// autonomous systems engineering

Orchestrating Intelligence.
Scaling Certainty.

Most AI projects fail not because the model was wrong — but because nobody engineered the system around it.

Deterministic workflows Observable agents Compliance by design
Let's discuss your architecture See how we're different
20+ Years of Engineering
Leadership
3 Durable Execution Runtimes.
In Active Use.
3 Cloud Providers.
One Architecture Standard.
0 AI Features Shipped
Without Governance.

// THE PROBLEM

AI Is Everywhere.
Production-Ready AI Is Not.

01

The Demo Trap

Your AI pilot worked beautifully in a notebook. Six months later, it still hasn't made it to production. The model was never the problem — the missing system around it was.

02

The Black Box Problem

Agents make decisions, but nobody can explain how. When regulators ask for evidence, when auditors need a trail, when a customer dispute lands — your AI has no answer.

03

The Fragility Tax

Workflows break at 3am. Retries create duplicates. Partial failures corrupt state. Your team spends more time firefighting than building. This is what happens when systems are assembled, not engineered.

// MANIFESTO

Pragmatism
Over Hype.

Systems
Over Demos.

AI is moving fast — but production still rewards the same fundamentals: clarity, durability, and observability.

Every system we deliver is trusted: deterministic where it must be, adaptive where it can be, and always auditable. This is not a feature. It's the architecture.

We don't ship prototypes. We ship infrastructure — workflows that survive failures, data pipelines that preserve state, and AI agents that can be traced, measured, and governed.

In complex domains, reliability is not optional — certainty is engineered.

// WHAT WE ENGINEER

From Model to Production System.

We don't add AI to your product. We engineer the autonomous infrastructure that makes AI production-ready.

A

AI & Agentic Systems

Engineering probabilistic models into deterministic workflows.

Generative AI Engineering

LLMs embedded into real workflows — not as a UI feature, but as a reasoning component inside measurable pipelines. RAG, evaluation, and integration patterns designed for scale.

AWS Bedrock · Azure OpenAI · Hugging Face · LangChain · LlamaIndex

Agentic Orchestration

Autonomous agents that observe, plan, and execute — with constraints, human-in-the-loop where required, and clear failure modes. Multi-agent systems where governance is part of the architecture.

AWS Strands · CrewAI · AutoGen · Semantic Kernel · OpenAI Agents SDK

AI Observability & Governance

"It usually works" is not a production strategy. We instrument agent actions, tool calls, token usage, and quality signals — backed by audit trails for autonomous execution.

OpenTelemetry · Langfuse · Prometheus · Grafana

B

Intelligent Process Automation

Where durable execution meets AI-driven decision-making.

Durable Workflow Orchestration

Long-running processes that survive failures, retries, and infrastructure changes. State is preserved. Execution is repeatable. Outcomes are reliable.

Temporal · Restate · DBOS · Durable Execution

Document Intelligence

Extraction, classification, validation, and enrichment of complex documents using multimodal models — designed as production pipelines, not one-off OCR scripts.

Multimodal LLMs · RAG · Vector Search · OCR

Adaptive Compliance

Systems that encode rules, monitor changes, and keep evidence. AI assists interpretation; workflows enforce execution. Governance stays intact.

Business Rules · Event-Driven · Audit Trails · Policy Enforcement

// HOW WE BUILD

Engineering Patterns.
Not Guesswork.

Every system follows repeatable architectural patterns. Here are three we use daily.

01

The Resilient Ingestion Pipeline

PROBLEM

External APIs fail, networks time out, and partial retries create duplicates or losses.

APPROACH

Durable orchestration with idempotency, resumable state, exponential backoff, and circuit breakers.

OUTCOME

Reliable ingestion at scale. Documents are never dropped — only delayed.

02

The Observable Agent

PROBLEM

AI agents in production are black boxes — hard to debug, impossible to audit.

APPROACH

Agent actions and decisions treated as structured events. Every step traced, indexed, and visualized using distributed tracing.

OUTCOME

AI that is debuggable, measurable, and governable — not just observed.

03

Idempotent Event Processing

PROBLEM

Distributed systems duplicate messages. Events cross service boundaries and compliance constraints.

APPROACH

Event-driven design with strict idempotency keys, outbox/inbox patterns, DLQs, and audit-first data modeling.

OUTCOME

Deterministic side-effects. Systems that scale without losing traceability.

// TECHNOLOGY

The Stack.

Open standards. Cloud-native. Proven at scale.

LANGUAGES

  • .NET / C#
  • Python
  • Rust
  • Node.js
  • TypeScript

AI & AGENTS

  • AWS Bedrock
  • Azure OpenAI
  • AWS Strands
  • LangChain
  • Semantic Kernel
  • CrewAI

ORCHESTRATION

  • Temporal
  • Restate
  • DBOS

CLOUD

  • AWS
  • Azure
  • Cloudflare

DATA

  • PostgreSQL
  • Redis
  • DynamoDB
  • ClickHouse
  • Pinecone
  • Elasticsearch

OBSERVABILITY

  • OpenTelemetry
  • Prometheus
  • Grafana
  • Langfuse
  • Jaeger

// PROOF OF ENGINEERING

We Don't Just
Consult. We Build.

Many engineering firms talk about best practices. We prove ours by building production systems with them — and shipping them under our own name.

HubFiscal

In Development

Global Fiscal Intelligence Platform

The proof that our engineering standards hold in one of the most complex regulated domains on Earth. Durable orchestration, agentic compliance, and multi-country fiscal document processing — built for enterprise scale.

If we can engineer global fiscal bureaucracy, we can engineer your domain.