AI engineering,
on a foundation that ships.

Software and mobile is the largest discipline at SDEN. The work spans the full surface: web platforms (B2B SaaS, internal tooling, consumer products), native iOS and Android apps, cross-platform mobile (Flutter, React Native), and the back-end services that hold them up. We take projects from a blank page through architecture, prototype, staged release, and post-launch operation — and we also take over codebases that have stalled and need to be rebuilt without losing the business logic already encoded.

Our architecture defaults are deliberately boring. Next.js with TypeScript and React for the web tier; PostgreSQL with Prisma or Drizzle for the data tier; Node.js for the API surface unless a domain (real-time, ML inference, embedded) demands something else. Mobile defaults to Flutter or React Native unless the product needs deep platform integration, in which case we ship native Swift or Kotlin. The shared principle: pick the tool the team can still maintain three years from now, not the framework that trended last quarter.

Security work at SDEN takes three shapes. First, security applied inside a delivery — threat modeling at design, dependency scanning in CI, secret scanning, branch protection, signed releases, secure-by-default architecture. Second, stand-alone engagements: audits, penetration testing scoped to OWASP Top 10 and OWASP ASVS levels, remediation roadmaps, and incident response. Third, compliance work: SOC 2, CCPA/CPRA, and PIPEDA posture, ISO 27001 readiness, SOC 2 readiness, and the documentation buyers ask for before they sign.

An audit from SDEN produces three artifacts you can hand to your board: a risk register ranked by exploitability and business impact, a remediation backlog scoped into shippable tickets, and a hardened CI configuration that prevents the same class of bugs from landing again. Penetration testing is documented with reproducible proofs of concept — never a PDF that vaguely references a finding.

Most CEOs and founders we meet are already running AI — usually three or four tools, often a homemade ChatGPT workflow, sometimes a vendor agent nobody has audited. The question is rarely whether to use AI. It is which of those integrations is load-bearing, which is leaking trust, and what should be built in-house instead. SDEN's AI engagements take three shapes. First, an AI audit: an inventory of every AI integration in the business, what data it touches, where it sits in critical paths, and a ranked remediation backlog with a build-or-buy verdict for each item. Second, custom AI workflows: designed against a measurable outcome, shipped with an evaluation harness, owned by the client. Third, embedded AI engineering, where SDEN sits inside an existing team as the discipline lead until the team can carry the work itself.

The hard part of shipping AI is not picking a model. It is deciding what to measure, building the evaluation harness that measures it, and keeping a live feedback loop running once the product is in production. We start every AI engagement with the question the model is supposed to answer for the user — and refuse to write code until we agree on how we will know whether the answer is good. From there we choose the simplest architecture that meets the bar: a well-prompted hosted model where it works, retrieval-augmented generation (RAG) over your data where the answers depend on private content, and fine-tuning only when prompt engineering and RAG have hit a ceiling.

Production-readiness for AI features at SDEN means a documented latency budget, a per-request cost ceiling, deterministic guardrails on the inputs and outputs (PII redaction, jailbreak detection, refusal taxonomy), and a logged evaluation pipeline that runs against a held-out set every time the prompt or the model changes. Models are commodities; the evaluation discipline is the moat.

Cloud at SDEN is multi-cloud literate by training and region-flexible by default. We deploy on AWS, GCP, and Azure where the workload requires it, and we deploy in your region (US, Canada, or EU) when the threat model and the data-residency requirements make a specific jurisdiction the better call. Either way the infrastructure ships as code (Terraform, Pulumi, or the provider-native IaC), reviewed in the same pull-request flow as application code.

Cost discipline is not optional. Every new feature ships with a published $/month estimate before it deploys, and we run a monthly cost review against the previous month's bill. The most common finding is over-provisioned dev environments — and the second most common is forgotten snapshots. Cost is not a finance concern downstream of engineering; it is an engineering output we sign for.

Data work at SDEN starts upstream of the warehouse — at the schema. We model events with the same care as application data: explicit contracts, versioned schemas, and rejection at the boundary when the data does not match. The pipeline then lands events into a warehouse (PostgreSQL, BigQuery, or Snowflake depending on volume), where dbt becomes the canonical transform layer and the metrics are computed against a documented model, not against ad-hoc SQL pasted into a dashboard.

Analytics deliverables are dashboards that survive the engineer who built them. Each chart has a documented data lineage, a freshness guarantee, and a defined behavior when the upstream data is late or missing. The team can answer 'where does this number come from?' without opening five tools.

Design at SDEN is not a layer painted on top of engineering — it sits inside it. The same engineer who scopes the data model is in the room when the wireframes are reviewed, and the same designer who runs user research watches the engineering team integrate the feedback. The output is a product whose interface and architecture were decided together, not stapled together afterward.

Accessibility is a default, not a feature. We ship to WCAG 2.2 AA on every product unless the client explicitly chooses a lower bar — and we test against assistive technology before we declare a release. Design tokens (color, type scale, spacing, motion) live in a single source of truth, exported into Tailwind for engineering and into Figma libraries for design — so the system stays in sync without manual reconciliation.

DevOps work at SDEN targets a specific outcome: a one-command deploy that the entire team trusts. The path to that outcome is unglamorous — branch protection, mandatory code review, automated tests gating merge, infrastructure as code, deploys triggered from main, observability wired before the feature ships. None of it is interesting in isolation. The compound effect is that releases stop being events and become a default rhythm.

Observability is treated as a feature, not an afterthought. Every service emits structured logs, RED metrics (rate, errors, duration), and traces by default. Dashboards are committed to the repo (Grafana as code) so they survive the engineer who built them, and SLOs are written down so the on-call engineer knows what a real incident looks like versus a noisy alert.

IoT at SDEN spans the full vertical: firmware on the device (C, C++, Rust, embedded Python where appropriate), gateway software at the edge, secure provisioning of new devices into a fleet, and the cloud back-end that ingests telemetry and pushes updates. We work in regulated and unregulated contexts and treat device security as a first-class concern — keys provisioned per device, signed firmware updates, and a documented disclosure path for vulnerabilities found in the field.

Fleet operations is where most IoT projects stall. We design for it from day one: every device is uniquely identified, every firmware image is signed and traceable, every over-the-air update is staged and reversible, and every connectivity outage degrades to a predictable offline behavior — not to a brick.