Data-intensive intelligence layers and ML in production for real operations.
From the data you have to the decisions you ship.
Productized services, by audience and vertical.
- § Hedge funds
Hedge Fund Suite
Alpha + risk products on top of existing fund infrastructure.
- § Private equity
Private Equity Suite
Deal-cycle automation and portfolio intelligence — sourcing to exit.
- § Family offices
Family Office Suite
FO ops modernization — K-1 extraction, multi-entity consolidation, reporting.
- § Venture capital
Venture Capital Suite
Sourcing, evaluation, portfolio pulse, LP communications.
- § Real estate
Real Estate Suite
Underwriting + operations across the asset lifecycle.
- § Operations
Operations Algorithms Suite
Customer intelligence + commercial optimization + OR. Math-anchored.
- § Shopify
Shopify Ops Intelligence Suite
Operations work, not dashboards. Mom-and-pop to multi-store portfolios.
- § iOS
iOS App Development Suite
Full iOS app builds. Native SwiftUI, App Store, monetization, retention.
Production is a property of the system, not a slogan.
These are the things that don't show up on a demo, but show up on day thirty — when the queue backs up, when the migration runs, when the alert fires at 3am.
- 01
Async correctness.
Workflows that move data have to survive partial failures: retries that don't double-process, queues that don't lose messages, jobs that resume from the last known-good state. We design for at-least-once delivery with idempotent handlers — not happy-path code with fingers crossed.
- 02
Data integrity at every boundary.
Schemas validated where data crosses a process line. Constraints enforced where the row lives. Every value that crosses a boundary fails loudly if it's wrong — not silently if it's surprising. Backfills that handle the rare row.
- 03
Capacity under burst.
Queues fill in minutes when something goes wrong upstream. We design with explicit capacity, dead-letter routing, circuit breakers, and back-pressure. The mean is a comfortable lie; what matters is the tails — p99 and p99.9.
- 04
Schema evolution.
Adding a column shouldn't break a deploy; renaming one shouldn't lose data. Migrations are forward-compatible, rolled out in stages (writers before readers), with both code paths kept alive long enough to roll back.
- 05
Observability.
Every meaningful state transition is logged with structure. Every metric has an alert at a threshold someone has thought about. Distributed traces follow a request through the stack. After an incident, the first question is — what did we see? And the answer is — all of it.
- 06
Deploys without dropping users.
Zero-downtime is a property of the system, not a slogan. Connections drained. Migrations idempotent. Feature flags decoupled from deploys. Rollbacks that finish in seconds.
None of this is the work itself. It's the bar to ship the work at all.
If this fits, the next step is one call.
The work is connect → clean → model → deploy → monitor → automate. The bar is production. The fit is operations that already exist.
We'll talk scope and fit. If we're not the right fit, you'll know fast.
Bogdan and team · async-first · OP—2026