AI Strategy & Production Systems

We know which
AI to use —
and when not
to use it.

Not a chatbot wrapper. Not a prompt template. A production AI practice that selects, fine-tunes, and operates the right model for your exact problem — with audit trails, human escalation, and 24/7 uptime baked in.

4,000+
NF-e processed
per month
78%
Workflows fully
autonomous
80K+
Signals classified
without human review
24/7
Agent uptime with
on-call ops coverage
AI Scaling Fluency

We operate where most teams stop.

Production AI is harder than a demo. We benchmark models, manage inference cost, handle edge cases, and keep it running when the API goes down.

01 — Task Complexity

Multi-agent pipelines for compound reasoning

Single LLM calls break on real business logic. We architect chained agents with specialized roles — classifier, reasoner, validator, executor — so complex workflows complete reliably, not just sometimes.

Complexity handled HIGH
02 — Compliance Depth

LGPD-compliant pipelines with full audit trails

Brazilian data law means AI systems touching PII need consent tracking, data minimization, and deletion flows. We build compliance in — not bolted on — with logs every regulator can read.

Compliance coverage MAXIMUM
03 — Production Throughput

80,000+ decisions per month, zero manual review

We benchmark inference cost vs. accuracy at your actual volume. Where a cheaper model gets it right 95% of the time, we run that model and escalate the 5% — cutting costs without cutting quality.

Monthly throughput VERY HIGH
04 — Operational Efficiency

78% autonomous — human review only where it matters

Most automation projects stall at proof-of-concept because edge cases break the agent. We build confidence scoring, escalation queues, and human-in-the-loop gates so the ratio improves over time, not degrades.

Automation rate HIGH
Model Routing Framework

Right model. Right job. Every time.

We benchmark AI models in production before selecting them. This is the actual routing logic behind live pipelines — not a vendor comparison chart.

Document intelligence
4,000+ fiscal docs processed / month
High volume, predictable structure, cost is a real constraint
high volume low variance sub-second
Fast specialist
Fine-tuned for fiscal schema — equal accuracy at 1/10th the cost of a frontier model.
Customer operations
78% of tickets resolved autonomously
Natural language, context tracking, multilingual response (pt-BR / es / en)
context aware multilingual tone control
Balanced model
Best quality-to-cost ratio. Escalates to human queue when confidence drops below threshold.
Sensitive data analysis
Medical, HR, financial — zero cloud egress
Data cannot leave the premises under LGPD Article 46 — no external API calls permitted
LGPD compliant on-premises full audit log
Local GPU
Open-weight model on X402-managed hardware. Inference stays on-premises; nothing touches an external API.
Complex orchestration
Audit, legal review, multi-step reasoning
Judgment, nuance, multi-hop reasoning required — accuracy matters more than cost here
high-stakes extended context tool calls
Frontier model
Best available reasoning capability — used selectively where the decision justifies the cost differential.
Industrial sensing
80,000+ signals classified daily, sub-100ms
Real-time, deterministic, runs at the edge — no API round-trip tolerated
edge inference offline-capable fine-tuned
Open source / edge
Fine-tuned open-weight model deployed to edge nodes inside the facility. Operates fully offline; syncs on reconnect.
Why this route
These documents have predictable structure and high volume — a fine-tuned classifier processes them at 1/10th the cost of a frontier model with equivalent accuracy. The cost difference compounds: at 4,000+ fiscal docs per month, model selection is a meaningful line on the P&L.
Live Agent Output

Three agents. Three domains. One ops team.

A glimpse at what production looks like — not a Jupyter notebook, not a demo environment. These agents run continuously and escalate to on-call humans when confidence drops.

Legacy Transformation

From manual to autonomous — three pathways.

Every organization starts somewhere different. We map the current state, identify the highest-leverage automation, and ship a working system inside 40 days.

Pathway 01

Document intelligence

NF-e, contracts, invoices, and government filings processed by an agent that reads, classifies, validates, and routes — replacing the manual ops team that currently handles it at end-of-day. Works with any format: PDF, XML, EDI, email attachment.

Pathway 02

Back-office workflow automation

Accounts payable reconciliation, supplier onboarding, purchase order matching, and inter-system data sync — turned into an agent loop that runs continuously and escalates genuine exceptions rather than routing everything to humans.

Pathway 03

Customer operations at scale

Inbound ticket classification, order status, billing queries, and escalation routing — handled in the customer's language (pt-BR, es, en) with CRM integration, LGPD consent tracking, and a human handoff queue for genuinely complex cases.

Before X402 AI
3–5 FTE manually processing documents end-of-day
No audit trail — spreadsheet-based reconciliation
Customer tickets routed manually, 4–8h response time
No LGPD data inventory — compliance risk
Machine anomalies found during — or after — breakdown
After X402 AI
Agents process 4,000+ documents/month with 78% zero-touch
Immutable log per decision — exportable for auditors
Classified and replied in <90s, humans only for complex cases
LGPD consent tracked per-record, deletion flows automated
Anomalies flagged 72h before predicted failure — zero surprise downtime
Production Engineering

Not a wrapper.
An engineering
practice.

Any agency can prompt an LLM. We build the surrounding system that makes AI reliable in production — the retries, the observability, the compliance layer, the on-call rota, the GPU infrastructure, and the benchmarks that tell you when to switch models.

Error handling Retry logic Audit trails Human escalation ERP integration LGPD compliance Observability On-call ops Model benchmarking Fine-tuning GPU infrastructure Air-gapped deploy
AI Strategy

Let's talk about
your specific problem.

We'll tell you which AI approach makes sense — and which ones don't. No retainer, no pitch deck. Just an honest technical assessment within 24 hours.

Email Mathias → See our work