SYSTEMS ONLINE — PRODUCT ENGINEERING LAB
STM LABS is what happens when three brothers who grew up taking machines apart start putting software together. No account managers, no hand-offs, no telephone game between "strategy" and "engineering." You talk to the people writing the code.
We've shipped realtime platforms, data pipelines, AI-native products and the boring-but-critical infrastructure underneath them. Small team, heavy output. If it compiles, it ships. If it ships, it scales.
Click a lane for scope, stack, and how we actually work. Short on purpose — the long version is a build.
We take products from whiteboard to production: architecture, UI, APIs, auth, data models, and the release path. You talk to the people writing the code — no strategy theater, no hand-off graveyard.
Typical shape: discovery sprint, thin vertical slice, then iterative ship cycles with observability from day one. We optimize for something you can put in front of users, not a slide that says you will.
Start a product build ↗We build AI that ships as infrastructure: retrieval, tool use, agents, eval harnesses, cost controls, and the human-in-the-loop bits demos skip. HANGAR is how we run this for ourselves; we apply the same discipline to yours.
Data side: pipelines, warehouses, feature stores, and the boring quality checks that keep dashboards from lying. Models are only half the system — the other half is everything that feeds and measures them.
Start an AI build ↗Platform work is product work that never makes the homepage. We design cloud layouts, deploy pipelines, environments, secrets, monitoring, and on-call realities so shipping is a habit instead of a hero moment.
If your deploy requires tribal knowledge and a prayer, we fix that. IaC, preview envs, metrics that matter, and alerts that are not noise machines.
Harden the platform ↗Time-boxed builds that answer a real question: does this workflow work, will users care, is the architecture even right? We cut scope hard, ship a clickable surface, and instrument enough to learn something true.
Prototypes here are not throwaway theater when we can avoid it — they are thin paths that can grow into production if the bet pays off. If it dies, it dies cheap and documented.
Book a spike ↗Real products we design, build, and run. Click a deploy for the full write-up — hover for technical specs.
PULSE is our low-latency trade terminal stack — the kind of surface where a few milliseconds are not a flex, they're the product. We built for streams that never politely wait: live quotes, order flow, risk overlays, and operator workflows that have to stay readable when the market is moving faster than the coffee is cooling.
Under the glass: tight event pipelines, careful rendering budgets, and UI that stays honest under pressure. Traders get signal, not theater. We'll keep expanding this write-up with architecture notes, latency targets, and the boring reliability work that keeps the terminal boring in the best way — predictable.
FORGE is where product design meets the machinery that actually ships it. We design applications end-to-end — flows, interfaces, system boundaries — then wire automation so releases, environments, and repetitive ops stop depending on someone remembering a checklist at 11pm.
Think design systems that survive real code, prototypes that don't die in Figma, and automation that removes toil without removing judgment. FORGE is how we turn "we should build an app for that" into something people can click, trust, and iterate on without a ceremony department.
LEDGERKIT is financial automation with calculators that earn their keep — amortization, pricing, projections, reconciliations, and the unglamorous glue between spreadsheets and systems of record. Numbers should be explainable. Rounding should be intentional. "It worked in Excel" is not a deployment strategy.
We build the tools operators actually use: clear inputs, auditable outputs, and automations that move money-adjacent workflows without inventing mystery math. More product detail landing soon; the mandate stays the same — finance software that is precise, boringly reliable, and slightly less soul-crushing than the process it replaces.
SIGNAL9 automates the social stack that otherwise eats a team's week: publishing, scheduling, monitoring, routing, and the dull multi-platform chores that make "just post it" a lie. We treat social operations like a system — queues, policies, retries, approvals — not a pile of browser tabs and vibes.
The goal is leverage without losing the plot: humans set voice and strategy; automation handles the conveyor belt. We'll expand this section with workflow diagrams and integrations as we open more of the surface. Until then: fewer copy-paste marathons, more signal, fewer 9pm "did we post?" panics.
HANGAR is our proprietary AI operating system — the internal layer that turns models, tools, data, and agents into something you can actually run like infrastructure. Not a chat window cosplaying as a company strategy: permissions, orchestration, evals, observability, and the boring controls that keep AI useful when the demo ends.
It's how we ship AI-native products without duct-taping prompts to production and hoping for the best. HANGAR is still evolving (as any real OS should). We'll keep adding architecture, capability maps, and what "production-ready" means when the runtime can write its own homework — with guardrails, logs, and a kill switch.
Same last name on the commits. Three specialties, one standard: it works, it's fast, it ships. Names and headshots landing soon — the roles are already in production.
HEADSHOT TBD
NAME TBD
S draws the system before the sprint does. APIs, cloud boundaries, failure modes, and the quiet decisions that decide whether something scales or just demos well. Placeholder bio — swap this for the real story: what he builds, what he refuses to build, and why the diagrams eventually become uptime.
DISTRIBUTED SYSTEMS / CLOUD / API DESIGN
HEADSHOT TBD
NAME TBD
T is the compile-and-ship reflex. Frontend, backend, AI surfaces, performance work — if it has a runtime, it's fair game. Placeholder bio — fill in languages, favorite problems, and the time something shipped faster than the meeting invite could be accepted.
FULL-STACK / AI-NATIVE / PERFORMANCE
HEADSHOT TBD
NAME TBD
M turns builds into businesses: launch, metrics, positioning, and the questions engineers pretend are someone else's job. Placeholder bio — add the go-to-market scars, the dashboards that matter, and how "shipped" becomes "used."
GO-TO-MARKET / DATA / GROWTHShipped beats perfect. Perfect ships next sprint.
Small team, senior hands, no hand-offs.
If we wouldn't run it in production, you won't either.