Decagon AI vs Gisti

Decagon AI and Gisti are both business tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Decagon AI

Decagon deploys AI agents that handle customer support end-to-end: identity verification, order lookups, refunds, subscription changes, and routing to the right team — without a human touching most of it. Workflows are defined in natural language through Agent Operating Procedures, so CX operations teams can update agent behavior without filing an engineering ticket. The platform unifies voice, chat, and email under one intelligence layer, which means the customer's context follows them across channels. Customer stories on the vendor site cite 80% deflection rates and 95% cost reductions — but those are headline outcomes from enterprise deployments with significant onboarding investment. Teams with in-house AI engineering appetite or sub-enterprise ticket volume will find the contract size hard to justify.

Gisti

Gisti ingests signals from support tickets, in-app surveys, review stores, and live chat, then runs clustering and deduplication automatically to surface product opportunities scored by evidence weight. Each opportunity arrives with the actual customer quotes attached, so prioritization arguments in planning meetings have a paper trail. The agent layer lets you explore, merge, split, or re-score clusters before pushing to Linear or an equivalent delivery tool. The routing layer — which drafts ops reports, product judgement docs, or pull requests and sends them to the owning team — is marked as still being built. Teams expecting full closed-loop routing today will be working with the clustering and prioritization half of the product while the action layer catches up.

Attribute	Decagon AI	Gisti
Pricing	Paid	Paid
Free trial	No	14 days
Open source	No	No
Has API	Yes	No
Self-hosted option	No	No
Platforms	Cloud (SaaS)	Web
Released	2023	—
Pros	Natural language Agent Operating Procedures let CX and operations teams update agent workflows without engineering involvement, so behavior changes ship in hours instead of sprint cycles. A single intelligence layer spans voice, chat, and email, which means customer context persists across channels and you avoid the broken handoff where an agent starts the conversation over on a different channel. Built-in A/B testing and QA simulation at scale let teams validate changes against live traffic before fully deploying, so a mis-configured workflow doesn't surface first in production at peak volume. The agent executes transactions — refunds, subscription changes, account recovery — not just lookups, so deflection rates reflect actual resolution rather than customers who gave up and called back. Usage-based pricing tied to conversations or resolutions aligns vendor incentives with actual outcomes, so you are not paying a flat fee for an agent that routes everything to a human.	Automatic clustering and deduplication across support tickets, reviews, surveys, and Slack, so you stop manually tagging the same complaint that arrived from four channels with different wording. Evidence panels attach the actual customer quotes to each ranked opportunity, which means planning arguments are grounded in source data rather than whoever summarized the feedback last. Impact scoring weights the evidence before ranking, so a bug mentioned once in a G2 review does not outrank a delivery problem cited across 23 support tickets. Linear sync pushes prioritized opportunities directly to the backlog tool the team already uses, so there is no manual translation step between insight and ticket. Intent-based routing — ops report, product judgement, pull request — is being built into the pipeline, which means teams get a path toward closing the loop from customer voice to the owning team's artifact format.
Cons	No self-serve trial and no free tier means you cannot validate fit before entering a procurement cycle — teams that need a proof of concept before budget approval are forced to negotiate access through a sales process, which typically adds weeks before any agent runs a single conversation. Self-hosting is not on offer, which is a hard stop for financial services or healthcare teams with data residency requirements that prohibit sending customer data to a third-party cloud — those teams move to a self-hostable competitor or build on an open-source agent framework instead. Contract structures in the six-figure annual range make Decagon economically indefensible for support operations below a certain ticket volume threshold — teams that are scaling toward enterprise but are not yet there exit for a mid-market tool with per-seat or lower-commitment pricing. Because the platform is fully managed and closed, teams with internal AI engineering capacity who want to own the model selection, retrieval architecture, or fine-tuning pipeline hit a wall — Decagon operates the agent for you, and if that is not what you want, the product is working against your team rather than with it.	The routing layer that drafts ops reports, product judgements, and pull requests is not in production — it is marked as 'building now' or 'exploring' depending on the output type. Teams who purchase expecting closed-loop automation today are buying a roadmap commitment, not a shipped feature. Agent message limits are capped on the free tier, and feedback volume from a multi-source setup hits those limits before a meaningful clustering run is complete. Teams processing more than a few hundred voices per cycle will find themselves rate-limited into the paid tier or manually batching inputs. No API is available, so any team that needs to pull cluster outputs into a custom analytics stack, a data warehouse, or a non-supported delivery tool has no programmatic path. Teams with that requirement abandon Gisti for a pipeline built on a vector database and a clustering library they control. Self-hosting is not an option, which eliminates Gisti for any team whose data governance policy prohibits sending customer feedback to a third-party SaaS — a condition that surfaces for regulated industries or enterprise contracts before the tool ever reaches a proof-of-concept stage.

Bottom line

Only Decagon AI exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Decagon AI and Gisti?

Decagon AI is Paid, while Gisti is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Decagon AI better than Gisti?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.