Support is one of the lowest-risk, highest-ROI agent deployments available. The data is already structured in knowledge base articles and past tickets, the metric is unambiguous at the deflection rate, and customers prefer self-serve when it actually works.
Who this is for
SMB to mid-market SaaS, fintech, and D2C teams processing 500 or more tickets per month with a support team that cannot keep up with volume growth.
What you get
- A RAG customer support agent grounded in your knowledge base and ticket history, so answers trace directly to your documentation.
- Brand-voice prompt tuning so responses sound like your team wrote them.
- An escalation path to a human agent with a structured handoff payload, including conversation history, user intent, and any account data already retrieved.
- Multi-channel deployment across chat widget, email, and in-app surfaces.
- A deflection-rate dashboard so you can measure the return on investment from day one.
How we work on this
We audit your support data, clean the knowledge base where needed, build and tune the agent, run an A/B test against your current flow, and cut over when deflection targets are met.
Tech stack
My AskAI for the fastest setup. Intercom Fin or Zendesk AI if you are already on their stack. Custom RAG with Pinecone and Cohere rerank when you need full ownership of the retrieval layer.
When this is the wrong choice
If your support load is dominated by genuinely novel issues that require expert judgment on every ticket, an agent will deflect under 20% of volume. We measure first and tell you before we build.
Pricing
$3,500 to $8,000 for a hosted setup with My AskAI or Intercom Fin, including tuning. $8,000 to $25,000 for a custom RAG implementation with full code ownership. Monthly operating costs for the custom RAG stack run approximately $200 to $1,000 depending on ticket volume, billed at actual cost.
FAQ
How accurate is the agent? We build a golden-example eval set from your real ticket history before writing a line of code. The agent does not ship until it hits the accuracy target we agreed on upfront, typically 90 to 95% on the eval set.
How do you prevent hallucinations? Every answer is grounded in retrieved documents. The agent cites sources, and the system prompt instructs it to say “I do not have information about that” rather than guess. We validate this behavior against the eval set.
How does escalation work? When the agent cannot answer or detects frustration, it hands off to a human with a structured payload: the conversation history, the user’s intent, and any order or account data it has already retrieved. The human agent does not start from scratch.
Can you train on our past tickets? Yes. Past resolved tickets are one of the best training signals available. We use them to build the golden-example eval set and to expand the knowledge base with answers that are not yet documented.
What is the typical ROI? Deflection rates of 60 to 70% are common in the first month for SaaS teams with well-structured knowledge bases. At a blended $8 per human-handled ticket and 1,000 tickets per month, that is $4,800 to $5,600 per month in saved labor against a one-time build cost of $3,500 to $8,000.