We help Ecommerce & Retail teams deploy autonomous AI agents into your revenue stack. Most "AI agents" are rule-based scripts dressed up in better marketing.
An ads agency and an email agency will never share a customer view.
Most "AI agents" are rule-based scripts dressed up in better marketing. A real agent reasons toward a goal, picks its own next step, and knows when to stop and ask a human. We build one only when the work is genuinely unpredictable, and we keep a person on the trigger for anything that touches a customer or a budget.
One warehouse-native architecture where the same enriched data feeds paid, lifecycle and the storefront. Server-side tracking and identity resolution produce a single customer view in BigQuery. That view feeds enriched segments back into the ad platforms, and the same data drives the email and SMS flows — so paid and lifecycle compound instead of competing.
Pipeline at machine volume, personalized enough to actually get replies. Agents research target accounts, read live buying signals, and write outbound personalized to each contact, booking meetings your reps close. A human approves every message before it sends, so you scale volume while your sender reputation stays intact.
Campaigns watched around the clock, experiences tuned to every visitor. Agents catch spend drift or a broken pixel the moment it happens, rebalance budgets toward what is working, and have reporting ready before standup. On site, offers adapt to each visitor in real time, tested in multivariate experiments and attributed to revenue.
Internal tools on your data, your stack stitched so copy-paste work ends. Claude- and GPT-powered proposal writers, research copilots, and knowledge assistants run on your data with scoped access, so answers stay grounded in your numbers. Your CRM, ad platforms, email, billing, and warehouse connect into workflows that keep records in sync.
Guardrails, monitoring, and approval gates on every agent we ship. Every agent ships with an evaluation suite that scores output against real examples before it touches production. High-stakes actions pass through human approval gates, live monitoring flags drift the moment it appears, and every action is logged and auditable.
We map your team's manual workflows, quantify the hours and the leakage, and hand back a ranked automation plan. The highest-return agent is scoped in detail: a named job, the data it may touch, its escalation path, and the success metric it will be judged on. The build starts before the month is out.
The first agent is built and tested against its evaluation suite, with monitoring and human approval gates wired in before it touches a real customer or budget. By day 60 it is live inside your stack and reporting starts reconciling its activity to pipeline. Your team works with it from day one.
The next agents on the plan go live one at a time, each with its own scoped job and success metric. We tune against the evals, prove the hours returned and pipeline influenced in your own dashboards, and keep documentation and training current as the roster grows. We agree the next quarter's plan together.
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