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Service and operations
Support that answers from your own record and hands off with the context.
An AI customer service agent answers from your documentation and account data, citing the source. It acts inside your limits and hands a person only what falls
Who it is for
Built for the team that does this by hand.
SaaS and subscription businesses where most tickets are the same
Service businesses fielding booking, billing and status requests
Public services answering the same eligibility and status questions
What it does
Customer support agents, by the job.
Answers with citations
Every answer points to the article, policy or account record it came from, so a customer or an auditor can check it instead of
Takes bounded actions
Status lookups, address changes, plan switches within limits, appointment moves: the actions your systems expose and you approve
Escalates with the thread
Refunds or cancellations beyond your limits, complaints and anything it cannot verify go to a person with the conversation, the account and a suggested
Measures what changed
Resolution rate, handoff rate, time to first answer and customer rating, reported beside the pre-agent baseline rather than in place
How it is built
The build, in order.
Every system here is scoped as one bounded workflow with a written success metric and a kill threshold agreed before the build. The steps below are the ones specific to
Read the ticket history
The top intents by volume and by cost, which ones have a written answer, and which ones a person must always handle. The scope is the list that survives
Build the knowledge base
What the agent may answer from, what it may not, and what happens when the corpus is silent. A question with no source gets a handoff, not
Shadow on live tickets
The agent drafts, your team sends. Every edit your team makes to a draft is read, and the disagreements tune it before it speaks to
Go live on its own
The agent answers and acts within your limits, refunds and cancellations included, and checks every action against them. What falls outside goes to a person, and each resolved handoff is added to what it can answer
Where this stands
What we run today.
The harness is the one our own AI Operations Agent runs on: it answers the team in Slack, runs scheduled jobs, and is held to a standard of knowing its own activity without a gap. Our public MCP server lets an AI assistant query our business the way a support agent would query yours. We have not published a customer-facing support deployment yet, so the first one is priced as a pilot like every other build on
Governance
How it checks its own work.
- Refunds, cancellations and account changes beyond written limits always route to a person
- Every answer carries its source, and an answer with no source is not sent
- Conversation logs are retained under your policy and never used to train a shared model
The rest of the posture is published on trust and security: client-granted access you can revoke, credentials in managed stores, an AI governance summary aligned to the NIST AI Risk Management Framework, our subprocessors, and a DPA on request. We do not hold SOC 2 and we say
Price
Published, not gated.
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month. Every AI build starts with a $7,500 AI Opportunity Audit over two weeks that is credited toward the build if you proceed, and a pilot is one bounded workflow on your real data over 30 to 90 days with a written success metric and a
The full price list is on the pricing page, and the cost drivers, with the questions that expose a padded quote, are in how much AI agents cost in 2026.
Questions
Customer support agents, answered.
What is an AI customer service agent?
How much does an AI customer service agent cost?
Can it work inside Zendesk, Intercom or HubSpot Service Hub?
Related
Where this connects.
AI workflow automation · Internal knowledge assistants · HubSpot and
The other systems in the catalogue: lead qualification agents, outbound sales agents, document processing agents, internal knowledge assistants, ad optimization systems, client reporting automation, content engines, computer vision systems, mcp servers and agent-callable surfaces, always-on operations agents.
Teams we've
Clients include 3M, Jiffy Lube, Goodwill, Western University, Hino Motors, Lundin Mining, Extendicare, Calian, Wabtec, ARGO, and
Kleo
Goodwill
GameGo
Hino Motors
Sad Panda Studios
StoryNest
Blush Blush
Stoic Forge
Beyond Score
MyPetHealth
TaskHive
Sincere Notes
Voices in my Head
Black Top Cabs
BeNow Trader
Crystalyze
24/7 AI Health
Aero Imagery
RepairFind
Prepr
Guiding Light
Freestyle Dance Studio
First Step Mobility
Noa Coach
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000
Real ranges, published. Ad spend stays in your
Where this fits: see the Strataigize Growth System, the ladder every engagement
Talk to the build team about customer support agents
Name the workflow you want it to run and we reply within 24 hours with whether it is a fit, the bounded scope we would propose, and the
Start with one workflow.
A free 30-minute consult with the senior team names the workflow this system should run first and what it should cost, whether you build it with us
Canadian and looking at funding? How the BDC LIFT program works. Public sector? How we build for government.
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