AGENT_01
Aria, Lead Qualifier
Reads every inbound enquiry, scores against your ICP, routes to the right rep. Claude 3.5 + Supabase.
( _ai-agents-and-automation )
AI agents that actually run inside your business.
Not chatbots. Not ChatGPT wrappers. Custom Claude and GPT-powered agents wired into your CRM, ad accounts, and ops layer, replacing tasks, not just summarising them.
AI agents & automation · live work
What we typically find
The failure modes we audit out first.
4 patterns we see almost every time we look at an existing account. The audit pulls them apart before we touch anything.
Failure mode 01
ChatGPT wrappers sold as 'AI agents'
Most agencies sell a Zapier flow with a GPT step. That's not an agent. We build agents that own a workflow end-to-end.
Failure mode 02
No observability, no eval loop
If you can't see every model call and score its output, you're flying blind. Langfuse + a defined success metric is non-negotiable.
Failure mode 03
Locked into one vendor
Vendor lock kills your bargaining power and your stack-flexibility. We build model-agnostic by default.
Failure mode 04
PII flowing through unvetted prompts
Patient data, financial data, contact data: most agency builds leak. We redact at the boundary.
Visualised
What an inbound enquiry actually goes through.
Lead lands. Aria scores. Echo books or hands off. Pivot nurtures. Ledger reports. Verity follows up. Every step writes back to your CRM.
Inbound enquiry, agent pipeline
01
Lead in
form / call / DM
02
Aria
qualifier · claude
03
CRM
routed to rep
04
Echo
books / texts
05
Pivot
nurture
↳ Every step is observable. Every prompt is in Git. The whole pipeline runs in your accounts.
Ledger · daily agent summary (sample)live · 07:00 GST
ROAS
4.2
↑ +0.6 vs 7d
CPL
$38
↓ -41%
Booked jobs
89
↑ +22 vs 7d
30-day spend efficiencytrending
The stack
What we ship, role by role.
Every layer named, scoped, and owned. No black boxes.
AGENT_02
Echo, Missed-call
Texts back within 9 seconds of a missed call. Books or hands off. Claude + Twilio + WhatsApp.
AGENT_03
Ledger, Reporting
Daily summary across ad accounts, CRM and call data. Slack at 7am sharp. Claude + Looker.
AGENT_04
Pivot, Follow-up
60 to 180 day nurture sequences with branching logic per buyer signal. GPT-4o + HubSpot.
AGENT_05
Verity, Reviewer
Post-job review request, sentiment-watch and reputation defence in one loop.
AGENT_06
Forge, Content
Weekly insights drafts, briefs and SOPs from your brand voice and data. Claude + MDX + Sanity.
How we ship it
4 steps. Each one auditable.
Step 01, discovery
Workflow mapping
We sit with your team and trace every task an agent could own. Pick the highest-leverage ones first.
Step 02, schema
Define the contract
Input schema, output schema, success metric, kill-switch criteria. All written before we touch a model.
Step 03, build
Wire it in
Prompts versioned in Git. Langfuse for observability. Deployed to your environment, not a black box.
Step 04, monitor
Eval weekly
Every agent gets a weekly eval against its success metric. Drift gets caught early.
Average client outcome
Average ops time saved per agent, per week, across deployed agents.
Aggregate across active engagements, 2025
12h
FAQ, ai agents & automation
The honest answers.
- 01
Who owns the agent code and prompts?
You do. Day one. Every prompt, every Supabase row, every workflow lives in your repo and your accounts. We sign IP transfer on contract.
- 02
What about hallucinations and quality control?
Every agent has a Langfuse dashboard tracking every call, latency, cost and output. We define a success metric per agent and review weekly. Kill-switch on every agent if it goes off the rails.
- 03
How do you handle data security and PII?
Models hosted via Bedrock or direct vendor API, no third-party prompt processors. PII redaction layer in front of every agent that handles patient or financial data. SOC 2 alignment available on request.
- 04
Can the agents act, or do they just summarise?
They act. The whole point. Lead qualifier writes back into your CRM. Booking agent writes to Cal.com. Reporting agent posts to Slack. If an agent only summarises, it's not solving a workflow.
- 05
What if a model gets deprecated?
Every agent is built model-agnostic with a config switch. Claude 3.5 to 4 to whatever's next: swap the config, re-eval, ship. You don't get locked into a vendor.
AI automation · taking clients
Custom agents. Built in your stack.
A 7-day audit identifies which workflows are agent-ready. Often the answer is more than you'd guess.