Taz designs and delivers AI operating systems for teams that need more than a generic chatbot. We turn fragmented knowledge and ambiguous workflows into bounded, reviewable systems with clear source boundaries, evaluation checks, and human approval paths. Our work spans AI architecture, retrieval-augmented knowledge systems, AI evaluation and guardrails, agent-ready commerce, RFP and security response workflows, and AI cost and context audits. Each engagement produces practical, source-linked artifacts: architecture maps, risk and evaluation plans, retrieval designs, owner registers, and implementation-ready handoffs. We are explicit about scope and uncertainty. Consequential decisions and external actions remain human-approved.
Taz designs and delivers AI operating systems for teams that need more than a generic chatbot. We turn fragmented knowledge and ambiguous workflows into bounded, reviewable systems with clear source boundaries, evaluation checks, and human approval paths. Our work spans AI architecture, retrieval-augmented knowledge systems, AI evaluation and guardrails, agent-ready commerce, RFP and security response workflows, and AI cost and context audits. Each engagement produces practical, source-linked artifacts: architecture maps, risk and evaluation plans, retrieval designs, owner registers, and implementation-ready handoffs. We are explicit about scope and uncertainty. Consequential decisions and external actions remain human-approved.
Location and contacts
Major clients
Processes and approach
How do you gather and validate client requirements?
We begin with a structured discovery: clarify the decision or workflow to improve, map source systems and owners, identify constraints, and record assumptions and open questions. We turn this into a scoped brief with acceptance criteria before implementation.
How do you ensure alignment with client goals and business strategy?
Each engagement ties proposed work to a defined operating goal, such as improving knowledge retrieval, evaluating an AI workflow, or preparing a governed implementation. We align scope, success signals, risks, and approval points with the client before work starts.
Which software development methodologies do you use (e.g., Agile, Waterfall, Scrum)?
We use an iterative, evidence-led delivery approach: discovery, design, prototype or audit, evaluation, review, and handoff. The cadence is adapted to the engagement; consequential decisions and external actions remain human-approved.
How do you keep clients and stakeholders updated on project progress?
Clients receive concise, source-linked progress updates that show completed work, current findings, decisions needed, risks, and the next milestone. Deliverables are organized so owners can review assumptions and approve changes efficiently.
How frequently do you hold check-in meetings or status updates?
We set the update rhythm at kickoff based on scope and stakeholder needs. For active delivery, we normally use milestone reviews and regular written updates, with additional check-ins whenever a material decision, risk, or approval is needed.
What quality assurance practices do you follow?
Quality assurance is built into the work: source-boundary checks, requirement traceability, evaluation criteria, reviewable artifacts, and human approval for consequential outputs. We test assumptions and document limitations rather than presenting uncertain outputs as facts.
How do you identify and manage project risks?
We identify risks during discovery, including missing sources, unclear ownership, privacy or security constraints, model limitations, and uncontrolled external actions. Risks, mitigations, and approval gates are kept visible in the delivery plan.
What kind of support or maintenance do you offer after delivery?
After delivery, Taz provides implementation-ready handoffs, documentation, and a review of next steps. Follow-on support can cover iteration, evaluation updates, knowledge maintenance, or scoped improvements under a separate agreed engagement.