Agents that finish real work.
Goal-directed AI that executes multi-step workflows — dialling leads, processing documents, answering customers — reliably enough to put your name on it.
The demo is easy. The 10,000th run is the product.
Anyone can wire a model to a tool and record a demo. The difference between that and a production agent is everything that happens when reality pushes back: ambiguous inputs, failed API calls, an angry customer, a compliance question. That is the part we engineer.
Our agents ship with guardrails, evaluation suites and full audit trails. Every action is logged, every edge case has a fallback, and a human can take over mid-task. We have run agents through more than a hundred thousand live calls — we know where they break, because we have already broken them.
Agents with a job description.
We build agents around a defined role with measurable output — not a chat window bolted onto your product.
Voice agents
Outbound and inbound calling that qualifies, books and routes through natural conversation — with live handoff to humans.
Workflow agents
Multi-step processes executed end-to-end: document intake, claims triage, order processing, follow-up sequences.
Retrieval-grounded assistants
Support and knowledge agents that answer from your own data — with citations, not improvisation.
Integration services
Agents wired into your CRM, ERP and ticketing via APIs and MCP — acting in your systems, not beside them.
Industry-specific copilots
Domain agents trained on your playbooks and constraints, from aviation compliance to food-service ops.
Evals & guardrails
Behavioural test suites, output constraints and monitoring that keep agents on-script as models and prompts evolve.
Autonomy, earned in stages.
We expand what an agent may do only as it proves itself — the same way you would promote a new hire.
01Define the job
One role, clear inputs, measurable output. We write the agent’s job description and success metric before any code.
02Human in the loop
First deployments run supervised — the agent proposes, a human approves. Trust is built on transcripts, not promises.
03Measure & harden
Eval suites run on every change. We chase down the 2% of runs that fail and engineer them out.
04Scale autonomy
Approved action classes go autonomous; sensitive ones keep approval gates. Full audit trail at every stage.
We have shipped this before.
A multi-tenant agent platform we built and operate — auto-dialling, qualifying and routing leads through natural conversation.
Lead Track AI
Multi-tenant SaaS that automates lead engagement with AI-powered voice agents — auto-dialling prospects, qualifying through conversation, and routing high-intent leads to sales with full context.
Chosen for the problem, not the résumé.
Orchestration, telephony and evaluation tooling chosen for reliability under load — not for the logo wall.
One team. Zero hand-offs.
Disciplines most often combined with AI agents — same architecture, same engineers, no integration tax.
Questions, answered.
The things buyers of AI agents ask us most. Anything else — put it in a brief, a senior engineer replies within a business day.
Put it in a brief. A senior engineer — not a sales rep — replies within one business day.
Q.01How do you stop an agent from going off-script?
Constrained outputs, allow-listed actions, and behavioural eval suites that run on every prompt or model change. Sensitive actions sit behind approval gates, and every run is logged so you can audit exactly what the agent did and why.
Q.02Can agents work inside our existing systems?
Yes — that is most of the value. We integrate via your APIs and the Model Context Protocol, so agents read and write to your CRM, ERP or ticketing system under the same permissions model as a human operator.
Q.03Voice agents sound robotic. Do yours?
Modern speech models hold natural, interruptible conversation with sub-second latency. Ours have completed 100K+ live calls; we will play you real recordings — unscripted, not cherry-picked — before you commit.
Q.04What happens when the agent gets stuck?
It escalates. Every agent we ship has explicit failure behaviour: hand off to a human with full context, queue for review, or roll back the task. Silent failure is an engineering defect, and we treat it as one.
Q.05How long does an agent take to build?
A single-purpose internal agent: 4–6 weeks. A customer-facing agent with multiple tools, approval flows, and observability: 10–16 weeks. We always start with a paid 1-week scoping spike.
Q.06Single agent or multi-agent — when do you split it up?
Default to a single agent with a clear tool set; multi-agent adds coordination overhead and failure surface. We split into planner/executor/critic roles only when a single context window can't hold the task, when sub-tasks need genuinely different tools or models, or when one role's output must be independently reviewed before another acts.
Q.07How do you stop an agent from looping forever or burning the budget?
Hard step limits, a per-task token and dollar budget enforced at the orchestration layer, and loop detection that catches when the agent repeats the same tool call with the same arguments. When a budget trips, the agent escalates to a human rather than failing silently — we use LangGraph or Temporal so the run is checkpointed and resumable.
Q.08How do you test an agent before it touches production systems?
We run it against sandboxed or mocked tools with a suite of scripted scenarios covering happy paths and known failure modes, then replay real (anonymised) traffic in shadow mode. The behavioural eval suite runs on every deploy and blocks merges that regress on safety or completion-rate benchmarks.
Have a workflow an agent
should own?
Describe the job — qualifying, triaging, answering, processing. We will reply within one business day with an honest read on whether an agent can actually do it.