Workflow 01
Contract redlining workflows
Pull a draft, flag deviations from your standard positions, and surface the precedent each redline is grounded in — before a senior lawyer opens the file.
AeroLex / AI workflow systems
AI workflow systems for boutique law firms and advisory teams.
AeroLex helps document-heavy teams turn scattered contracts, meeting notes, project updates, and client context into structured workflows people can actually use.
Currently
AI consultant, M37 Ventures
Background
M.S. Bioinformatics
Engagements
Project-based, scoped to one workflow
Built for
Document-heavy professional teams
Inputs · scattered
5 sources
Contracts
— v3.2.docx
MSA — Walden & Pierce
redline?
Email
— THU 14:08
Re: scope question — needs context from Q1 thread
Meeting notes
— 04 / 22
Discovery call — partner only attended last 12 min
Client files
— SHARED
/clients/walden/2024-Q4/...
Project update
— SLACK
"Where did we land on the indemnity carve-out?"
AeroLex layer
workflow.run
Workflow runtime
Active
01Clean/ normalize sources
02Structure/ extract clauses, parties, dates
03Retrieve/ pull prior precedent
04Draft/ proposed redlines & brief
05Review/ partner sign-off
HUMAN-IN-THE-LOOP
SOURCE TRACEABILITY · ON
Outputs · usable
5 artifacts
Redlined contract
2 flags
Indemnification shall apply without limitation subject to §8.4 caps.
— matches Walden & Pierce precedent (2023, §8.4)
Project status
on track
Discovery100%
Drafting62%
Partner review18%
Client brief
ready
Follow-up draft
needs review
"Sharing the redlined MSA with §8.4 reconciled. Open question on the indemnity carve-out — copying Maya."
Action log
3 / 4
Reconcile §8.4NZ
Draft client briefSYS
Send follow-upNZ
Partner sign-offJM
§ 02 — Workflows
Four practical systems AeroLex builds for document-heavy teams.
Workflow 02
Project status dashboards
Stop reconstructing where each engagement stands from scattered Slack threads, email, and meeting notes. One synthesised view per project, updated from real artifacts.
Workflow 03
Client memory systems
A living context layer per client — prior matters, decisions, preferences, contacts — so juniors don't ask partners questions the firm has already answered three times.
Workflow 04
Data cleanup & documentation setup
Most AI workflows fail upstream. Before tooling, the source material gets cleaned, de-duplicated, and documented so the system has something coherent to work from.
§ 03 — Approach
Targeted workflow changes, not broad transformation projects.
Map the bottleneck
Find the specific place senior time is leaking — usually a recurring document, decision, or hand-off.
Clean the source material
Most AI work fails because the inputs are messy. Normalize, de-duplicate, document.
Build one useful workflow
A single workflow with clear operating logic, a real output, and a human review step.
Test it against real work
Run on actual client matters and prior files. Calibrate against partner judgment.
Document the system
Operating notes, source traceability, and a runbook so the workflow doesn't depend on me.
Engagement note
AeroLex works through project-based engagements, usually starting with one concrete workflow rather than a broad transformation project. Most engagements run four to ten weeks and ship a single system the team actually uses on Monday.
§ 04 — About
Bioinformatician turned AI workflow designer.
I came to AI workflow design through biology and bioinformatics, where messy systems, source traceability, and documentation are not optional. That background shapes how I build: targeted workflow changes, clear operating logic, and systems that preserve human judgment.
Before AeroLex I worked on clinical and lab data pipelines — the kind of environment where a wrong answer that looks confident is worse than no answer at all. The same discipline carries over to professional-service teams: traceable sources, human review steps, and small systems that earn trust before they expand.
The goal isn't to automate judgment. It's to put the messy work — the cleaning, the retrieval, the first draft — somewhere it can be inspected, so senior people spend their time on the call that's actually hard.
Education
M.S. Bioinformatics
Currently
AI Consultant, M37 Ventures
Discipline
Workflow automation, documentation systems
Built for
Document-heavy professional teams
§ 05 — Writing
Notes on AI workflows, documentation systems, and building AeroLex.
Source · LinkedIn
Field notes from professional-services workflows.
Short pieces on what's actually working when teams adopt AI inside document-heavy operations — and what falls apart on contact with real client work.
linkedin.com/in/noahzepter
Source · Substack
Longer essays on documentation, judgment, and technical operations.
Slower, longer-form writing on the design of workflow systems, source traceability, and the parts of professional services that AI shouldn't try to replace.
substack.com/@aodhanizm
§ 06 — Contact
Have a workflow that keeps stealing senior time?
Book an intro call or email Noah directly. The first conversation is about identifying the bottleneck, not pitching a prebuilt product.