AI pilots don’t become
production systems on their own.
We design the data architecture, governance workflows, and AI integration patterns that move teams from experiment to operational system.
The gap between an AI pilot and an operational AI system is not a model problem.
Most organizations have run an AI pilot. Few have operational AI systems. The difference is not the model—it is the architecture, the data layer, and the governance that surround it.
Data is fragmented
AI depends on data it cannot reliably access. Processes run across tools that do not talk to each other. There is no unified data layer, no quality controls, no lineage.
No governance model
Who owns the AI output? Who validates the answer before it becomes a decision? Without defined approval boundaries, every output is both used and distrusted.
Workflow disconnected
The prototype runs in a notebook. The actual workflow runs in email, spreadsheets, and manual steps. They are parallel, not integrated.
Unclear ownership
The project started with enthusiasm. Now it sits in a shared folder with no owner, no maintenance plan, and no metrics. Nobody knows if it still works.
Low adoption
The team was not involved in the design. The interface does not match how people actually work. The system exists; nobody uses it.
No traceability
Decisions supported by AI have no audit trail. When something goes wrong, there is no way to reconstruct what the model saw, what it returned, or why.
What we build and deliver
Every engagement is scoped to a specific operational problem. We do not sell “AI” in general. We solve the specific architecture and workflow problems that are keeping AI pilots from becoming systems that work in production.
AI Process Audit + Opportunity Map
A structured review of your current processes, data flows, and AI readiness. We identify concrete integration points, estimate operational leverage, and produce a prioritized roadmap. Not a slide deck — a workable plan with clear next steps.
Back-Office and Process Automation
AI agents that handle classification, extraction, routing, and reporting across back-office workflows. Designed to integrate with existing tools, with human escalation protocols and full audit trails. Not a black box.
Supervised Web and WhatsApp Assistants
AI assistants for client-facing channels — web chat, WhatsApp Business — with human review gates, escalation logic, response validation, and audit trails. Human-supervised by design, not as an afterthought.
Internal AI Agents for Operations
Agents for recurring operational work: report generation, data classification, document analysis, and decision support. Built for your specific context, integrated into the tools your team already uses.
AI-Ready Data Architecture
Design the data layer your AI systems will actually need. Unified access patterns, lineage tracking, quality controls, and governance model — before the models, not after the fact.
Work you can read, not just claims you can read about.
GitHub is the evidence layer. Each lab track is a category of active, public work demonstrating a specific type of engineering capability. Below is what each track proves — and how to verify it directly.
Reproducible research implementations
Working implementations of AI and ML research papers. Each project takes a published paper and reproduces its core algorithms with clean, testable code. Not demos — implementations verified against paper results, with CI.
- Paper-grounded: every class and function maps to a specific section
- Reproducible: key tables and figures verified numerically
- CI-validated: smoke tests on every push
Proves: rigor, evaluation discipline, ability to work from primary sources.
Syntran-Labs/paper-lab →AI engineering operating system
SYNTRAN AIEOS — the governed workspace for AI-assisted engineering. Specialist agents, execution skills, explicit approval gates, domain knowledge packs, and usage telemetry. The system that governs all SYNTRAN work, open source.
- Governance model active in production across all projects
- Reusable agent and skill definitions with output contracts
- Telemetry layer: every session is measured and traceable
Proves: production-grade AI governance, systems thinking, operational discipline.
Syntran-Labs/syntran-aieos →Knowledge transfer and technical education
Structured learning projects, documented engineering processes, and materials designed for knowledge transfer to engineering teams and non-technical stakeholders.
- Structured documentation following Diátaxis taxonomy
- Engineering concepts explained for varied audiences
- Education-first design: code is the vehicle, understanding is the goal
Proves: communication capability, documentation discipline, knowledge transfer.
Syntran-Labs/learning-lab →Specific work, not capability claims.
Each demonstrator is a real project with a specific architectural objective. Links go directly to the code on GitHub. Read the README, inspect the structure, check the tests.
Governed LLM Scientific Workflow
Uses Wolfram's 256 Elementary Cellular Automata rules as a finite, reproducible testbed for LLM-assisted hypothesis generation. Tests whether governed AI workflows can produce falsifiable, non-overclaiming scientific outputs. Governed by SYNTRAN AIEOS.
Python · SYNTRAN AIEOS · Jupyter · pytest
View repository →AI Engineering Operating System
The SYNTRAN AIEOS in full. Specialist agents, execution skills, explicit approval gates, domain knowledge packs, and usage telemetry. The governance system that underlies all SYNTRAN work — open source and documented.
Markdown · PowerShell · Claude Code · Windows-first
View repository →RAG + Knowledge Graph for Dataset Discovery
Implementation of Diamantini et al.'s graph RAG approach to dataset discovery. Two-layer knowledge graph (BKG + SKG), KG-enriched query generation, preference-oriented ranking, and explainability. Tables 1–5 reproduced.
Python · NumPy · pandas · matplotlib · pytest · GitHub Actions
View repository →Automated Technical Analysis Reports
Dual-format report generation — PDF for human reading, JSON for LLM consumption — from a single ETF analysis pipeline. All computation routes through a single source-of-truth dataclass. 21 passing tests. Demonstrates AI-ready data design.
Python · yfinance · pandas · ReportLab · pytest
View repository →How operational AI systems are actually built.
These diagrams represent the structural patterns applied in every engagement. They are not abstract frameworks — they are the practical architecture of moving from experiment to production.
From business problem to operational decision
Identify the process, the decision, the bottleneck
Unified access, lineage, quality controls
Agents, prompts, validation, tracing
Escalation gates, approval protocols
Traceable, auditable, reversible
AI readiness maturity ladder
Most organizations operate between Level 1 and Level 2. SYNTRAN engagements target Level 4 or Level 5.
Before and after: back-office process automation
Before
- Emails arrive → manual classification by a person
- Data extracted manually into a spreadsheet
- Report assembled manually from multiple sources
- Sent for approval via email chain
- Decision recorded in a different spreadsheet
After
- Emails arrive → AI classifies, routes, and logs automatically
- Data extracted by agent, validated against schema, stored
- Report generated from structured data, no assembly required
- Routed for human review with full audit trail attached
- Decision recorded with traceability to source data
If you have AI pilots and need operational architecture, we should talk.
We work with technical leads, operations managers, and founders who are past the “should we use AI?” question and into the “how do we make it work reliably in production?” problem.
There is no contact form. Reach out directly — it filters for the right conversations.
syntran.xyz