AI for SaaS teams whose support queue grows faster than revenue.

The failed version of this project is a bot bolted onto the front of the queue that answers everything with confidence and annoys the people it cannot help. The version that works reads the ticket, pulls the account and billing context, and answers directly only when it can cite a policy or a record.

Everything else routes to a human with the context already attached, which is where the second saving hides: the tickets a person still handles take less time because the assembly work is done. Veyra's median first response fell from four hours to eight minutes and CSAT moved up slightly, from 4.4 to 4.5 out of five.

Thresholds are tunable and every routing decision is logged with its confidence and its evidence, so the support lead can see exactly why a ticket went where it went and move the line themselves.

Once deflection is running, the next hour drain is usually onboarding: chasing configuration details, checking that a workspace is actually set up, writing the same nudge email to the fortieth account this month, and spotting the customer who logged in twice and disappeared.

That work has the same structure as ticket triage. Signals live in three systems, the policy for what to do about them is written down somewhere, and the action is a message or a task. Slate Partners had the equivalent problem on the way in rather than after the sale, and their lead qualification now scores 41 inbound leads and routes nine of them in about four seconds.

Shipping an AI feature inside a SaaS product is mostly not prompt work. It is deciding what the feature refuses to do, what it shows as evidence, how a user corrects it, what gets logged, and what happens when the provider has an incident during your business hours.

We build the model layer behind one interface so the provider is swappable. Nordwind's claims agent shipped on a frontier model and was benchmarked against two alternatives; changing model is a config change plus an evaluation run. In a multi-tenant product that also means per-tenant controls: which customers get the feature, what data it may read, and whether their contract allows a third-party model at all.

An AI feature without an evaluation harness is a feature you cannot safely change. Veyra's agent and Nordwind's claims layer both shipped with a graded set of historical cases that runs on every change, so a prompt edit that improves one answer and breaks four is caught before it reaches a customer.

For Nordwind that suite is 1,200 historical claims. For a SaaS product it is usually a few hundred real tickets or documents with a known correct outcome, assembled during the audit week. It is unglamorous and it is the reason the second release is not a rollback.

Where does customer data go, is it used for training, can a tenant opt out, who can see the logs, and what happens on deletion. These questions arrive in the security review, and the honest time to answer them is while the thing is being designed.

The pattern we default to is your cloud account, your tenancy, zero-retention model endpoints, and logs that record the decision and its evidence rather than the customer payload. MedArc's rebuild came out of a review that started with the same list and finished with zero critical findings.

A production support agent with evaluations, logging and rollout is a $50,000 to $150,000 project and takes eight to twelve weeks. A single scoped LLM feature inside an existing product is smaller, usually $15,000 to $50,000. Automation of an internal workflow starts at $4,000.

Payback is worth calculating before you start, and it is usually simple arithmetic: tickets per month, minutes per ticket, loaded cost per hour. Veyra passed payback in month five on ticket cost alone, before counting the six hires they did not make.