Worth your time, given what you already run.

Runs offline evals and production tracing for LLM features, scoring outputs against datasets you curate from real traffic.

Fit for your stack: This is the one genuine hole in your stack: you are shipping an in-product AI feature this quarter and Amplitude will tell you engagement dropped but never that your prompt regressed. Log traces from day one and build eval datasets out of the Zendesk tickets that complain about the feature, so quality has a number attached before your first prompt change.

First step

Have one engineer wrap the new AI feature's model calls in Braintrust logging on staging, then hand-label 40 real outputs as the first eval set — a two-day job that gives you a regression gate before launch.

Owner

Engineer owning the AI feature, with the PM defining what 'good output' means

Cost

Free tier for small volumes, usage-based above it; enterprise pricing on request

Converts natural-language questions into SQL against a semantic model you define, running inside Snowflake.

Fit for your stack: It runs in the Snowflake account you already pay for, so no new vendor, no data egress and no security review — and your dbt models are the semantic layer it needs. This is how eight PMs stop queueing behind the data team for the onboarding funnel questions Amplitude does not already answer.

First step

Point a semantic view at your existing dbt onboarding models and let two PMs ask the ten questions they most often file data requests for; compare the SQL it writes against what the data team would have written.

Owner

Analytics engineer, with a named PM as first user

Cost

Consumption-based on your existing Snowflake credits — no separate licence

Runs automated diagnostics on product data and does semantic search across unstructured feedback such as support tickets.

Fit for your stack: You are already paying for Amplitude, and its agents combine your quantitative funnel data with semantic search over Zendesk tickets — which is most of what a separate feedback-intelligence purchase would give you. Turn this on before you evaluate any tool in that category, because it changes the buy/no-buy answer.

First step

Ask your Amplitude rep to enable Agents on your plan, connect the Zendesk source, and run one deep-dive on the onboarding drop-off step you care about this quarter.

Owner

PM for onboarding

Cost

Depends on your existing Amplitude contract tier; confirm inclusion with your rep rather than assuming

Resolves common support requests autonomously, priced per automated resolution rather than per seat.

Fit for your stack: It is inside the support desk you already run, so it needs no new integration and inherits your existing macros and help centre. Deflecting the repetitive onboarding questions also directly serves your time-to-first-value goal, because the fastest onboarding fix is a question the user never has to ask.

First step

Pick your top three onboarding ticket intents by volume, point AI agents at those only, and measure automated resolutions and reopen rate for two weeks before widening scope.

Owner

Support lead, with the onboarding PM reviewing which intents get automated

Cost

Per automated resolution; a small allocation is included per agent per plan and additional resolutions are billed by volume — check your plan's included allowance first

Provides AI search, chat and agents across Atlassian data, and is bundled into Jira's paid Cloud plans.

Fit for your stack: Your 900-item backlog lives in Jira, and Rovo is included with Jira Premium and Enterprise rather than sold separately — so if you are on Premium you may already own the backlog-triage capability you were about to buy a tool for. Verify your plan tier before spending anything here.

First step

Check which Jira plan you are on in your Atlassian billing page; if Rovo is included, have one PM use it to cluster 100 backlog items by theme and judge whether the grouping is usable.

Owner

PM who owns the backlog, plus whoever administers your Atlassian licences

Cost

Included with Jira Premium/Enterprise Cloud plans; standalone and add-on quota pricing varies — confirm on your own bill

Generates dbt model code, tests and documentation from natural-language prompts inside the dbt platform.

Fit for your stack: You already run dbt on Snowflake, and Cortex Analyst's answers are only as good as the documented, tested models underneath it. This is the unglamorous prerequisite that makes the text-to-SQL adoption above actually work rather than confidently return wrong numbers.

First step

Use it to backfill descriptions and tests on the ten dbt models that feed your onboarding and activation metrics — the exact models Cortex Analyst will query first.

Owner

Analytics engineer

Cost

Part of paid dbt platform plans; pricing on request for your seat count

What to leave alone — and why.

It is a capable product, but its core job for you — unifying Zendesk tickets into themed product insights — substantially overlaps what Amplitude AI Agents now does with semantic search over your ticket data, and you already pay for Amplitude. Buying it before you have switched on the analytics AI you own means paying twice for ticket clustering.

Instead: Turn on Amplitude AI Agents against the Zendesk source first and run one quarter. Revisit only if PMs can name specific questions it demonstrably cannot answer.

Spend avoided: Mid five figures annually at your size — pricing is on request, so treat this as a range rather than a quote

Dovetail earns its keep where there is a dedicated research function generating call and interview transcripts to synthesise. You have eight PMs, no research team in the inputs you gave us, and your backlog problem is structured Jira items — not unanalysed qualitative sessions. The tool would be bought for the backlog and used for nothing.

Instead: Cluster the Jira backlog with Rovo if your plan includes it, and keep ChatGPT Team for ad-hoc synthesis of the occasional interview batch.

Spend avoided: Low-to-mid five figures annually; published tiers exist but enterprise pricing is on request

Adopting it means a second system of record next to Jira, and a 340-person company with eight PMs will not keep both in sync — you would be buying a migration and a permanent reconciliation tax to solve a triage problem. Your 900 items are a prioritisation decision that no new database makes for you.

Instead: Triage the existing backlog in Jira. Close or merge aggressively first; a 900-item backlog is usually a 200-item backlog with duplicates.

Spend avoided: Five figures annually plus the unbudgeted migration effort

Measuring Copilot's impact across 55 engineers is a real question, but it is an engineering-leadership purchase, not a product-org one, and nothing on your stated quarter depends on it. Buying it now spends your one AI budget conversation on a metric none of your three goals is graded against.

Instead: If the CTO wants Copilot ROI this quarter, start with GitHub's own Copilot usage metrics before licensing a platform for it.

Spend avoided: Five figures annually; pricing on request

This category has collapsed into tools you already have — your conferencing platform almost certainly ships recaps, and your 30 ChatGPT Team seats will summarise a pasted transcript. Per-seat notetaker licences across a 340-person company is the classic AI line item nobody can later attribute a decision to.

Instead: Use whatever your conferencing vendor already includes, and ChatGPT Team for anything that needs real synthesis.

Spend avoided: $10-$30 per user per month wherever it spreads — easily five figures annually if it goes company-wide

You already pay for ChatGPT Team at roughly 30 seats and GitHub Copilot for engineering. Adding another assistant vendor duplicates that spend and fragments where prompts and context live, which makes the eval discipline you actually need harder rather than easier.

Instead: Consolidate on the two assistant subscriptions you have and spend the delta on the eval tooling in the adopt list.

Spend avoided: $25-$35 per seat per month for any duplicated assistant rollout

Where to start on Monday.

Not yet, but not never.

Revisit next quarter if Amplitude's agents demonstrably fail to answer feedback questions your PMs can name specifically — that failure is the only thing that justifies the second line item.

Widen it past the product org once your dbt semantic coverage is documented and tested; without that groundwork it returns confident wrong answers to people who cannot spot them.

Re-check in a quarter once you can measure merge and revert rates on AI-authored PRs; without that baseline you cannot tell an improvement from a regression.