Shipping AI Products with Confidence

Profiteek treats AI delivery like any other mission-critical system. The same Parker-inspired discipline we apply to typography and layout applies to prompt flows, memory layers, and evaluation harnesses.

1. Align on measurable outcomes

Before writing a single line of agent code we run a discovery workshop to confirm:

• The workflow we are augmenting or automating
• The data sources that can be used safely
• The guardrails, escalation paths, and reporting expectations

2. Architect the stack

We combine retrieval augmented generation (RAG), function calling, and deterministic fallbacks. A typical Profiteek stack includes:

graph LR
  A[User request] --> B[API Gateway]
  B --> C[Policy + Auth]
  C --> D[Vector Store]
  C --> E[Tooling Service]
  D --> F[LLM]
  E --> F
  F --> G[Observability]

3. Evaluate relentlessly

Every model update goes through automated evals with synthetic and real user data. We track latency, cost per request, satisfaction score, and fallback frequency.

4. Launch with an ops plan

Shipping is not the finish line. We set up incident response, human-in-the-loop QA, and roadmap ceremonies that treat AI workstreams like first-class product surfaces.