AI agents

Agents that read, decide, and act, with a person one click away.

Agents that answer from your data and use only the tools you approve. Anything irreversible waits for approval. We test each one against your own past cases before it meets a customer.

What it looks like

Examples we've shipped in this category.

Inbox and ticket triage

6–8 weeks

Reads every incoming message, drafts the reply, files the ticket, and routes the hard ones to the right person with context attached.

Support agent that looks things up

8–10 weeks

Answers from your help center, order history, and billing data, cites its sources, and escalates refunds and account changes.

Research and briefing agents

6–8 weeks

Prepares account briefs, competitor summaries, or weekly ops updates from your systems and the web, on a schedule.

Voice agents for scheduling and intake

8–12 weeks

Answers the phone, books or moves appointments in your calendar, and collects intake details, with a warm handoff to staff.

Finance operations agents

8–10 weeks

Matches invoices to purchase orders, chases missing approvals, and prepares reconciliations for a human to sign off.

Sales operations agents

6–9 weeks

Adds detail to leads, scores them against your ideal customer, drafts outreach, and keeps the CRM accurate without a rep touching it.

How we build it

The same discipline every time.

  1. 01

    Define the job and its boundaries

    What the agent may read, what it may do on its own, and what always waits for a person. This becomes the tool list and the approval policy.

  2. 02

    Build the evaluation set first

    An evaluation set is a collection of real past cases with known right answers. We pull 200 to 500 from your systems and label the correct outcome. Nothing ships until the agent clears the pass mark you set on that set.

  3. 03

    Connect it to your data and give it tools

    The agent looks things up in your docs and records. It gets narrowly scoped tools that check their inputs. Every action is logged with the reasoning behind it.

  4. 04

    Shadow mode, then supervised, then live

    The agent drafts without sending. Then it sends with approval. Then it acts on its own for the cases it has proven it handles. You decide each step up.

What you receive

Deliverables

  • The agent, deployed in your infrastructure with your AI model keys
  • An evaluation set and a scorecard you can re-run after any change
  • Approval interfaces where your team already works (Slack, email, or a queue)
  • A tracing dashboard: every run, every tool call, every cost
  • A monthly cost estimate for AI model and tool usage
  • Team training, an operator runbook, and 30 days of support
Typical tools

Tools we reach for

Chosen per engagement. If you already run something that fits, we build on it.

ClaudeClaude Agent SDKLangGraphPostgres + pgvectorSupabaseLangfuseZendeskHubSpotTwilioDeepgramNext.js
Case study · B2B SaaS · Series B A first-line support agent that resolves 61% of tickets on its own Read the case study
FAQ

Questions we get about ai agents.

How do you stop the agent from making things up?
Three layers. It can answer only from records it looks up, and it must cite them. It can act only through tools we define, with checked inputs. And we score it against real past cases before launch, so you know its accuracy before a customer does.
What does it cost to run each month?
AI usage for a support agent handling a few thousand tickets a month is typically $150 to $600. The scope includes a cost estimate, and a dashboard tracks it after launch.
Which models do you use?
Whichever scores best on your evaluation set at an acceptable cost and speed. That is usually a top-tier model for reasoning steps and a smaller one for sorting. If your data can't leave your environment, we run models you can host yourself inside it.
What if it gets something wrong after launch?
We log every action, and it can be undone where the underlying tool allows. Wrong answers go back into the evaluation set, and we re-score the agent before the fix ships. On a retainer, this loop runs monthly.

Tell us what your team still does by hand.

One paragraph is enough. We reply within a business day with questions or a time for a 45-minute call.

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PagesHomeAutomation and AI agents for growing companiesPagesServicesAutomations, workflows, and agentsServicesAutomationsRule-based handoffs that remove copy-paste work between tools.ServicesComplex workflowsProcesses that span several tools, with approvals, exceptions, and clean data.ServicesAI agentsAgents that read, decide, and act across your tools. People stay in the loop.PagesSolutions by industryLogistics, healthcare, professional services, e-commerce, SaaSPagesWorkCase studies with the numbersCase studiesLumen LogisticsQuote requests from inbox to booked load in nine minutesCase studiesHalcyonA first-line support agent that resolves 61% of tickets on its ownCase studiesMeridian Dental GroupRecall reminders that cut no-shows by a third in 90 daysPagesPricingFixed prices and the instant estimatorPagesSample scopeThe one-page scope every client receivesPagesSample evaluation reportWhat ships with every agent buildPagesExample dashboardThe reporting every client getsPagesAboutA small senior teamPagesInsightsField notes from the buildInsightsWhen not to use an AI agentMost automation value comes from rule-based work. A guide to choosing rules, workflows, or agents for each process.InsightsDesigning approval gates that people useHuman review fails when people are asked too often or too late. Here are the patterns we ship, with the numbers behind them.InsightsThe true monthly cost of an automationAI model usage, tool seats, hosting, and the upkeep nobody budgets for. A worked example from a real quote pipeline.PagesContactBook a discovery call
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