Describe the goal, current systems and constraints. We will review the scope and identify the next evaluation step.
Expected value, implementation cost, and the measurement method are agreed before development begins.
Milestones are set around data readiness, integrations, security review, and the production environment.
Current cost, cycle time, quality, capacity, or risk is established before improvement is measured.
Results are reported against approved source data, a defined system boundary, and an agreed measurement period.
ALLTIPLY builds AI assistants for customers, employees and support teams. Each one answers from your documentation, policies and systems, in the channel people already use: your website, your product, your help desk or internal chat.
Human-approved answers are served first, generated answers are grounded in your sources and cited, and anything out of scope goes to a person. The product help assistant rebuild analysis describes a rebuild completed in about three weeks. The related retrieval field report identifies the demonstration proof, the defects found and the measurement work still planned.
These firsthand reports distinguish production use from proof environments and prototypes.
Explore our work and evidence status or read the research library directly.
Four phases from a question inventory to an assistant in production.
We collect real questions, identify each audience and channel, and agree which topics the assistant answers, which it escalates and how it is measured.
We organize source content, write approved answers for high-volume and sensitive questions, and build the evaluation question set.
We build retrieval, answer generation, fallbacks and escalation, connect the channel, and test against the question set before release.
The assistant goes live with answer logging, gap reports and a release gate, so quality holds as your content and products change.
