AgentsCustom-built · Human-approved · SOC 2

An Agent is a specialist on retainer.
It runs 24/7 and asks before it acts.

Not a chat window. Not a wrapper on a public model. A purpose-built operator that watches one job, does one job, and hands the result back to a human for approval before anything external happens.

Why Agents are safe in enterprise

A retainer specialist, not a black box.

Every enterprise objection to AI agents resolves to one of three concerns: safety, scope, and ROI. We designed the answer into the architecture.

Safe by default

No external action is ever taken without explicit human approval. The agent shows what it's about to do, the exact payload, and waits. Redirect, refine, or reject at any step.

Purpose-built

Each agent is scoped to a single job, not a general chatbot. Narrower scope means higher accuracy and predictable failure modes.

Measurable ROI

Every agent ships with a pre-agreed success metric: hours reclaimed, revenue lifted, fraud caught, tickets deflected. If the number doesn't show, we rebuild it.

How custom agents get built

We don't sell pre-made agents. We build yours.

We keep a small roster of reference agents so you can see the shape of what's possible. The agent you deploy is the one we build for your organization, your data, your systems, and your approval policy.

Our engineers come from the top 1% of AI practitioners in North America. Average turnaround from first call to production-deployed custom agent: 72 hours to 1 week, depending on scope.

From scope to live · 72h – 1wk
  1. 01
    Scope call

    One session with your team + one of our engineers. Exit with a one-page agent spec and a success metric.

  2. 02
    Data access

    Least-privilege credentials to the systems the agent needs. Nothing else. Read-only by default.

  3. 03
    Build

    48–72 hours of focused engineering. We test against your real data, not a sandbox.

  4. 04
    Human-in-the-loop review

    You approve the first N runs before the agent runs unattended. Approval UI ships with it.

  5. 05
    Deploy

    Production-deployed. Monitored. Measured against the success metric agreed on day one.

Reference agents

Six examples of what we've built.

Every one of these started as a scoped client request. Yours will look different, but the shape of the outcome will feel familiar.

Sales Enablement

Sales Co-Pilot: live research during calls

83%
revenue growth in AI-using sales teams
Salesforce State of Sales 2024
50%
less prep time per prospect call
Gong Labs 2024
2.5×
higher meeting-to-opportunity rate
Salesforce 2024

A mid-market B2B client asked us to build an agent that pulls live prospect research during the call. Company news, recent funding, exec changes, product launches overlaid on the sales rep's screen as the conversation unfolds.

It also captures objections in real time and surfaces the best rebuttal from the team's own winning calls.

Social Listening

Reddit Monitor: first-responder brand advantage

10×
engagement lift for first-hour responses
Reddit Community Insights
< 30s
alert-to-marketing-team latency
30+
monitored subreddits per client

A D2C consumer client asked us to watch 30+ industry subreddits for mentions of their brand and closest competitors. First-hour commenters capture the bulk of thread visibility.

The agent notifies their marketing team in under 30 seconds with context, sentiment, and a draft reply the team can edit before posting.

Research

Deep Research Agent: reports in minutes, not days

12×
faster than a human analyst
OpenAI Deep Research 2025
500+
sources synthesized per report
30 min
average runtime for a full brief

A client asked us to build an autonomous research agent that delivers results: market sizing, competitor teardowns, regulatory scans, investor memos.

It plans its own research path, runs dozens of tool calls in parallel, cites every claim, and ships a structured report your analyst can pass upstream without a rewrite.

Marketing

Outreach Agent: signup and churn lifecycle

320%
more revenue vs. non-automated email
Mailchimp 2024
451%
increase in qualified leads with automation
Annuitas
open rate on welcome sequences
Experian

A SaaS client asked us to build an agent that watches two signals, new signups and recent churners, and drafts personalized outreach for each.

New signups get a user-interview request with calendar options. Churners get a win-back sequence with a tailored promotion.

Full drip scheduling, A/B on subject lines, and a dashboard showing lift per campaign against a held-out control.

Finance · Fraud

Fraud Co-Pilot for credit unions and insurers

90%
real-time fraud catch rate
FICO Fraud Analytics
50%
fewer false positives vs. legacy rules
FICO
$5.3B
annual CU fraud loss the industry is trying to stop
NAFCU 2023

A credit union asked us to watch every member transaction in real time. Card swipes, ACH pulls, wire initiations, new-device logins.

The agent flags anomalies against the member's own 12-month pattern, cross-references known fraud rings, and drafts the member-outreach SMS and fraud-case file before the analyst opens the ticket.

On high-confidence cases it auto-freezes the card and waits for human approval to release.

Administrative

Digital Secretary: exec-level time reclamation

23%
of exec time historically lost to admin
Harvard Business Review
6 hrs
reclaimed per executive, per week
40%
reduction in admin overhead
Gartner AI Agent Outlook 2025

An executive team asked us to build a digital secretary that handles inbox triage, calendar negotiation, meeting prep packets, follow-up drafting, and expense categorization, with approval gates on anything external.

The agent knows which threads to bubble up, which to archive, which to schedule, and which to draft a reply for.

Six reclaimed hours per exec per week, measured against their pre-deployment baseline.

Make any Agent, for any department

Our engineers are the top 1%. Turnaround is 72 hours to a week.

The six above are starting points, not a catalog. If your team can describe the job in a one-page spec, we can ship the agent. If the first deployment doesn't hit the success metric we agreed on, we rebuild it on our time.

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