Custom development · Build & operate

Custom AI agents, built and run in production.

We design, build, deploy, and operate custom AI agents — voice, chat, and workflow — grounded in your data, wired into your systems, and shipped with guardrails, monitoring, and human escalation. Not a demo: production software that does the work.

Weeks

Scope to production

End-to-end

Build + deploy + operate

Your stack

Real integrations

The problem

Most 'AI agent' projects stall at the demo.

A prototype that works once in a sandbox is easy. An agent that handles real customers, connects to your systems, behaves safely, and keeps working as models and data change is the hard part — and it's what actually moves revenue.

Demos don't survive production

Real traffic brings edge cases, bad inputs, and integration failures. We build for production from day one.

Integrations are where it breaks

An agent is only useful if it can read and write your real systems. We do the unglamorous integration work properly.

Unsafe agents are a liability

Hallucinations and unbounded actions are real risk. We ship guardrails, grounded retrieval, and human-in-the-loop.

Agents decay without operators

Models update, prompts drift, data changes. We stay on after launch — monitoring, evaluating, and tuning.

How it works

How we build an AI agent.

01

Scope & design

We map the use case, success metrics, data sources, integrations, and safety requirements before writing code.

02

Build & ground

We build the agent with governed retrieval over your approved data and wire it to your real systems and channels.

03

Guardrail & evaluate

We add guardrails, fallback logic, and an evaluation set; we test against real scenarios before launch.

04

Deploy & operate

We ship to production with monitoring and logging, then tune against live traffic on an ongoing basis.

Integrations

Built on production-grade foundations.

We use the right model and tools for the job — no vendor lock-in pressure.

OpenAIAnthropic ClaudeGoogle GeminiVapiRetellTwilioPinecone / pgvectorFirebaseCloudflareAWSHubSpotSalesforceStripe
Deployment timeline

From scope to production in weeks.

  1. Week 1

    Discovery & architecture

    Use-case definition, data and integration mapping, safety requirements, and a fixed plan.

  2. Weeks 2–4

    Build & integrate

    Agent build, grounded retrieval, integrations, guardrails, and an evaluation set.

  3. Week 5

    Test & harden

    Adversarial testing, edge-case handling, and human-handoff calibration; you sign off.

  4. Week 6+

    Launch & operate

    Production deploy with monitoring; ongoing tuning and feature work under a retainer.

Proof

What this looks like in production.

How we work
Build + operatenot build-and-leave

We ship production agents with monitoring, evaluation sets, fallback logic, and human escalation from day one — and we keep operating them as models and data change. That's the difference between an impressive demo and an agent your business can actually depend on.

Timeframe
ongoing after launch
Workflow
scoped build → guardrailed deploy → live tuning
Stack
OpenAI / Claude · Vapi · Pinecone · your CRM
FAQ

Questions buyers ask first.

Straight answers — the ones that come up in nearly every audit call.

We design, build, deploy, and operate custom AI agents end-to-end: voice, chat, and workflow agents grounded in your data, integrated with your systems, shipped with guardrails and monitoring, and tuned against live traffic after launch.

Have an AI agent idea that needs to actually ship?

Book a free 30-minute audit. We'll scope it honestly — what to build, how, and what it costs.