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Business analysis, systems, AI automation

I build intelligent systems that run businesses on autopilot.

Business analyst and project manager. I build AI automation, data pipelines, and system architecture that run operations without you.

Book an auditSee the work
Available for new engagements/2026/
(01)

Systems in production, not slideware.

01 / 03·AI · predictive analytics

Enterprise AI data agent and predictive analytics dashboard

−38%

customer churn within two quarters

The challenge
Churn appeared in the numbers sixty days after it started. Every deep-dive question meant an analyst, a SQL export, and a three-day wait.
The system
A conversational AI agent over a governed SQL warehouse answers executive questions with verified, read-only queries. A churn model rescoring nightly in Python feeds live dashboards: every at-risk account surfaces with its statistical drivers and a recommended play.
0.91
model AUC in production
3d → 45s
question to answer

Stack: SQL / Python / scikit-learn / Claude API / PostgreSQL

02 / 03·CRM · automation architecture

End-to-end CRM operations architect

40+

hours of manual work eliminated every week

The challenge
A 60-agent call center re-keyed every interaction across three systems, routed callbacks by gut feeling, and burned shifts on reports nobody trusted.
The system
An event-driven layer around the CRM: webhooks sync every disposition in real time, bots run wrap-up and data hygiene autonomously, and a predictive router sends each callback to the agent most likely to close it first time.
+24%
first-call resolution
0
records re-keyed by hand

Stack: Webhooks / REST APIs / n8n / Predictive routing / TypeScript

03 / 03·Finance · executive BI

Advanced financial analytics and executive BI pipeline

12 → 1

days to close monthly reporting

The challenge
Month-end close meant five analysts reconciling ERP, bank, and billing exports by hand. The board pack arrived late, and no two versions of revenue ever matched.
The system
A statistical cleanup pipeline feeds one governed metrics layer. Executives get a board-grade BI surface where every metric traces back to validated transactions, built to be read in the room, not deciphered after it.
99.8%
reconciliation accuracy
1
version of the truth

Stack: Python / pandas / dbt / SQL / Power BI

Live sandbox. Run the nightly pipeline, or type to build it by hand.

dani@prod: ~/pipelines

$ ▍Press run to execute the nightly churn pipeline, or click in and type on any keys to build it by hand.

(02)

Every system ships through the same four gates.

No improvisation and no black boxes. Each phase closes with artifacts you keep, whether or not we continue.

  1. 01

    Discovery and data auditing

    Map where the operation loses time and money, extract the existing data, and put a number on the target ROI before any code is justified.
  2. 02

    Architecture and schema design

    PostgreSQL and Supabase modeling, end-to-end flowcharts, and the prompt-routing topology that decides which model handles what, and what never touches a model.
  3. 03

    Core engineering and automation

    Python pipelines, secure API and webhook integrations, automation bots, and LLM orchestration with validation gates. Version-controlled, reviewable, never held hostage.
  4. 04

    Deployment and analytics guardrails

    Production release with instrumentation from day one: live BI dashboards and KPI guardrails that alert when reality drifts. The system proves its ROI instead of asserting it.
(03)

I translate operational chaos into systems that measure, automate, and improve themselves.

I work at the intersection of business and engineering. As a business analyst and technology project manager, I have led digital transformation end to end: stakeholder discovery, process mapping, architecture, vendor selection, delivery, adoption, and measurable ROI.

My rule for every engagement is simple. If a process runs on someone's memory, a spreadsheet, and hope, it can be turned into a system. And once it is a system, it can be measured, automated, and improved.

How I work

  1. 01End-to-end ownership, from discovery through adoption
  2. 02Fluent in boardroom language and technical specification alike
  3. 03Data-first decisions: every recommendation backed by numbers
  4. 04AI pragmatist: automation where it pays, humans where it matters

Tools

  • SQL
  • Python
  • PostgreSQL
  • Supabase
  • Claude API
  • n8n
  • dbt
  • Power BI
  • Next.js
  • TypeScript
  • Webhooks
  • CI/CD
0+
Years in tech, data and operations
0+
Systems and workflows automated
0K+
Manual hours eliminated yearly
0+
Cross-functional projects delivered
(04)

Fifteen minutes. One process. A concrete first move.

Not a discovery call, and nobody will try to sell you anything. Bring your messiest process; we map it live, locate the automation seams, and you leave knowing what to do next.

What manual work is costing you.

Rough numbers before we even talk. Move the sliders to match your operation.

12 hrs
$45
70 %
Live projection12-month horizon

Cumulative recovery, adoption-ramped

Hours reclaimed per month

36 hrs

Recovered per year

$19,656

≈ 11 full work-weeks per year, back in the business.

Directional estimate based on your inputs. The audit replaces it with real numbers.

15-minute system architecture audit

A direct working session with me. One process, mapped and diagnosed live.

  1. 01A systems map of your highest-friction workflow
  2. 02The two or three automation seams with fastest payback
  3. 03A plain verdict: automate, redesign, or leave alone

3 audit slots per week

Send an inquiry

Describe your challenge. I reply within one business day.

Prefer email? [email protected]