Talk to an AI architectBook a free AI assessment
AgentsWorkflowsService DeskAnalytics
AI Agent DevelopmentAI Development CompanyAI Automation AgencyConversational AIGenerative AI ConsultingAnswer Engine OptimizationAI SEO AgencyAI Agents for Marketing
AI agents for businessHow to become AI-nativeBuild vs buy AI agentsAI agents vs chatbotsAI agent development costAI agent ROI calculatorAI agent glossaryTop AI agent companiesFounders Grit podcastBlog
IntegrationsBlogCareersBook a free AI assessment
AI-native implementation partner

Ship your first production AI agent.

Bring one workflow where time, money, or follow-up keeps leaking. Gaper maps it, builds the agent, proves the savings, launches it in your stack, and hands your team a system they can run.

Book the free assessmentSee where agents pay off
30 min workflow teardown1 savings caseNo platform lock-in
Free AI assessment

What you leave with

  • The workflow worth automating first
  • A plain-English build-vs-buy call
  • The first agent worth shipping
  • The savings metric to prove before launch
  • A production path your team can own
Request info ->
ProductionNot another demo
Model-agnostic
In your CloudYour auth, your data
You own itCode and runbook handed over
Opportunity model

Put a measurable target on the workflow.

These are illustrative operating ranges, not guaranteed results or client claims. Every engagement replaces them with a measured baseline, controlled pilot, and source-labeled result.

Modeled opportunity15-30%fewer no-shows

Healthcare scheduling agents fill empty slots, reduce missed visits, and recover lost appointment capacity.

Modeled opportunity60%less intake admin

Legal intake workflows can move qualification, routing, follow-up, and case setup out of manual queues.

Modeled opportunity5xfaster close prep

Accounting agents reconcile transactions, chase exceptions, and prepare close work inside the ledger flow.

Modeled opportunity3xfaster quotes

Revenue and operations agents pull context, update systems, and move quote workflows through approval.

Pick the workflow

Where agents pay for themselves.

Start with a painful operating lane, not an AI category. These are the places where agent work can remove admin, shorten cycle time, and keep humans on the exceptions.

01

Customer support

Stuck because

Tickets bounce between helpdesk, policy docs, order data, and human approval.

Agent outcome

Resolve, update the case, and escalate with context.

See workflow ->02

Finance & accounting

Stuck because

Close work stalls on matching, exceptions, approvals, and follow-up.

Agent outcome

Reconcile, chase exceptions, and prepare the close faster.

See workflow ->03

Sales operations

Stuck because

Leads, CRM updates, research, routing, and handoff work sits between tools.

Agent outcome

Enrich, score, update CRM, and prep reps before calls.

See workflow ->04

Document processing

Stuck because

PDFs, emails, forms, and attachments create manual review queues.

Agent outcome

Extract fields, validate against systems, and route exceptions.

See workflow ->05

Internal knowledge

Stuck because

Employees ask the same questions while answers live across stale docs.

Agent outcome

Answer with citations, freshness, and clean human handoff.

See workflow ->06

Healthcare, legal, accounting ops

Stuck because

Regulated workflows need judgment, audit trails, and human review.

Agent outcome

Launch bounded agents with approvals and traceable decisions.

See workflow ->
What we ship

Agents that act inside the workflow.

Not a chat window bolted onto your site. The agent reads the request, checks systems, takes the approved action, writes back, and leaves a trace your team can inspect.

Production agent layerSpecialist agents deployed into real workflows.

Each agent gets tools, permissions, evals, escalation paths, and production ownership before users depend on it.

Live

Support Ops Agent

Resolves tickets, looks up accounts, updates Zendesk, and hands off with full context.

92% eval pass
Ready

Finance Exceptions Agent

Reads invoices, reconciles mismatches, and routes approvals to the right owner.

AP / AR
Live

RevOps Agent

Enriches leads, updates Salesforce, and prepares reps before high-intent calls.

CRM writeback
Review

Knowledge Agent

Answers from internal docs with citations, freshness checks, and “I don’t know” paths.

Grounded RAG
Scope workflowBuild toolsGate with evalsGo live
24hFirst build12 evalsRelease gatesYour stackDeploy target
The production filter

A useful agent does more than give a good answer.

Every first engagement is designed around the operational details that demos skip: systems of record, representative test cases, risky actions, escalation, and an owner after launch.

01Integration

Touches the real workflow

The agent reads from and writes back to the systems where work already happens.

02Measurement

Has a measurable job

We define the baseline and the operational metric before claiming impact.

03Governance

Keeps people in control

Approvals, confidence thresholds, and escalation sit on consequential actions.

04Ownership

Can be operated without us

Your team receives the codebase, evals, monitoring view, and runbook.

05

Customer operations

Read policy and order context, take permitted actions, and hand off the exception with full context.

06

Finance operations

Match documents and transactions, surface exceptions, and prepare human review inside the existing close process.

