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AI agent development company

AI agent development for production agents.

Gaper builds and deploys supervised AI agents from your real workflows: discovery, workflow map, data layer, connectors, evals, sandbox, and human-gated go-live under one accountable team.

Book a free AI assessmentWhat is an AI agent?
Map the workflowBuild the supervised agentSandbox, verify, go live
gaper · agent runtime
$ gaper deploy agent --to production
✓ plan ……………… 4 steps
✓ retrieve …… 1,240 docs grounded
✓ tool ………… salesforce.update_record
✓ eval ………… 12/12 checks passed
● live · p95 1.2s · 0 errors
● in productionowned by your team
In one sentence

An AI agent development company designs, builds, integrates, and runs production AI agents, software that plans and takes multi-step actions toward a goal, inside your existing systems, deployed in your stack and owned by your team.

ProductionBuilt to ship, not demo
Model-agnostic
Sandbox firstVerified before launch
You own itCode, evals, runbook
Why this matters

A proof-of-concept is easy. An agent that survives real data, real edge cases, and real users in production is the hard part, and it is the only part that matters.

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.

Documented workflow agents

Agents scoped from your SOPs, templates, spreadsheets, portals, and inboxes, then turned into repeatable production runs.

Retrieval & knowledge

Grounded answers over your documents and data, with citations and freshness, RAG done so it holds up in production.

Tool & API actions

Agents that take action: update the CRM, file the ticket, reconcile the ledger, wired in via your APIs and MCP.

Multi-agent orchestration

Specialist agents that hand off to one another with a supervisor pattern, so complex jobs get decomposed and done.

Evals & human gates

Automated evals, policy guardrails, and approvals for signatures, submissions, policy changes, and other risky actions.

Observability

Traces, cost, and quality dashboards so you can see exactly what the agent did and why.

Security & compliance

SSO, RBAC, PII redaction, and audit trails, deployed inside your cloud when regulation demands it.

Governance & handover

Access control and documentation so your team can own, extend, and trust it.

Built into production, not bolted on

We deploy where your data lives: your cloud, your auth, and your controls. The agent inherits your security posture instead of widening your attack surface.

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

You own the outcome, and the code

A forward-deployed team works alongside yours and hands over a system you fully control. No black box, 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

Industry research keeps pointing to the same gap: agent pilots are everywhere, production agents are rare. We design for that gap from day one: evals before users, guardrails on risky steps, and a real owner at launch.

  • Evals that gate every release
  • Fallback and escalation paths
  • A runbook, not a hope
Release gate
Eval suitePolicy checkHuman fallbackRelease

p95 latency 1.2s

eval pass 12/12

rollback ready

Model and stack agnostic
OpenAIClaudeGeminiMCPLangGraphYour APIsPostgresSnowflakeSalesforceZendesk
FAQ

Questions buyers ask us.

What does an AI agent development company actually do?+
It designs, builds, integrates, and deploys AI agents, software that uses an LLM to plan and take multi-step actions toward a goal, into your real workflows. Gaper covers the full path from strategy to a production system your team owns.
How is this different from buying an AI platform?+
Platforms give you tooling to build it yourself. We build and deploy the agent for you, wired into your specific systems, and hand over something you own. When an off-the-shelf product fits, we’ll tell you, most real workflows need custom integration.
How do you scope an AI agent engagement?+
We start with a free assessment of one high-leverage workflow and make an honest build-vs-buy call. From there we scope the smallest production release: discovery, workflow map, the data and connector layer, evals, sandbox, and a human-gated go-live. Scope and timeline depend on integration and governance, which we agree up front.
How long does it take to build an AI agent?+
A first working build can land in as little as 24 hours for a scoped use case. Production timelines depend on integration and governance needs, which we scope up front, typically weeks, not quarters.
Which models and stacks do you work with?+
We’re model- and infrastructure-agnostic: OpenAI, Claude, Gemini, and open models, deployed in your cloud, integrated via your APIs and MCP.
Can you deploy inside our cloud for compliance?+
Yes. For regulated workloads we deploy in your cloud with your security controls, SSO, RBAC, PII redaction, and full audit logging, so data never leaves your boundary.
What happens after launch, who maintains it?+
You own the code and documentation. We can hand it fully to your team, or run it under an SLA with observability, evals, and ongoing improvement, your call.
Keep exploring
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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