Software developer
who measures
everything.

problemLawyers were waiting minutes per query across a growing corpus of case files, long enough that many kept side spreadsheets instead of trusting the platform.

builtA dedicated Elasticsearch service with custom analyzers, filtered queries, and relevance tuning for legal citations and case metadata.

impactSub-second results across 500K+ documents for 15K+ daily users. Search went from the platform's biggest complaint to its selling point.

problemEvery new enterprise client meant 2 weeks of an ops team manually matching records against a 3Cr+ (30M+) case dataset. Onboarding was the bottleneck on revenue.

builtA hybrid pipeline: Elasticsearch does fast candidate matching at scale, then an LLM resolves the ambiguous cases a human used to eyeball.

impactOnboarding dropped to ~6 hours, a ~95% reduction. Go-live in a day instead of a month.

problemEvery client formats legal documents differently. Supporting a new client's templates took an engineer a week of hand-written parsing rules.

builtAn LLM powered template engine that reads a client's documents and derives the structure itself, no per-client parsing code.

impact40+ client formats supported; new client setup collapsed from a week to 1 day.

problemSenior engineers were losing hours to routine PR review and a steady drip of small debug tickets.

builtA Claude based agentic workflow that reviews every PR automatically and picks up small, well-scoped tickets end to end: reproduce, fix, open a PR.

impactRoutine review happens on every PR without waiting for a human. Senior time goes to the hard problems.

problemClient-facing teams compiled usage and case reports by hand, every reporting cycle, for thousands of users.

builtA React.js real time analytics dashboard fed by live platform data.

impact7K+ users self-serve their numbers; manual reporting overhead down ~90%.

problemWhen production breaks on a platform lawyers rely on for court deadlines, minutes matter and someone has to own it end to end.

builtA first responder habit: triage, communicate, fix, write the postmortem. Plus onboarding docs and mentoring so the next person is faster.

impact20+ incidents resolved, and 2 interns growing into engineers who can take the pager.

problemCourt hearing alerts cannot be lost. A missed notification can mean a missed hearing, and the old flow had no retry story and no failure visibility.

builtA RabbitMQ backed microservice with dead letter queues, exponential retries, and idempotency keys so retried sends never duplicate.

impact100K+ (1L+) messages a month with full failure visibility, owned end to end as an intern.

problemSome clients run on-prem (MinIO), some in cloud (S3). Two storage code paths meant inconsistent behavior and ~1% upload errors on legal documents.

builtOne storage abstraction with a single API and uniform retry and validation logic, backend chosen per deployment.

impactUpload error rate cut ~100×, to under 0.01%, on documents that legally cannot go missing.

problemBanks and pharma clients will not let legal data leave their own infrastructure, so cloud-only deployment was a dealbreaker.

builtPer-client isolated Docker deployments, configured and debugged remotely and on site.

impactEnterprise deals stayed on the table that cloud-only competitors could not serve.

problemFive interconnected modules were each rebuilding the same UI state logic in slightly different ways.

builtReusable React components with shared Context API and useReducer patterns.

impactAdopted across 5+ modules. My first taste of writing code other teams depend on.