07

Revenue operations

Research, enrich, route, and update the CRM while preserving the team’s approval rules.

How Gaper works

Claim the first workflow. Then reuse the foundation.

A working release is more useful than a broad transformation deck. The first agent creates the evaluation, integration, and ownership pattern for the next one.

  1. 01

    Assess one expensive workflow

    Review the SOP, queue, systems, exceptions, and owner. Decide whether a product, automation, or custom agent is the right answer.

    Artifact
    Workflow and build-vs-buy brief
    Control
    Scope excludes actions without a clear owner or safe fallback.
  2. 02

    Build against real cases

    Connect the data and tools the job needs, then create a representative eval set before users rely on the system.

    Artifact
    Working agent and eval baseline
    Control
    Every risky action is bounded by policy, threshold, or approval.
  3. 03

    Run in a sandbox

    Compare agent output with the current process, observe failures, and tune the handoff path before enabling write access.

    Artifact
    Release decision and trace review
    Control
    Sandbox outputs are reviewed before production permissions are enabled.
  4. 04

    Launch supervised, hand over cleanly

    Move the bounded workflow into production with monitoring, rollback, and a named operating owner.

    Artifact
    Runbook, dashboard, and knowledge transfer
    Control
    A named owner can pause, roll back, or change policy.
Ownership is a deliverable

Your team is not left with a vendor dashboard and a promise.

Handoff begins during delivery. The people who will operate the workflow see the evaluation criteria, review traces, and receive the materials needed to extend or retire the agent responsibly.

Versioned brief

Workflow decision log

The workflow boundary, exclusions, approvals, and escalation policy.

Owner: Business process owner
Repository and architecture diagram

Code and infrastructure map

The services, connectors, secrets boundaries, and deployment path.

Owner: Engineering or IT owner
Representative test set and release checks

Evaluation suite

The cases used to validate changes before they reach production.

Owner: Product and operations owner
Runbook and trace dashboard

Operating runbook

Monitoring, triage, rollback, and the cadence for improvement.

Owner: Named service owner
Why gaper

Most teams get a demo. You need production.

Most “AI agent” vendors
  • Hand you a slick demo
  • Run in their sandbox
  • Stall on your real data and edge cases
  • Leave you to operate it
Gaper
  • Ships to production in your stack
  • Runs in your cloud, your auth, your data
  • Evals and guardrails on real cases
  • You own the code; we run the SLA
What we do

From stuck workflow to owned agent.

Gaper helps you decide what should become an agent, build it into your stack, prove it works, and launch it with a team that can run it.

01Find the workflow

Start with stuck work

We pick one expensive workflow where people copy, check, approve, route, and update the same things every week.

OutputA ranked workflow with expected savings, risk, and build-vs-buy call.
Compare options ->02Build the agent

Wire it into your stack

The agent gets tools, permissions, data access, approvals, traces, and human gates before users depend on it.

OutputA working agent connected to the systems the workflow already uses.
Build the agent ->03Prove the savings

Measure before launch

We test against real cases, track cycle time, handoffs, accuracy, cost, and the exceptions that should still go human.

OutputAn eval suite and savings case before the agent reaches production.
See workflows ->04Launch in your stack

Move past the demo

We deploy with your auth, your cloud, your data boundaries, rollback paths, and a named owner.

OutputA supervised production release instead of another sandbox demo.
Plan production ->05Handoff + operate

Your team can run it

You get code, runbook, monitoring, and operating rhythm. We can hand it over or run the SLA with you.

OutputA production agent your company owns after launch.
See operating model ->
How we work

A production system, not a proof-of-concept.

The analysis says the open market gap is pilot-to-production. This is the operating model: start with one workflow, verify it against real cases, then launch with a named owner.

01

Assess one workflow

Bring a real process: tickets, invoices, leads, documents, approvals. We make the build-vs-buy call before anyone writes code.

Free AI assessment
02

Map the production path

We define inputs, systems, owners, risky actions, success metrics, and the smallest supervised release worth shipping.

Workflow map + scope
03

Build and verify

The agent gets tools, permissions, evals, guardrails, traces, and human approval gates before it reaches users.

Agent + eval suite
04

Launch with ownership

We deploy into your stack, document the runbook, train the owner, and either hand it over or operate it under SLA.

Code + runbook
DesignWe map your workflows, find where an agent creates real leverage, and scope the smallest thing worth shipping.BuildWe build production agents on OpenAI, Claude, or Gemini, wired into your existing systems with evals and guardrails.DeployWe put it in production, governed, observable, and owned by your team, live in as little as 24 hours.
Start here

Not sure if you need an agent, automation, or an off-the-shelf tool?

Read: AI agents for business
See what operators from other companies think about AI Agents:
Learn more
Production AI agents, shipped with an owner

Ready to deploy your first agent?

Book a free 30-minute assessment. We'll map the highest-leverage workflow and scope the smallest thing worth shipping, live in as little as 24 hours.

Build, deploy, runYour cloudYou own the code