AI Agents Development — Voice, Sales, Support & Ops Agents Built to Ship

Most AI agents you see online are demos. Pretty, fast, and broken the moment a real customer asks a slightly off-script question. We build the other kind — production AI agents that handle real customer conversations, qualify real leads, resolve real tickets, and run real renewal checklists, with the evals, observability, and guardrails to keep them safe in production.

The problem we kill

Your team is hiring tier-1 support reps instead of senior engineers. Your SDRs spend 80% of their week on research and personalisation instead of conversations. Your CS team spends Mondays running renewal checklists. Your AMs are paged at 2 AM for a card decline that an agent could have flagged at 5 PM. Every one of these tasks follows a repeatable pattern — and every one of them is the wrong work for a human to own.

You don't need another chatbot. You need an agent that can listen, reason, call the right tools, hand off to a human when stakes are high, and prove its work after the fact.

Agents we build

Voice agents

Phone-grade voice agents with sub-second latency, real-time transcription, natural turn-taking, and clean hand-off to a human on edge cases. Built on Deepgram, Cartesia, ElevenLabs, or OpenAI Realtime, depending on cost and quality requirements. Wired into your CRM, ticketing system, and knowledge base.

Support and triage agents

First-touch resolution on inbound tickets, with safe escalation paths and SLA-aware routing. Integrates with Zendesk, Intercom, Front, HubSpot Service, or your own ticketing stack. Includes confidence scoring so the agent knows when to stop and hand off.

Sales and outbound agents

Research a prospect, draft personalised outreach, schedule meetings, log to CRM. Includes account-level context, signal monitoring (funding rounds, hiring, product launches), and reply-handling. Integrates with HubSpot, Salesforce, Outreach, Apollo, and Gmail.

Ops and renewal agents

The repetitive checklist work — renewals, reconciliations, dunning, lead routing, refund decisions. Each step gets explicit, auditable logic. High-risk actions go to a human queue before they fire.

What you get

• Custom-built agents shipped to your stack (Slack, HubSpot, Salesforce, Zendesk, Stripe, your own APIs).
• Eval suites that gate every prompt change before it deploys.
• Cost guardrails — per-user budgets, prompt caching, model routing, batch APIs.
• Observability dashboards (Langfuse / Helicone / OpenTelemetry) with prompt versioning and drift detection.
• Human-in-the-loop checkpoints on high-stakes actions (refunds, escalations, contract changes).
• Production deploy with runbook, alerting, and on-call rotation.

Tech stack

We work with the best provider for each job, not just one. Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-source models via Together, Replicate, Groq, or self-hosted vLLM. Voice via Deepgram (STT), Cartesia / ElevenLabs / OpenAI Realtime (TTS), and Twilio / Vonage for telephony. Vector retrieval via pgvector, Pinecone, or Qdrant.

Industries we serve

SaaS companies (B2B, vertical, fintech, healthtech, legaltech), marketplaces, e-commerce, professional services, and enterprise IT teams across the US, UK, Germany, France, Netherlands, Ireland, and Sweden.

How we engage

Discovery sprint (2 weeks) to define the agent's job, draft eval criteria, scope cost and latency budgets, and pick the right model. Build phase (4–16 weeks) with weekly demos and live eval scores. Launch with prompt versioning, cost dashboards, drift detection, and an incident runbook. Optional managed support after launch.

Frequently asked questions

How long does it take to ship an AI agent?

A focused single-task agent ships in 4–6 weeks. A multi-agent system with voice, tools, and a managed knowledge base runs 10–16 weeks. We give a fixed proposal after the 2-week discovery sprint.

How do you keep an AI agent from hallucinating in production?

RAG with citations, structured outputs (JSON schema or function calling), confidence scoring, output validation, and human-in-the-loop checkpoints for high-stakes flows. Designed to fail safely, not silently.

Will the agent be tied to one model provider?

No — we build provider-agnostic. Swap Claude for GPT for Gemini without rewriting your app. Important the day one provider has an outage or a price hike.

How is this different from a Voiceflow / Botpress build?

Those tools are great for static decision trees. Real AI agents need reasoning, dynamic tool use, eval-driven prompt iteration, and proper observability. That's an engineering build, not a no-code flow.