Most “agents” break on the second turn. We ship the parts that make them production-grade — tool-use, retrieval, guardrails, evals and observability — so they resolve tickets, book meetings and run workflows you can actually leave running.
Start with the one that hurts most. Each links to how we scope, build and ship it.
Tool-use, function-calling, orchestration
Bespoke agents that reason over your data and take real actions in your stack via tool-use and function-calling.
How we build itDeflect tickets, keep the handoff clean
Support agents that resolve the repetitive tickets from your own docs and escalate the rest with full context.
How we build itQualify, enrich, follow up — 24/7
Agents that qualify inbound, enrich leads, draft personalised follow-ups and book meetings straight into your calendar.
How we build itAnswers with citations, not hallucinations
Retrieval-augmented assistants over your knowledge base that answer with sources and admit when they don’t know.
How we build itReal-time voice, low latency
Low-latency voice agents for inbound and outbound calls — booking, triage, qualification and support.
How we build itMulti-step automations across your tools
Agents and pipelines that run multi-step work across your tools — triage, data entry, reporting, back-office ops.
How we build itWire LLMs into your product & stack
Integrate LLMs and tool-use into your product or internal tools — including MCP servers that expose your systems to agents.
How we build itGuardrails, evals, observability
The safety layer: eval suites, guardrails, tracing and cost/latency monitoring that keep agents trustworthy in production.
How we build itNot a prompt in a box — a loop with tools, grounding and a safety layer around it.
Drag the sliders to your reality. This is the conversation we start with — then we scope the real number and the guardrails against your actual volume.
Rough estimate at 60% automation. We scope the real number — and the guardrails — against your actual volume before you commit a dollar.
Typed tool schemas and MCP servers so the agent can safely act in your systems.
Tuned chunking, hybrid search and re-ranking so answers are grounded and cited.
Input/output checks, PII and jailbreak filters, confidence-gated human handoff.
Task + regression suites so a prompt change can’t silently break behaviour.
Full request tracing plus cost, latency and quality dashboards.
Claude, GPT or open models — chosen per task against cost and latency.
Questions engineers ask
The things technical buyers check first.
No. A chatbot answers questions; an agent takes actions — it calls your tools, reads and writes to your systems, and decides when to hand off to a human. We build the second thing, with the guardrails that make it safe to leave running.
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