AI service · AI Agents
AI Agent Development
We build production AI agents that do jobs, not demos — agents that read your CRM, draft the follow-up, file the ticket, reconcile the invoice, and hand off to a human exactly when they should. Every agent ships with evaluation suites, guardrails, and full tracing so you can trust what it does unattended.
Free scoping call · Clear ROI plan before any commitment
The challenge
Every vendor now sells 'agents,' but most are chat wrappers that stall the moment a task needs three steps, a lookup in another system, or a judgment call. Teams pilot them, watch them fail on edge cases nobody scripted, and quietly go back to manual work — having spent budget without retiring a single hour of labor.
How we solve it
We design agents around a specific workflow with a measurable outcome: the tools they may call, the data they may touch, the decisions they may make alone, and the ones that route to a person. Then we prove reliability the way engineers do — with automated evals against real historical cases, staged rollouts, and dashboards that show completion rate, escalation rate, and cost per task.
Capabilities
What we deliver
The building blocks of a production-grade ai agents engagement.
Task-completing autonomous agents
Agents that own a workflow end to end — lead qualification, order processing, claims triage, report generation — calling your APIs and finishing with an auditable result.
Human-in-the-loop workflows
Approval gates, confidence thresholds, and clean escalation paths so agents handle the routine 80% and route the risky 20% to your team with full context.
Multi-agent orchestration
Coordinated agent teams — a researcher, a drafter, a reviewer — with a supervisor pattern that keeps long-running work on track and recoverable.
Tool use & MCP integration
Well-typed tool interfaces to your CRM, ERP, ticketing, and databases, built on Model Context Protocol so new capabilities plug in without rework.
Agent memory & state
Durable memory and checkpointed state so agents survive restarts, remember customer history, and improve from prior runs instead of starting cold.
Evaluation & observability
Eval harnesses run on every change, plus per-step tracing of decisions, tool calls, and cost — so you see exactly why an agent did what it did.
How we work
A clear path to production
Five stages, each with visible output — you're never waiting on a black box.
Discovery & scoping
We map the problem, success metrics, constraints, and existing systems before writing code. You leave with a clear scope, timeline, and a fixed view of what 'done' means.
Architecture & design
We design the system end to end — data model, integrations, security, and a path to scale — and validate it against your real workloads, not a demo.
Iterative delivery
We ship in short, reviewable increments. You see working software every sprint, give feedback early, and never wait months to find out it missed the mark.
Hardening & launch
Testing, observability, performance, and security are built in, not bolted on. We launch with monitoring in place and a rollback plan ready.
Support & iteration
After launch we stay on — measuring outcomes, fixing fast, and iterating on what the data tells us actually moves the metric.
Representative stack
- Claude (Anthropic)
- OpenAI
- LangGraph
- Model Context Protocol (MCP)
- Temporal (durable execution)
- TypeScript / Python
Where it applies
AI Agents in the industries we serve
Vertical context changes what good looks like — see how this capability lands in your space.
Fintech
Payments, lending, and financial platforms built for security, reliability, and compliance.
Explore FintechLogistics
Tracking, routing, and warehouse software with real-time data and AI-driven optimization.
Explore LogisticsE-commerce
High-performance commerce, headless storefronts, and AI personalization that lift conversion.
Explore E-commerceAnswers
Frequently asked questions
What can an AI agent actually do for my business?
Anything that follows a describable process across your systems: qualify and route inbound leads, process orders and refunds, triage support tickets, chase unpaid invoices, compile research briefs, or keep records synced between tools. The best first agent targets one high-volume workflow with a clear definition of done.
How is an AI agent different from a chatbot or RPA bot?
A chatbot answers questions; an RPA bot replays fixed clicks and breaks when the screen changes. An agent reasons about a goal, chooses which tools to call, adapts to variations, and completes multi-step work — with human approval wherever you set the line.
How do you stop an agent from taking a wrong action?
Defense in depth: agents get least-privilege access to scoped tools, destructive actions require confirmation or human sign-off, outputs are validated against schemas, and every run is traced. We also test agents against a library of adversarial and edge cases before they touch production.
How long does it take to ship a production agent?
A focused single-workflow agent typically reaches supervised production in 6–10 weeks, including integration and evaluation. Multi-agent systems or deep ERP integrations take longer; we scope that precisely in discovery.
What does an AI agent cost to run?
Run cost is usually cents to a few dollars per completed task depending on complexity and model choice. We instrument cost per task from day one, route simple steps to cheaper models, and cache aggressively — so you can compare agent cost directly against the labor it replaces.
Keep exploring
Related AI services
Ready to build with AI Agents?
Book a free consultation and we'll map the fastest path to a working system — with the metric that proves it.