FAQ
Ask us anything.
Ask about the systems we build and how we build them. Matches surface a short overview, the related questions, and a direct line to us.
Both. We diagnose and design the architecture, then implement the critical paths ourselves in clean TypeScript with strong boundaries — and hand the system back understandable and maintainable.
It usually starts with a focused architecture call and a short diagnostic. From there we scope the work — a review, a rescue, a build, or a security pass — with clear milestones and ownership.
For a short architecture review or a critical unblock we can usually start within days. Larger builds are scoped after the diagnostic so timelines are realistic.
Yes. Most of our work is alongside an existing team — we strengthen the architecture, unblock the hard parts, and mentor as we go rather than replacing what already works.
Security is part of the architecture, not an afterthought. We are comfortable with NDAs, least-privilege access, and treating threat modeling as a first-class part of the work.
We integrate AI where it creates real leverage and keep production systems deterministic, observable, and safe — with controlled tool access, clear data boundaries, and human-in-the-loop where it matters.
The hard parts: complex state, hydration and SSR correctness, browser runtime behavior, performance and rendering, design systems, SDKs and embedded widgets, and multi-surface (web + mobile) product experiences — not just building screens.
Yes — APIs and contracts (OpenAPI/GraphQL), data models, auth and permissions, events and background jobs, caching, and observability, designed to scale and stay maintainable.
Yes. Docker, Kubernetes, and CI/CD pipelines with automated, observable deployments — build, test, and ship workflows with blue-green and canary rollouts you can trust.
Unit, integration, and end-to-end tests, plus white-box flow and stress/load testing — including profiling for memory leaks and event-loop lag across long sessions.
Scoped, auditable tool access; clear data and API boundaries with least-privilege; guardrails and human approval paths; and defenses for prompt injection, tool abuse, and data exfiltration.
Yes — penetration testing, threat modeling, and red-team thinking across auth, permissions, API surface, secrets, supply chain, SSRF/injection, and AI-agent attack surface. We prove the exploit, then harden it.
That’s a common engagement. We diagnose the failure modes, stabilize the critical paths, and put the architecture, tests, and observability in place so it stops breaking.
Working, production-grade code on the critical paths, documented decisions, tests and observability, and a team that understands and can maintain the system. No black boxes.
We stay stack-pragmatic and choose per problem, but our default toolkit spans the whole system. Frontend: React and TypeScript with MUI or Tailwind, built on Vite/Nx. Backend: Node and NestJS with typed, contract-first APIs (OpenAPI/REST and GraphQL). Databases: PostgreSQL and MongoDB. Caching & key-value: Redis and Valkey. Queues & event-driven: BullMQ, NATS, and Kafka. Infrastructure: Cloudflare (Workers/Pages), Docker, Kubernetes, and Terraform. Observability: OpenTelemetry traces, metrics, and structured logs with Grafana and Sentry. AI: MCP, FrontMCP, and guardrailed agents with scoped tool access. Delivery: Vitest, Playwright, and GitHub Actions CI/CD. The point is fit, not fashion — we pick the right tool for each layer.
Yes — from a light retainer for architecture reviews and unblocks to deeper ongoing partnership, depending on what your team needs.
An embeddable, multi-tenant component SDK — drop-in auth, account, and form UIs that B2B products embed into their own sites and theme per tenant. We build it on framework-agnostic custom elements with style isolation (Shadow DOM), so the same component runs in vanilla HTML, React, Vue, or Angular without clashing with the host page’s CSS. Theming is token-driven and deep-merged per tenant (colors, fonts, radius, spacing, copy); fields are configurable with their own validation (Zod or JSON Schema) and bound to your backend data keys; and everything is localized, including RTL. It’s tree-shakable behind a stable compound-component API, and configured inline or from a remote URL — served from a CDN or vendored into source — so tenants change themes and fields at runtime with no rebuild.
Yes. The component SDK is framework-agnostic — built on custom elements so customers embed it in vanilla HTML, React, Vue, Angular, or anything else, with thin framework wrappers where they help.
Fully. Per-tenant theming covers colors, fonts, radius, field sets and ordering, button styles, and copy — all driven by design tokens deep-merged per tenant and compound components so the UI matches each host’s design language.
Yes — they connect to localization out of the box, including right-to-left (RTL) languages, so the same component speaks each tenant’s language and locale.
It’s versioned, tree-shakable, and exposes a stable compound-component API, so your developers can adjust and upgrade centrally without breaking host integrations — one SDK, many tenants.
Compound components expose composable parts for flexible layouts; custom elements make the SDK framework-agnostic and style-isolated, so it embeds cleanly and behaves as first-class, controlled UI inside any host site.
Yes — it’s a drop-in embed with a small snippet, so non-experts can integrate it, while theming and upgrades stay managed centrally.
