Capability 01
AI agents
Agents that carry out multi-step work across your tools — reading, deciding and acting, with a human threshold built in.
ExploreThe work thatruns itself.
AI agents, chatbots and workflow automation wired into the systems you already run — handling the repetitive work and handing you the rest with context attached.
Live · Customer email receivedStep 1/5
72%
Handled20s
First reply28%
EscalatedRead & classify
Intent, urgency and language detected from the message body.
72%
Of routine queries handled end to end
24/7
Without a shift roster
4 wks
Typical first automation live
01 / Overview
Automation only helps if it knows when to stop.
Automation only helps if it knows when to stop.
The failure mode of business AI is not that it cannot answer. It is that it answers confidently when it should have escalated, and nobody finds out until a customer does.
We build automations that are grounded in your own content and your own records, with an explicit confidence threshold. Above it, the agent acts. Below it, the work goes to a person with the context already gathered, which is faster than starting from scratch anyway.
Everything is logged: what triggered a run, what the model saw, what it decided, and what a human changed afterwards. That log is what lets you widen the automation safely over time instead of guessing.
Highlights
01AI Agents
02Grounded Answers
03Human Handoff
04Workflow Automation
05Predictive Analytics
02 / Capabilities
Three places to start.
03 / Benefits
What your team gains.
Routine queries answered in seconds, around the clock.
Escalations arrive with the context already gathered.
Answers grounded in your content, so they are checkable.
Every run logged — trigger, decision and human correction.
Staff time moved off re-keying and onto the exceptions.
Automations widened gradually, on evidence rather than hope.
04 / Use cases
Where it earns its keep.
01
Customer support
02
Lead response
03
Document processing
04
Internal helpdesk
05
Reporting
06
Forecasting
05 / Process
How we roll it out.
Step 01
Find the repetitive work
We look at volume and handling time to find the task where automation actually pays, rather than the one that demos well.
Step 02
Build with a threshold
The agent is grounded in your content and ships with an explicit confidence cutoff and a human handoff path from day one.
Step 03
Measure and widen
Run logs show where it was right, wrong or unsure. We widen scope on that evidence and tighten where it slipped.
Stack
Claude APIPythonNode.jsn8nLangChainPostgreSQLpgvectorTwilio & WhatsAppDocker
What you receive
Workflow map with volume and time-saved estimates
Deployed agents or automations with a confidence threshold
Knowledge base grounded in your own content
Human handoff path and escalation rules
Run logging and a quality dashboard
Integration documentation and source code handover
06 / Case study
Proof, not promises.
72%
Tier-one support, handled
A support desk was spending most of its day on the same dozen questions. An agent grounded in their existing help content now closes around 72% of incoming queries end to end, and escalates the rest with the account history already attached.
07 / FAQs
Common questions.
Answers are grounded in your own documents and records rather than generated from general knowledge, and anything below the confidence threshold is routed to a person instead of answered. Every run is logged so you can check.
Ready to move forward
What are you doing twice?
Tell us the task your team repeats most. We'll tell you honestly whether automating it is worth the money.