AI agents that run in production not demos.
We design, build, and operate AI agents on Claude and custom MCP servers. Sales reps, concierges, search, and automation — shipped with real customers, kept running on retainer.
senior engineers·10 production systems · 9 verticals
Trusted by clients
Built with · Production stack
When you actually need an AI agent
[ Draft brief replace with 2–3 short paragraphs in your own voice. ]
• The difference between a chatbot bolted onto a FAQ and a real agent: it reads live data, decides, and takes an action inside the systems the client already runs.
• When an agent is worth it repetitive, judgment-based work tied to data that changes fast. Use your own concrete examples.
• When it is NOT worth it say honestly that a form or static content is sometimes the right answer. That honesty is a differentiator.
How we build them: Claude and custom MCP servers
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• Claude does the reasoning; custom MCP servers connect it to the client's real systems (database, booking engine, CRM). The separation is the point.
• Why it matters the agent never holds business logic or credentials; a narrow, audited tool surface; answers grounded in live data, not guesses.
• Regulated / money-handling angle — bring your microfinance, debt-recovery, and logistics experience FORWARD here. This is the hardest-to-fake proof you have.
• How you run it — guardrails on every action, logging on every decision, same engineers on retainer.
Agent patterns we ship
Six shapes of agent we have built and run in production. Most engagements combine two or three.
Sales & qualification agents
Qualify leads, answer product questions, and book demos against live inventory and pricing. In production as the Motago.ai AI sales rep.
Concierge & support agents
Handle bookings, changes, and customer questions across multiple providers in real time. Live as the Admiral Travel AI Concierge.
Smart search & retrieval
Intent-based search over your catalog or knowledge base — it understands what users mean, not just what they type.
Workflow automation
Replace manual operations with an agent in the loop: routing, data entry, reconciliation, follow-ups. Reliable, logged, reversible.
Analytics & monitoring agents
Agents that read incoming data and decide what matters — anomaly detection, triage, alerting. In production as NuReply email-threat analysis.
Guardrails & safety
Prompt-injection defense, action limits, audit logs, and human-in-the-loop checkpoints. Required, not optional, for regulated systems.
Agents running in production
Not slideware. Live systems we built and maintain — across SaaS, travel, iGaming, retail, and regulated operations.
Pricing
What an agent costs
Fixed-scope, with a negotiated cap. You know the maximum before we start.
Before any code. We map the use case, design the tool surface and guardrails, and hand you an architecture document with cost and timeline.
- Use-case and feasibility review
- MCP tool surface design
- Architecture doc + fixed quote
A complete agent built on Claude and custom MCP servers, with guardrails, logging, and deployment. Running with your real customers.
- Claude + custom MCP servers
- Guardrails, audit logs, human-in-the-loop
- Deployment + monitoring
- Fixed scope, capped price
AI features embedded into an existing product, plus the team that keeps them live. Same engineers who built it, on retainer with an SLA.
- Gen-AI features in your product
- Ongoing tuning + new tools
- SLA-backed maintenance
AI agents — frequently asked questions
Honest answers about how we engage and where we fit.
Have a task an agent could run?
A 30-minute discovery call to see if an AI agent fits — and to tell you honestly if it does not. No commitments.
Book a discovery call