AI engineering,
on a foundation that ships.

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 we 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.

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. 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.

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.