Conversational AI

AI Support Agent

A support agent that resolves the majority of tickets autonomously, escalating the rest with full context.

PythonFastAPIOpenAIpgvectorRedis

At a glance

What this shipped

The numbers that mattered to the client — measured before and after.

60%
Tickets auto-resolved
Seconds
First response
Auto-summary
Escalation context
Cited
Answer grounding

The problem

What we were called in to fix

A growing SaaS team was drowning in repetitive tickets — password resets, how-tos, billing questions — while genuinely hard issues waited in the same queue.

They'd tried a canned chatbot that frustrated customers and deflected nothing. They needed something that actually resolved issues and knew when to hand off to a human.

Our approach

How we actually built it

No magic — just the right architectural calls in the right order.

We grounded the agent in their real help docs and past resolved tickets via retrieval, so answers cite current, real information instead of hallucinating.

A confidence and sentiment gate decides in real time whether to answer, ask a clarifying question, or escalate to a human with a full summary attached.

Every conversation is logged and evaluated, so we could measure resolution rate and tighten prompts weekly instead of guessing.

Guardrails and per-action limits stop the agent making changes it shouldn't and keep costs predictable.

The outcome

What changed for the client

Around 60% of incoming tickets are resolved fully autonomously, with no human touch.

Escalations arrive pre-summarized with sentiment flagged, so agents start halfway to a solution.

Median first response dropped from hours to seconds for the deflected majority.

Tech stack

Every meaningful piece

PythonFastAPIOpenAIpgvectorPostgreSQLRedisCelery

We don't do generic case-study writeups. Want the unredacted version with names, screenshots and architecture diagrams? We share those on a call.

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