.
How do you avoid hallucination in critical decisions?
Three layers: RAG with mandatory source citation, schema enforcement on model output, and active human review on cases below the confidence threshold. No model decides alone where the cost of error is high.
Let's take AI from PoC to production.
A call under NDA to size the proof of value with real data, model contract and a LGPD governance plan from day one.