Project Management Software for AI

Research doesn't move like a feature pipeline

Applied AI work doesn't move like a normal feature pipeline. A "task" might be a two-day experiment that either becomes a shipped capability or gets abandoned once the eval numbers come back, and the abandoned ones outnumber the shipped ones by a wide margin, for entirely legitimate reasons. That's not a process failure. It's what research looks like.

The actual failure shows up later: three months on, someone tries the exact approach a teammate already ruled out, because the only record of that first attempt was a Slack message that scrolled out of view, or a note in a personal doc nobody else had access to. The company re-pays a cost it already paid once, and doesn't know it.

The pattern repeats at review time too. A quarterly review asks what the research team accomplished, and the honest answer is a list of approaches tried, most of which didn't ship, which sounds like a lot of nothing to someone outside the team, unless there's a record showing what each attempt ruled out and why that was worth finding out.

Estimating this work is genuinely harder than estimating a standard feature, and no tool fixes that by getting cleverer about dates. An experiment that might not work doesn't become more predictable just because a project tool asked for a due date. What can be fixed is what happens once results come in: turning an outcome, positive or negative, into a piece of institutional memory instead of into nothing.

ShipSprint's fit here is fairly specific: a wiki that sits next to the work, where "we tried X, it didn't beat the baseline, here's why" is as easy to write as a Slack message and a lot harder to lose, plus a board that can hold both the shipped roadmap and the experiment queue without pretending they're the same kind of work.

The retrieval approach someone already tried in March

Picture a team six weeks into a new project, debating whether a particular retrieval strategy is worth building out properly. Someone remembers a teammate looked at something similar back in March, but that teammate is heads-down on a different problem now, and the only trace of what they found is a half-remembered comment in a review meeting nobody wrote down. The team spends two days re-deriving a conclusion that already existed, because the fastest way to find out was to just run the experiment again.

The fix isn't a smarter search tool. It's making the five minutes it takes to write "tried this, here's why it didn't pan out" actually happen at the time, on a page anyone can find later without knowing who to ask. A wiki page linked from the relevant project, searchable and not living in one person's head, turns that kind of dead end into something the next person can build on instead of repeat. On a team where this discipline actually sticks, the second experiment on a dead-end idea just stops happening.

What the Claude and ChatGPT connection actually is

Worth being direct about this, because it's the audience most likely to notice if we oversell it. The Claude and ChatGPT integration lets you ask questions of your workspace and make updates to it in plain language, nothing more automated than that. ShipSprint doesn't auto-generate tasks from a model's output, and there's no AI layer forecasting your research timelines for you; the forecasts are the same measured-velocity calculation every ShipSprint team gets, whether they're shipping a CRUD feature or running an ablation study.

Where this fits

This is written for a team where a meaningful share of the week is genuinely exploratory, trying an approach, evaluating it, deciding whether to build on it, rather than executing a fully specified roadmap. That's a different rhythm from a team that already knows what it's building and just needs to build it.

A company building a conventional product with AI features bolted on, where the day-to-day is mostly regular engineering work against a known spec, will likely find a page built around sprints and releases closer to how its week actually runs.

Most applied AI teams are actually a mix of both at once: a research thread that's genuinely open-ended, running alongside a productization thread that behaves like normal sprint work once an approach is chosen. That split is exactly why the board and the wiki are meant to hold different kinds of work differently, rather than forcing an experiment and a shipped feature into the same shape.

What it costs

• Free, forever, for up to 5 users and 2 projects, room enough for one research pod to pilot the wiki-and-board combination.
• Team costs ₹299 per user monthly, or ₹2,899 yearly, capped at 40 users.
• Business, ₹599 per user monthly or ₹6,499 yearly, adds forecasts, scorecards and the owner command center across research and product.
• Every paid plan opens with a 14-day full-access trial on Business, a sample project already loaded, no card needed.