astrix*
/ Services

/ AI readiness

Find the AI workflow worth building before you build it.

We review the workflow, data access, existing systems, risks, and success criteria so your team can choose a practical first AI investment.

Start an AI auditSee relevant work

/ Audit shape

Clarity before implementation.

The audit identifies where AI can reduce manual work, where the data is not ready, and where software foundations need attention before automation.

  • Workflow map and bottleneck list.
  • Data and integration readiness review.
  • Risk register for privacy, permissions, review, and failure modes.
  • Roadmap for the first useful version and later phases.

/ Outcome

A build plan your team can evaluate.

You leave with a practical recommendation: build now, fix the foundation first, narrow the workflow, or avoid the idea because it will not create enough leverage.

  • Scope brief for the recommended workflow.
  • Success criteria and examples for evals.
  • Recommended tools, integrations, and handover needs.

/ proof

Relevant work
behind the claim.

/ AI product engineering

TextCortex

Product engineering on AI knowledge workflows, chat interfaces, and user-facing AI systems used by large audiences.

/ Event operations

Varuna

Technical leadership across a CRM and event operations platform with workback plans, email ingestion, AI task generation, and production architecture.

/ related

Keep exploring.

First 90 days of AI

A practical sequence for choosing and measuring AI work.

AI workflow integration

Build the workflow after readiness and scope are clear.

Human-in-the-loop AI

Design review paths around risky model output.

/ questions

Do we need clean data before the audit?

No. Messy data is part of the audit. We need examples of the workflow, systems involved, and what success should look like.

Is the audit only for AI agents?

No. It covers AI workflows broadly: retrieval, summarization, routing, classification, assistant interfaces, and human review systems.