Style isolation. Each component renders inside its own Shadow DOM boundary, so the host page’s styles can’t leak in and the component’s styles can’t leak out. The host only influences appearance through the theme tokens you expose — never by accident.
Yes. Every field carries its own validation — Zod or JSON Schema — and binds to a specific backend data key, so the embedded form validates on the client and maps cleanly onto your API contract. Add custom fields, reorder them, or change rules per tenant without forking the SDK.
No. Configuration is inline on the page or fetched from a remote URL, and the bundle is served from our CDN or vendored into your source. Change a theme, fields, copy, or locale and it updates at runtime — nothing to recompile or redeploy.
Yes — packages are side-effect-free ESM so bundlers drop everything a tenant doesn’t use, and the embed can be lazy-loaded from a CDN separately from the host’s JavaScript. Type definitions ship with no runtime cost.
A very large NestJS Nx monorepo — 29 service apps and 60+ shared libs — where a one-line change took 25–35 seconds to rebuild and restart the server. We built a custom Nx executor for true NestJS hot module reload that swaps only the changed module graph in place while the process stays alive, cutting the edit-to-running loop to 20–100ms — faster than a typical API request. In the same project we built a dev gateway server — an enhanced dev server that spins up multiple nested instances of each app over unix sockets and routes to them by URL prefix — exposing both an external port for clients and an internal port for service-to-service calls, so an app can call the gateway or another server from inside the dev environment — so local development runs the same multi-node, queue, and event-driven topology as production instead of a single process.
Yes — this is exactly the kind of work we do. On a monorepo of 29 NestJS service apps and 60+ shared libs, a one-line change took 25–35 seconds to rebuild and restart. We built a custom Nx executor for true hot module reload that brought the edit-to-running loop down to 20–100ms — faster than a typical API request.
Instead of recompiling the dependency graph and booting a new process, the executor swaps only the changed module graph in place. The process keeps running, in-memory state and open connections are preserved, and the DI container re-wires just the affected providers — so you see the change in milliseconds, not seconds.
A cold loop pays for re-type-checking/transpiling the whole affected graph plus a full Node + Nest bootstrap (config, DB pools, queues, gateways). HMR skips all of that: it patches the single module that changed and re-binds it live, so the cost is the size of your edit, not the size of the app.
Yes. We built a dev gateway server — an enhanced dev server that forks multiple nested instances of each app, each bound to a unix socket, and routes to them by URL prefix (/orders/*, /billing/*, …) behind a single port. Locally you get the same multi-node concurrency, queue, and event-driven fan-out as production — so behaviour you only used to see in prod (sharding, competing consumers, event ordering) shows up on your laptop.
It was built for scale: 29 service apps and 60+ libraries shared across servers in one Nx workspace. Because HMR patches only the changed module graph and the gateway composes apps over sockets, the developer loop stays near-instant regardless of how big the workspace grows.
A real-time intent-detection engine that turns raw browser behavior into a live read of what a visitor actually wants — with no tags or markup on the host page. A lightweight in-page sensor captures ordinary interactions (pointer movement, hovers, scroll, element exposure, clicks, and form focus) and resolves each one to its meaning: what the element is (role), where it sits on the page (zone), and what it is about (topic). Those events stack and aggregate, then a per-page codec encodes them into compact, opaque coded events — so the taxonomy and scoring never travel on the wire, only structure and timing do. A continuous funnel streams the coded events, in order, into a realtime AI agent that infers intent from the behavior — evaluating pricing, weighing the enterprise tier, ready to convert, or confused and stuck — and chooses an action: offer help, open a conversation, assist a form, or hold and do nothing. The hot path completes in well under 250ms, with a safe local fallback when the backend is slow. It is privacy-aware by construction: it reads structure, timing, and intent, never input values or PII, and enforces cooldowns so visitors are never spammed.
It is reading what a visitor wants from how they behave — live, on the page they are on. Instead of waiting for a form fill or a conversion, a lightweight in-page sensor watches ordinary interactions (pointer movement, hovers, scroll, element exposure, clicks, form focus) and turns them into a continuously updated read of intent: evaluating pricing, weighing the enterprise tier, ready to convert, or confused and stuck — with a confidence score attached.
Each captured event is resolved to its meaning automatically — what the element is (its role), where it sits on the page (its zone), and what it is about (its topic) — so the host site needs no manual markup or data attributes. Those semantic signals roll into short, confidence-weighted time windows, and a scorer reads the windows to infer intent. The contract is the meaning of the behavior, not a hand-placed tag on every button.
Analytics and replay tell you what happened after the fact. This is a realtime decision system: it resolves behavior into meaning, scores it, and acts within the same visit — in well under 250ms — so the product can respond while the visitor is still there. It is built to decide, not just to record.
