AI Agents & Automation
Your competitors are using agents. Your team is still doing it manually.
We build and deploy custom AI agents for mid-market businesses. In six weeks, your highest-cost manual workflow runs on autopilot — every decision logged, every escalation human-controlled.
01The Problem
The status quo isn’t free. It has a price.
of employee time goes to repetitive tasks a well-built agent handles end to end.
loaded cost of the person doing that work — against roughly twelve cents per task for an agent.
growth in enterprise AI spend since 2023. Your competitors are not waiting for permission.
2.3M conversations handled by one agent — $40M in projected annual savings.
$100M ARR in 21 months selling agents that resolve, not deflect.
51% of support volume resolved with no human in the loop.
$575K saved and 5× coverage from a single deployment.
Same work. Different economics.
Gartner projects $15T in B2B purchases will be agent-mediated by 2028. This table is why — and the window to be early closes in about 18 months.
02Investment
What it costs. What it returns.
Agent Audit
$5K–$15K2 weeks
Best for: For teams that want the map before the build.
- Workflow cost analysis
- Agent-fit assessment
- ROI model you keep
- Build/no-build recommendation
Agent Sprint
$25K–$75K6 weeks
Best for: One high-cost workflow, live in production.
- Custom agent, built on your data
- Runs in your environment
- Every decision logged
- Human escalation built in
- 90 days of monitoring
Full Transformation
$150K+3–6 months
Best for: Agent infrastructure across the whole operation.
- Multi-agent architecture
- SOC 2 / HIPAA compliance documentation
- Audit logs & governance framework
- Team training + handover
ROI is modeled before you commit — payback typically lands inside the first month of production.
Fair questions
Serious answers for serious systems.
Is this just a chatbot?
No. Chatbots answer questions. Agents complete work — they read the ticket, check the account, take the action, and log the reasoning. A chatbot is a mouth; an agent is a pair of hands.
What stops it from making expensive mistakes?
Confidence thresholds. Above the line, the agent acts and logs its reasoning; below it, a human decides. Every action is reversible, every decision auditable, and the thresholds are yours to set.
Will it work with our legacy systems?
That is usually where the money is. Agents meet your systems where they are — APIs where they exist, structured interfaces where they don’t. The audit tells you exactly what integration takes before you spend a dollar on it.
Who owns the agent — and our data?
You do, on both counts. The agent runs on your infrastructure, your data never leaves your environment, and you hold full source access. We are model-agnostic, so you are never locked to one vendor.