LAT·51.227N / LON·6.773E LIVE — AGENT NETWORK--:--:--

Applied AI
that ships.

I'm Muhammad Bilal — an applied AI consultant based in Germany. I build agentic systems, analytics pipelines, and AI-driven operations tools for teams tired of demos that never reach prod.

DELIVERY€50K/yr saved
EFFICIENCY49 → 65%
RESEARCHSTOXX 600, cited
STACKNode · Python · Claude
§ SELECTED_WORK

Three engagements, one rule:
ship what matters.

case_01LAMECHKY GMBH
€50K
REPLACED, PER YEAR
POWER BINODE.JSEXPRESSRENDERABSENCE.IOTOGGL
[ 01 ]2026

BI stack that replaced a €50K/yr controlling role.

A German marketing agency with no end-to-end view of billable efficiency. Over ten weeks I built 8 Power BI dashboards, 23 KPIs, and a Node middleware bridging Absence.io and Toggl. First month of deployment moved team efficiency from 49% to 65%.

  • +8 Power BI dashboards, 23 KPIs — project + service teams
  • +Node/Express middleware for HAWK + API-key auth bridging
  • +49% → 65% project-team efficiency (Feb → Mar '26)
  • +186-hr PoC converted to paid contract, now in phase 2
"It took me three years and it took you three weeks."
COO, LAMECHKY GMBH
READ_ENGAGEMENT
case_02OPEN SOURCE
5min
NOTES → JIRA, PER SPRINT
PYTHONCLAUDE APIATLASSIAN RESTPYDANTIC
[ 02 ]2026

Meeting notes to sprint backlog in five minutes.

Planning meetings generate messy transcripts. Jira expects clean epics, stories, acceptance criteria, and estimates. This tool bridges the two — parses raw notes, drafts the ticket tree, files it against the Atlassian API. Built for teams tired of the Monday-morning ticketing tax.

  • +Epic + story decomposition from meeting notes
  • +Acceptance criteria drafting
  • +Atlassian REST integration
  • +Claude-backed extraction pipeline
SEE_IN_ACTION
case_03TUM × NUST
600
EUROPEAN FIRMS, 6 YEARS
PYTHONCLAUDE APIPDF PARSINGPANDAS
[ 03 ]2024

The pipeline behind STOXX 600 climate-disclosure research.

A TRR 266 working paper needed financial-statement paragraphs from six years of STOXX Europe 600 annual reports, machine-readable. I built the scraper: PDF ingestion, LLM-backed section extraction, structured output. The paper cites the work in its acknowledgements.

  • +STOXX Europe 600 annual + audit report coverage
  • +PDF ingestion and section extraction
  • +LLM-backed structured output
  • +Cited: SSRN 4763140 (Müller, Ormazabal, Sellhorn, Wagner)
"We acknowledge excellent research assistance by Muhammad Bilal…"
SSRN 4763140
READ_PAPER
§ CAPABILITIES

Four practices, one outcome: AI that reaches production.

/01

Agentic Systems

Multi-step LLM pipelines that own real work — parsing, extracting, routing, acting. Built for production, not for demos.

ClaudeOpenAIPydanticDSPy
/02

BI & Analytics

KPI design, Power BI dashboards, Node middleware to bridge systems that don't speak to each other.

Power BINode.jsDAXRender
/03

Research Engineering

Data infrastructure for academic and regulatory research — PDF ingestion, LLM extraction, reproducible pipelines.

PythonPandasDuckDBarXiv
/04

Ops Automation

Internal tools that collapse hours of operations work: note-to-ticket, CV-tailoring, report generation, more.

Claude APINotion APIAtlassianScriptable
§ CONTACTREPLY WITHIN 48 HOURS

Got a problem worth solving?

I take on a small number of applied AI consulting engagements per quarter. Send a short note with the problem and constraints — I'll tell you honestly whether I can help.

mbilal.workmail@gmail.comLINKEDIN ↗GITHUB ↗