The capabilities
that build and run your AI.
Eight integrated engineering disciplines under one senior team: the capabilities we mobilize to build, ship, and operate your AI. AI is the core; software, security, cloud, and data are the foundation that makes it production-grade, not coordinated across vendors. They power the Build & Run offer.
Overview
SDEN's engineering centers on AI as the core discipline, on a foundation of seven supporting ones: software and mobile development, cybersecurity, cloud management, data engineering and analytics, product and UX design, DevOps and automation, and IoT and embedded systems. Each is led by a senior engineer who owns the discipline end-to-end on every engagement.
We keep AI and the disciplines that ship it inside one small team for a deliberate reason. Most vendors split disciplines across separate companies (an AI shop, a frontend agency, a security consultancy, a cloud reseller) and then ask the client to coordinate the seams. That coordination is where AI projects fail. At SDEN the seams are inside one team, with one architecture, one set of conventions, and one accountable lead, which is also what lets us hand the system over cleanly, so your team can run it without us.
What follows is what we mean, concretely, by each capability: the work we take on, the technical defaults we bring to every project, the deliverables you can expect, and one anti-pattern we will not ship into your codebase.
SDEN audits the AI integrations a business already runs, designs the custom workflows it should run next, and ships them to production with the evaluation harnesses that keep them honest: RAG, agents, classification, generation.
Most founders we meet already run AI: a few tools, a homemade ChatGPT flow, maybe a vendor agent nobody has checked. The real question isn't whether to use it, but which integration is load-bearing, which is leaking trust, and what belongs in-house.
Defaults we ship
- AI integration audit with a remediation backlog scoped into shippable issues
- OpenAI, Anthropic Claude, and open-weight models depending on cost / latency / privacy
- RAG with hybrid retrieval (semantic + lexical) and explicit citation
- Offline eval harness + online A/B before any prompt or model change ships
- PII redaction and prompt-injection guardrails at the boundary
Deliverables
- AI audit report: inventory, risk register (OWASP LLM Top 10 + data exposure), and a ranked remediation backlog
- Use case definition with measurable success criteria
- Evaluation harness committed to your repo with a golden dataset
- Production runtime with latency, cost, and quality dashboards
- Guardrails: input validation, output filtering, refusal handling
What we refuse to ship
We will not ship an AI feature without an evaluation harness. Demos that work in the founders' hands and break in production are how AI projects lose budget.
SDEN designs and ships production web platforms, SaaS applications, and native and cross-platform mobile apps: from a blank page to App Store, Play Store, and live production.
Our largest practice: web platforms, native iOS and Android, cross-platform mobile (Flutter, React Native), and the back-end services behind them.
Defaults we ship
- TypeScript end-to-end (no untyped boundaries between server and client)
- Component-driven UI with a shared design system
- Server-rendered by default; client-rendered only where interactivity demands it
- App Store and Play Store releases automated through CI
Deliverables
- Architecture decision record (ADR) for every non-trivial choice
- End-to-end typed API contract between front-end and back-end
- CI/CD pipeline that builds, tests, and deploys on every commit
- Documentation written for the next engineer, not the project manager
SDEN treats cybersecurity as an engineering discipline applied to every line of code: from threat modeling at the design stage to continuous monitoring once the product is live.
Security shows up three ways. Baked into a build: threat modeling, dependency and secret scanning, branch protection, signed releases. As a stand-alone engagement: audits, pentests scoped to OWASP Top 10 and ASVS, remediation roadmaps, incident response. Or driven by compliance: SOC 2, CCPA/CPRA, PIPEDA, ISO 27001 readiness.
Defaults we ship
- Threat modeling at the design stage, not after launch
- OWASP Top 10 + OWASP ASVS Level 2 as the minimum bar for shipped products
- Dependency scanning (SCA), SAST, and secret scanning enforced in CI
- Audit logs retained for a minimum of 12 months
Deliverables
- Risk register with severity, exploitability, and business impact
- Remediation backlog scoped into shippable issues
- Hardened CI configuration (SCA, SAST, secret scanning) committed to your repo
- Re-test report after fixes land
SDEN builds the data pipelines, warehouses, and analytics layers that turn raw product events into metrics teams can defend in a board meeting.
Data work starts upstream of the warehouse, at the schema. Events are modeled with the same rigor as application data: explicit contracts, versioned schemas, rejected at the door when they don't match.
Defaults we ship
- Schema-on-write with explicit data contracts at ingestion
- dbt as the canonical transform layer; SQL is reviewed like code
- Warehouse choice based on volume, not on the loudest vendor
- Dashboards with documented lineage and freshness SLAs
Deliverables
- Event schema definitions checked into the application repo
- dbt project with documented models and tests
- Analytics dashboards (Metabase, Looker, or your existing BI tool)
- Data quality monitoring with alerts on freshness and row-count anomalies
