AI and machine
learning
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.
What this domain covers
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.
We answer three ways: an audit of every integration, custom workflows built against a measurable outcome and owned by you, or an embedded engineer who leads the discipline until your team can carry it.
The hard part is never the model. It's deciding what to measure and keeping the loop honest in production. We settle the success metric first, then reach for the simplest thing that clears the bar: a well-prompted hosted model, RAG over your data when answers depend on private content, fine-tuning only after both hit a ceiling.
Models are commodities. The evaluation is the moat.
AI and machine learning: the SDEN defaults
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.
More from
the SDEN blog.
Cornerstone writing from the SDEN team: what AI changes, what it doesn't, and how a senior team ships the difference.
How AI is rewriting business operations, and where it still has to earn trust
AI is moving from demo to production inside operating businesses. What changes, and what to refuse, when intelligence becomes a load-bearing part of the stack.
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.
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