AI agents that do the work.
Engineered for production.
SDEN is your agentic engineering partner. We build AI agents (software that plans, uses tools, and completes multi-step work) and the systems that keep them reliable: evals, guardrails, monitoring. Then we hand it all to your team. No black box, no lock-in.
or explore the offers →The capabilities behind your agents.
One engineering standard.
An agent is only as reliable as the engineering underneath it. Four of the eight domains we put behind every agent, owned end to end.
How we work.
One method, every engagement: we scope where agents are worth it, prototype them on real data, build and harden them, then run and orchestrate them with you. Four deliberate phases, each ending in production agents your team owns and runs without us.
Scoping & architecture
What this phase produces
- Written problem statement with measurable success and eval criteria
- Architecture diagram + decision log (ADRs), including the build-vs-buy and 'AI vs not' call
- Risk register ranked by exploitability and business impact, with EU AI Act classification where it applies
- Data-readiness read for any AI use case (sources, quality, access, retention)
- Go / no-go recommendation, with the scope we would commit to
Design & prototyping
What this phase produces
- Interactive prototype of the highest-risk flows, running on real data
- Model and architecture decision (model choice, RAG / fine-tune / agent) with written rationale (ADRs)
- An eval harness: a graded test set and the metrics production will measure on every change
- Cost and latency budget per AI path, named up front
- Design system (tokens, components, accessibility baseline)
Development & hardening
What this phase produces
- Production-grade application in your repositories, deployed to staging
- Test and eval suites covering success paths, error paths, edge cases, and AI quality
- Guardrails and cost ceilings wired in (input/output checks, spend limits)
- Security review against the OWASP Top 10, the OWASP LLM Top 10, and the relevant ASVS level
- Load and chaos test results against the documented traffic shapes
Delivery & support
What this phase produces
- Staged production release with feature-flag rollout
- Operational runbook for every routine production task
- Monitoring of SLOs and AI behavior: quality, drift, hallucination rate, and cost
- On-call playbook for the incidents the risk register anticipated
- Handover of the prompts, evals, and guardrails, with documentation for the next engineer
The agent systems we
have actually shipped.
From AI agents in production to regulated client systems: the engineering work SDEN has actually shipped, orchestrated, and still runs today.
How an AI-native team works.
A look inside SDEN: the technologies we run, the AI agents live in our own production, and how an organization becomes AI-native, from the software to the people. The same path we take clients down.
- The stack and AI agents we run in production
- How we operate: evals, guardrails, on-call
- Becoming AI-native, from software to people
Security is not optional.
Security is an engineering discipline, not a checkbox. It's built in from the architecture stage, on every delivery, with no extra configuration and no compromise.
Data protection
Encryption, isolation, and access control applied to every delivery.
End-to-end encryption
Data encrypted in transit (TLS 1.3) and at rest (AES-256), with keys managed and rotated regularly.
Access control & MFA
Multi-factor authentication, SSO, and granular role-based permissions across every application we ship.
Data isolation
Multi-tenant architecture with strict separation: each client's data stays their own.
Deploy in your region
Deployed in the region you require (US, Canada, or EU) with encrypted, restorable backups.
Learn free.
No signup.
SDEN is built as a ladder, and this is the free rung: courses, playbooks, and live tools that show you where you stand before you spend anything.
How AI is rewriting
business operations.
One cornerstone article per engineering discipline: what AI changes, what it does not, and how a senior team ships the difference.
RAG for business: building knowledge assistants that actually work
Retrieval-augmented generation grounds AI answers in your data. What RAG is, when it beats fine-tuning or a plain prompt, and what separates a knowledge assistant you can trust from a demo.
AI agents for business: where they work, and where a workflow wins
Agents are powerful and easy to get wrong. When a task genuinely needs an agent, when a plain workflow is the better answer, and how to keep an agent safe and affordable in production.
What it means to be an AI-native organisation
An AI-native organisation is built around AI from the start, not bolted onto old processes. What that means, what it is not, and what it changes for the business.