The Lab

Experiments before they become products.

Curiosity in public — hypotheses, thin prototypes, and what each attempt taught. Not a roadmap of invented startups.

Currently exploring

Where attention is going.

  • AI Agents
  • Document Intelligence
  • Search Systems
  • Automation
  • Knowledge Graphs
  • Human + AI Workflows

Experiments

Hypothesis → prototype → learning.

00

Shipping · private tool

Portfolio CMS & knowledge publishing

Hypothesis
A private admin on the same Worker can keep a knowledge library honest and current without turning the site into a generic CMS product.
Prototype
GitHub-allowlisted /admin, D1 documents, R2 media/overlays, first-party analytics overview, publish rebuilds for search artifacts.
Technology
Workers · D1 · R2 · KV sessions · Analytics Engine + D1 event mirror
Learning
Scope stays “this site only.” Multi-tenant CMS fantasies can wait; publishing cadence cannot.

Read the public note →

0A

Planning · private workspaces

Product Incubator

Hypothesis
Decade-scale products need full planning (users, architecture, risks, decisions) before production code — and that planning must stay private until Prototype.
Prototype
CMS Incubator module with 20-section workspaces; seeded eClearBag, ScholarQuest next chapter, UKSathi, NepalIPMS Public Demo.
Technology
D1 planning tables · admin UI · public index gated to Prototype+
Learning
Public visitors should never see unfinished internal research. Plan like a company; ship when ready.

Public incubator index →

01

AI Agents

Matter-aware legal assistant

Hypothesis
An agent that retrieves from a firm’s own matters and register can draft useful next steps — if every answer cites a source and never auto-files.
Prototype
Retrieval over matter notes + trademark FTS, draft outline with citations, forced human confirm before anything touches a client file.
Technology
Workers · embeddings API · D1 FTS · review queue patterns from NepalIPMS
Learning
Agents fail when scope is open-ended. Narrow tools (“summarize this matter’s deadlines”) outperform chat that pretends to practice law.
02

Document Intelligence

Layout-tolerant bulletin parsers

Hypothesis
Separating OCR text from field intelligence lets parsers survive DoI layout changes without retraining a whole model.
Prototype
Stage pipeline already in production intake — experimenting with per-issue layout fingerprints and confidence thresholds.
Technology
R2 · layout OCR · Workers field mappers · human review
Learning
OCR alone is never enough. Classification and review gates are the product; extraction is infrastructure.
03

Search Systems

Exact-first register lookup

Hypothesis
For 130K+ trademarks, prioritizing normalized exact and FTS tokens beats fuzzy “smart” matching when desks act on the result.
Prototype
Production search paths in NepalIPMS; lab work on controlled variant expansion after exact miss.
Technology
D1 FTS5 · KV hot cache · query planners
Learning
False conflicts destroy trust faster than a missed suggestion. Accuracy first; flexibility second.
04

Automation

Deadline engines as workflow, not alerts

Hypothesis
Renewal reminders only work when they attach to a verified matter record and a named owner — not a generic notification feed.
Prototype
Cron-triggered deadline checks tied to matter ownership; exploring smarter escalation when no one acknowledges.
Technology
Workers cron · D1 · notification hooks
Learning
Automation without ownership is noise. The hard part is the social graph of who must act.
05

Knowledge Graphs

Company → matter → asset graph

Hypothesis
Explicit graph edges between companies, matters, and assets make portfolio questions (“everything for this client”) faster than nested joins alone.
Prototype
Domain model already encodes the hierarchy; exploring lightweight edge tables for related-party and opposition links.
Technology
D1 relational model · optional edge index · Workers API
Learning
Start from desk nouns. A graph that doesn’t match how lawyers talk about files becomes another abstraction to fight.
06

Human + AI Workflows

Confidence as a first-class UI

Hypothesis
Showing field-level confidence next to source pages makes practitioners faster at correcting AI than hiding uncertainty.
Prototype
Review queue patterns from OCR intake; testing denser “source + guess + confirm” layouts.
Technology
Portal UI · confidence scores · provenance IDs
Learning
Human+AI is a product surface, not a model choice. The review screen is where trust is won or lost.

What I’m building next → · ← Portfolio