Gisti
Customer feedback to actions
36 followers
Customer feedback to actions
36 followers
Today's Launch: Gisti ingests feedback across all customer channels, synthesizes it into prioritized, evidence-backed opportunities, and lets product teams interact with an AI agent to explore, validate, and act. Future: Gisti.ai will be a fleet of Product & Customer Ops AI agents that analyze interactions, identify churn risks, and auto-route actions: code fixes to Eng, insights to PMs, and tasks to Ops.
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Hi all — shubham@gisti.ai here, building Gisti.
Gisti.ai eventually will be a fleet of Product and Customer Operations AI agents that continuously analyze customer interactions, identify the churn risk and automatically route the right actions: code fixes to engineering, product insights to PMs, and operational tasks to customer operations.
Today's Launch
Today, Gisti ingests customer feedback from every channel your customers already use, synthesizes it into a prioritized, evidence-backed list of opportunities, and lets product teams interact with an AI agent to explore, validate, and act on each one.
We are building Gisti with a philosophy of complete automation for specific workflows.
Demo Account
Demo Account has on-boarded Target US retail Store's App from Playstore and iOS store.
User: target-app-demo@gisti.ai
Password: Demopassword1
Need demo? Please reach out: https://calendly.com/shubham-gisti/30min
One thing that would make this way more useful for me: a way to push a synthesized opportunity straight into Linear or Jira with the supporting evidence attached. Right now the output looks great for alignment, but my PMs still have to manually re-translate the rationale into a ticket, and that's where the context tends to lose fidelity. Even a simple "send to backlog" button would close the loop.
@halil255859 it does sync to Linear. Soon to be two way sync sending updates to the customers
The evidence-backed prioritization is the right call. Most feedback tools stop at clustering into themes and you get a nice report nobody can act on, so this is the harder and more useful half.
One thing from running a multi-channel support inbox. The feedback you ingest isn't a representative sample of your customers, it's a sample of the ones who wrote in. The loud, the power users, the already-angry. The churn you actually want to catch is usually the account that just goes quiet and leaves, and it files no feedback at all. So "prioritize from what's in the feed" structurally under-weights the exact thing your roadmap says it's for.
How do you weight silence? A customer whose ticket volume drops to zero is a stronger churn signal than one still filing angry tickets, but only the angry one is in your data.
One more, from the multi-channel side. The same person shows up on two channels with different heat, a two-line angry IG DM and a calm long email about the same thing. If identity isn't tied across channels you double-count the loud one and score severity by tone instead of impact.
The downstream routing already looks solid, I'm more curious how you're thinking about the input…
Finally got around to testing this and it pulled sentiment from our support tickets faster than I expected, surfacing two issues I hadn't flagged yet. Wish it had a bit more transparency around how it scores urgency though.
@erolsabrlviy2 You can find how we calculate the score near the actual score!
Love that it ties feedback directly to evidence and not just vibes. One thing I'd want as a PM is the ability to filter opportunities by customer segment or ARR bucket so I can see whether a complaint is coming from high-value accounts or just a noisy free tier.
@yavuzaliolceop awesome working on it
love how the agent interaction sits at the center of the workflow instead of being buried in a dashboard corner, makes the synthesis step feel like a real conversation rather than another report to read
@mustafarz0m glad you liked it 😊 thank you 🙏
The synthesis angle sounds genuinely useful, especially the evidence-backed prioritization since that is usually where feedback tools fall apart. One thing that would make this stick for me though is letting the AI agent draft the actual PR or ticket description once it validates an opportunity, not just hand it back as a summary. Right now it feels like the last mile from "we know what to do" to "the work is queued up" still needs a human bridge.
@ezgiprjp agreed and this is what I am building next. Stay tuned