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 glossaryTop AI agent companiesFounders Grit podcastBlog
IntegrationsBlogCareersBook a free AI assessment
custom ai development

Custom AI development that ships agents into your stack.

Gaper builds custom AI agents, copilots, and data pipelines end to end and deploys them inside your systems on your data and auth. You get the code, evals, and runbook, and your team owns what we ship.

Book a free AI assessmentWhat is an AI agent?
Map the workflowBuild the supervised agentSandbox, verify, go live
gaper · opportunity map
Support triage92
Invoice processing84
Lead enrichment71
Report drafting58
ranked by ROI × feasibilityship #1 first
In one sentence

Custom AI development is the design, build, and deployment of bespoke AI software, agents, copilots, retrieval systems, and data pipelines, wired into your existing stack on your data and auth. Gaper delivers it end to end and hands over the code, evals, and runbook for your team to own.

ProductionNot another demo
Model-agnostic
In your cloudYour auth, your data
You own itCode, evals, runbook
Why this matters

A working demo and a production system are different problems. The gap between a prototype that impresses and software that survives real data, edge cases, and daily users is where most AI work stalls, and closing it is the whole job.

Production filter
  • Does it touch real systems?
  • Can the outcome be measured?
  • Where does human approval stay?
  • Who owns it after launch?
Free AI assessment

Book a free assessment. We will identify one high-leverage workflow, make the build-vs-buy call, and scope the smallest production release.

Map your first production agent
How we work

From strategy to production, owned by your team.

  1. 01

    Map the workflow

    We start from the documents, SOPs, portals, inboxes, and spreadsheets your team already uses, then turn the repeatable path into an agent workflow map.

  2. 02

    Build the supervised agent

    We build on OpenAI, Claude, Gemini, or the right model for the job, with evals, guardrails, citations, and human approval gates where risk matters.

  3. 03

    Connect the stack

    The agent gets the data layer, APIs, MCP tools, auth, and write-backs it needs to finish work inside your systems, not beside them.

  4. 04

    Sandbox, verify, go live

    We launch in a sandbox, verify every run, then move into supervised production with traces, rollback, and an owner.

What we build

Agents wired into the systems you already run.

Custom AI agents

Goal-driven agents that plan and take multi-step action across your systems, scoped from your real workflows, not single-turn bots.

Copilots and assistants

In-app assistants grounded in your data and rules, embedded where the work already happens.

Retrieval and knowledge

Grounded answers over your documents and data, with citations, freshness, and RAG built to hold up in production.

Tool and API actions

Agents that update the CRM, file the ticket, and reconcile the ledger, wired in through your APIs and MCP.

Data pipelines

Extraction, classification, and processing pipelines that run unattended at scale and feed the agents clean inputs.

Evals and human gates

Automated evals, policy guardrails, and approvals on signatures, submissions, and other risky actions before users see them.

Built into production, not bolted on

We deploy where your data lives: your cloud, your auth, your controls. The agent inherits your security posture instead of widening your attack surface, with traces and rollback from the first run.

  • Runs in your environment or ours
  • SSO, RBAC, and audit logging
  • No data retention you did not ask for
Deploy target
OpenAIOpenAIClaudeClaudeGeminiGeminiSalesforceSalesforceSnowflakeSnowflakePostgresPostgres
SSORBACAudit logCloud

You own the code, not a black box

A forward-deployed team builds alongside yours and hands over a system you fully control: clean codebase, evals, and a runbook. No lock-in, no vendor-only workflow trapped outside your stack.

  • Clean, documented codebase
  • Knowledge transfer built into delivery
  • Extend it without us
Handover state
handoff packageCode, runbook, evals, dashboard
owned by your team
Source repoRunbookEval suiteOwner training

Access your auth

Data your environment

Ops monitor or handoff

From pilot to production, the part most teams miss

Pilots are everywhere and production agents are rare. We design for that gap from day one: evals before users, guardrails on risky steps, and a named owner at launch, so what ships keeps working.

  • Evals that gate every release
  • Fallback and escalation paths
  • A runbook, not a hope
Release gate
  1. 01Eval suiteknown + edge casespass
  2. 02Policy checkguardrails enforcedpass
  3. 03Human fallbacklow-confidence routedhold
  4. 04Releaseshipped to prodlive

p95 latency 1.2s

eval pass 12/12

rollback ready

Model and stack agnostic
OpenAIClaudeGeminiMCPYour APIsSnowflakeSalesforcePostgres
FAQ

Questions buyers ask us.

What does custom AI development cover?+
It is the build and deployment of bespoke AI software, agents, copilots, retrieval systems, and data pipelines, integrated into your existing stack. Gaper takes it from workflow map through evals, sandbox, and a human-gated go-live your team owns.
How do you deploy what you build, and who owns it?+
We deploy where your data lives: your cloud, your auth, and your controls, with sandbox-first launches, traces, and rollback. You own the code: we hand over a documented codebase, evals, and a runbook, and can also run it under an SLA.
Which models and systems do you work with?+
We are model- and infrastructure-agnostic: OpenAI, Claude, Gemini, and open models, chosen per task, integrated through your APIs and MCP, and deployed across AWS, GCP, or Azure on Postgres, Snowflake, and your systems of record.
How long does a custom AI build take, and is it secure?+
A first working build can land in as little as 24 hours for a scoped use case; production timelines run in weeks depending on integration and governance. For regulated work we deploy inside your cloud with SSO, RBAC, PII redaction, and full audit logging.
See what operators from other companies think about AI Agents:
Learn more
Keep exploring
Ai Agent Development CompanyAi Agents For BusinessAi Automation Agency
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