Yes — that distinction is the point. Commercial intent (pricing dwell, plan comparison, CTA hesitation, enterprise interest) and friction (dead clicks, rage clicks, scroll reversals, abandoned fields) are scored apart, so a high-intent buyer gets a sales nudge while a stuck visitor gets help instead of a pitch. Reading the two together is what makes the response feel intelligent rather than annoying.
The hot path is built to decide in well under 250ms. If the backend is slow or unreachable, a safe local fallback policy still produces a decision on the client, so the experience never stalls waiting on the network — it just leans to the conservative, low-risk action.
It is privacy-aware by construction. The sensor reads structure, timing, and the meaning of interactions — never input values or PII. Field contents, what is typed, and sensitive inputs are never captured. Decisions run on anonymous behavioral signals, and cooldowns prevent the same visitor from being interrupted repeatedly.
The output is a decision, not just a label: surface a contextual help prompt, open a conversation with sales, assist or prefill a form, adapt a CTA — or deliberately do nothing when the signal is weak. A policy maps each intent to an action behind guardrails (confidence thresholds, consent, and cooldowns) so interventions stay relevant and rare.
Aggregated events are encoded by a per-page codec into compact, opaque coded events — small integer arrays keyed by a page-scoped dictionary. That keeps payloads tiny and, just as importantly, keeps the taxonomy and scoring off the wire: what travels is structure and timing, not labels a competitor or a script could read. The meaning of each code lives on the backend, never in the page.
The coded events are streamed, in order, through a continuous funnel into a realtime agent that infers intent from the behavior rather than from a single click — reading the shape of the whole session as it unfolds. It produces both a labeled intent with a confidence score and the action to take, on a hot path under 250ms. If it is ever slow or unreachable, a conservative local policy still answers, so the experience never stalls.
A platform where autonomous AI agents work together as governed, traceable flows — with a human approving the moments that matter. An agent can be triggered by an event, by another agent finishing, or by an approve/deny decision; each agent connects to multiple resources (databases, APIs, documents, tools) and receives instructions injected per working entity, so one agent definition behaves correctly across thousands of cases. Flows compose agents into steps that run in parallel where they are independent and depend on prior steps where order matters, with input/output verification on every boundary — a bad output stops the flow instead of propagating. Human-in-the-loop approval gates pause a live run until someone approves or denies, and the decision itself triggers what happens next. Agents iterate in loops until their output passes, within hard caps. Where a model is not needed, steps are deterministic data transformations or custom code, with caching, so flows stay fast and cheap. Every run is traceable end-to-end: each trigger, agent execution, approval, retry, and iteration is a span you can inspect and replay.
Yes — when the autonomy is governed. Agents run inside flows with verified inputs and outputs on every boundary, hard caps on iterations, and human approval gates at the moments that deserve judgment. The agents do the work; the flow decides what is allowed to happen next. Autonomy without governance is a demo — this is built for production.
Three things: an event (something happened in your system), another agent (one finishing triggers the next), or a human decision (an approve or deny resumes or redirects the flow). That trigger model is what lets flows chain agents together, fan out, and pause for review without any glue code in between.
A flow can declare an approval gate at any step. When a run reaches it, the flow pauses — state preserved — and a human sees exactly what the agents produced and approves or denies. The decision itself is a trigger: approve resumes the dependent steps, deny stops the run or routes it to a correction path. Critical actions never execute on model output alone.
Each agent declares the resources it needs — databases, APIs, document stores, tools — and connects to several at once, with scoped, least-privilege access per resource. The flow knows exactly which agent touched which resource in which run, so access is auditable, not ambient.
Instruction injection. An agent has base instructions, and the platform injects context from the working entity — the specific case, customer, or record the run is about — at execution time. One definition, correct behavior across thousands of entities, instead of a forked prompt per case.
Yes. A flow is a dependency graph, not a script: steps that are independent run in parallel, and steps that need earlier results declare what they depend on and wait for exactly that. You get the speed of fan-out and the correctness of explicit ordering — including gates that wait on several parallel branches at once.
Every step boundary is verified: inputs are checked against a schema before an agent runs, and outputs are checked before they propagate to the next step. A failed check stops the flow, retries, or sends the agent into another iteration of its loop until the output passes — within hard caps. Bad output is contained at the boundary where it happened.
No — and they shouldn’t. Data transformations and well-understood logic run as deterministic steps or custom code, with caching, so the flow only spends model time where judgment is actually needed. The result is faster, cheaper, and more predictable than putting a model in every hop.
Every run is traceable end-to-end: each trigger, agent execution, resource call, approval, retry, and iteration is a span in one trace. You can inspect what an agent saw, what it produced, what was approved, and why a branch took the path it did — and replay the run to reproduce it.
Yes — agents and flows loop. An agent can iterate on its own output until it passes verification or a quality threshold, and a flow can cycle work between agents (produce → review → revise). Every loop has explicit exit criteria and a hard iteration cap, so refinement converges instead of running away.