We build production AI agents that actually do the work. Not chatbots.
Cloptim is an agent-native AI agency. We build agents for customer service, sales, voice and internal knowledge, ship them to production and keep improving them on retainer.
Or readAn evals-first approach to shipping AI agents →1import { agent, tool, abstain } from "@cloptim/runtime";2 3export const customerService = agent({4 model: "claude-sonnet-4",5 retrieval: helpCenter,6 abstain: abstain.below(0.55), // never makes things up7 tools: [8 tool("refund", refundIssue, { approval: true }),9 tool("order.lookup", orderLookup),10 ],11 evals: tier1Suite, // runs on every PR12});Stack
How we got here
We came to AI from cloud cost engineering. The waste was never where we were told.
Years working inside engineering teams taught us where the real waste was. Not unused EC2 or oversized warehouses. It was repeatable human work that should have been automated years ago. AI agents finally make that work automatable, when they're built properly: with retrieval, tool use and real evals.
Read the full story →Industry benchmarks
What “good” looks like in production.
Target outcomes typical of well-built agents in this class. Specific numbers vary by your domain and your data quality. We model yours during the discovery sprint and put measurable targets in the proposal.
Solutions
Productized engagements with prices on the page.
AI Customer Service Agent
→Handles 60-80% of tier-1 inbound. Escalates the rest cleanly.
AI Sales Outreach System
→Researches, qualifies, drafts and follows up at scale. Without sounding like a bot.
AI Voice Receptionist
→Answers every call. Books appointments. Routes urgent issues. 24/7.
Internal Knowledge Agent
→Your company's collective brain, asked in plain English.
Custom Agent Build
→When your workflow doesn't fit a productized box, we build a bespoke agent for it.
Architecture
An agent isn't a model. It's a system around the model.
The model picks an action. The system around it delivers that action safely, with observability and proper guardrails. Most projects don't fail at model selection. They fail at everything below it.
Permission-aware document and ticket lookup. Citations on every answer.
Typed function calls with allowlists, dry-runs and human approval for destructive actions.
Continuous quality measurement against production traffic. Regressions caught before customers see them.
Trace dashboards explaining why the agent answered. Not a black box.
1import { agent, tool } from "@cloptim/runtime";2 3export const customerService = agent({4 model: "claude-sonnet-4",5 retrieval: helpCenter,6 tools: [7 tool("refund.issue", refundIssue, { /* requires approval */ }),8 tool("order.lookup", orderLookup),9 tool("escalate", escalateToHuman),10 ],11 evals: tier1Suite, // runs on every PR12});Process
No surprises. Weekly demos. Production by the date on the proposal.
Every engagement runs the same loop. Discovery, design, build, eval, ship.
- Week 1
Discovery sprint
Workflow analysis, success metrics, ROI model. You keep the analysis even if you don't proceed.
- Week 2
Design
Agent architecture, eval strategy, integration plan. Locked scope, fixed price.
- Weeks 3-6
Build
Incremental shipping. Weekly demo. The agent improves visibly every Friday.
- Week 7
Eval & rollout
Measure against the success model. Tune. Deploy. Hand off or retain us for ops.
Insights
What we’ve written about shipping agents.
Why most AI agents fail in production and what we do about it
The model isn't usually the problem. The agent system around it almost always is. Here's the failure taxonomy we keep meeting in the wild, with the engineering moves that prevent each one.
An evals-first approach to shipping AI agents
The single highest-leverage decision in building a production agent isn't choosing a model. It's deciding what 'good' looks like and measuring it from day one.
Have a workflow that should be an agent?
Book a 20-minute call. We'll tell you what's feasible, what's not and what we'd build.