We Fix Your Processes. Then Make Them Autonomous.

We don't automate broken processes. We audit your workflows, fix what's broken, then build agentic systems that handle the work autonomously.

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The Problem

Sound Familiar?

95% of enterprise AI projects fail. Not because the technology doesn't work, but because nobody fixed the process first.

01

You Automated a Broken Process

Your workflow has tribal knowledge, inconsistent data, and manual workarounds. Layering automation on top just makes the dysfunction faster.

02

The Demo Worked. Production Didn't.

Your vendor showed a perfect demo with clean data. Real data, real volume, real users broke it within the first week. Now the project is shelved.

03

Your Vendor Built It and Disappeared

No training, no documentation, no one to call when the system needs updating. You're paying your team to babysit a system you don't understand.

Get a Free Process AuditWe'll tell you honestly if automation is the right move.

The DOE Framework

Reserve LLMs for Judgment. Let Code Handle the Rest.

This architecture makes workflows debuggable, auditable, and self-annealing.

Layer 1

Directive

Markdown-based SOPs that define the task. What to do.

Layer 2

Orchestration

LLM routing that interprets directives. How to coordinate.

Layer 3

Execution

Deterministic Python scripts. Actually doing it.

directive-layer.md
markdown
01# INVOICE PROCESSING SOP
02
031. Extract vendor name, date, and amount.
042. If vendor exists in DB, map to ID.
053. If amount > $10,000, flag for human review.
064. Route anomalies to exceptions queue.
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orchestration-layer.ts
typescript
01// LLM only used for judgment
02const decision = await llm.evaluate({
03 prompt: "Does this invoice match the expected vendor format?",
04 context: currentInvoice,
05 directives: sop.rules,
06});
07
08if (decision.isAnomaly) {
09 await queue.push("exceptions", currentInvoice);
10}
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execution-layer.py
python
01import modal
02
03@app.function()
04def process_approved_invoice(invoice_data):
05 # Deterministic execution. No LLM calls here.
06 erp_client = ERPClient(api_key=secrets["ERP_KEY"])
07
08 erp_client.create_record({
09 "vendor_id": invoice_data["vendor_id"],
10 "amount": invoice_data["amount"],
11 "status": "APPROVED",
12 })
13
14 return {"status": "success", "record_id": invoice_data["id"]}
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Proof, Not Promises

Real Systems in Production

Document Intelligence

Academic Research Pipeline

Fully autonomous document processing pipeline used by researchers. 1,300+ academic papers analyzed and enriched.

Visit the website
metrics.log
PRIMARY METRIC
8,000+
written narratives processed
SECONDARY METRIC
65,000+
academic papers triaged
System running in production

Is This For You?

We Work With Leaders Who Are Done Experimenting

Your Situation

Your team spends 60% of their time on tasks that should be automated. Manual data entry, document processing, report generation. They're burning out on work that doesn't require human judgment.

What We Do About It

We audit your workflows, find the 20% of processes causing 80% of the pain, and build systems that handle them autonomously. Your team focuses on strategy, not spreadsheets.

Talk to a FounderNot a sales rep. An actual founder who builds these systems.

FAQ

Questions You Should Be Asking

Start Paying for Results.

30 minutes with a founder. No sales pitch.