Know exactly what to build.
Automate everything else.
Specs written from your real repository, pre-coded onto a branch. Reviews, estimates and bug root-cause handled the moment they're needed. Agents that remember what they found last week. Everything that can be automated is — so your developers spend their time on the part that genuinely needs a person.
lib/aggregation/aggregator.rb
app/views/surveys/results.html.erb
Six things stop being manual.
They ship together and share an advantage no point tool has: they read your codebase and keep the project's memory. Buy them separately elsewhere and you're wiring up four vendors that can't see each other's context.
Specs your team can start on Monday
The AI interviews you about the product, checks the technical answers itself, and writes the ticket with real file paths — then pre-codes a branch for it.
See a real before/afterAgents with their own memory
They watch on a schedule, remember what they've already seen, pick up support tickets, investigate against the repo, and report anywhere — plug in any other data source you rely on.
See what they do 03AI pull-request review, out of the box
Every new or updated PR reviewed within minutes, commented directly on GitHub. No prompt engineering to get started, and it never approves or merges.
How it reviews 04Estimates balanced across the whole board
Every task sized against the same yardstick, written to its own Jira field. Consistent numbers are what make developer and team performance comparable at all.
Why consistency matters 05Bug analysis that finds the pattern
Not just who touched the line. Whether the cause is one person, a whole area of the code, or shipping faster than review can keep up.
How attribution works 06Features delivered, not just velocity
What actually shipped last sprint or month, in plain language, next to the points it was worth. The answer when someone asks what the last sprint produced.
See the dashboardIt starts with a ticket worth developer time.
The same ticket, twice. On the left, what most teams actually ship. On the right, the same request after Flosis has read the codebase and asked four questions. Only one of these can be started on a Monday morning without a meeting.
Analysts need a better export. Should aggregate by cohort instead of raw rows. Same as the dashboard basically. Nice to have for this sprint.
As an analyst, I want a privacy-safe aggregated export of a completed survey so I can analyse results without manual reshaping or risking small-cohort exposure.
• Cohorts below threshold render as masked rows, never raw counts.
• Surveys without a threshold use the default of 5.
• Existing raw export output is byte-for-byte unchanged.
lib/aggregation/aggregator.rb — reuse
app/models/survey.rb — threshold default 5
Illustrative example from a real product surface, not a customer quote. The point isn't the wording — it's that one side is a request and the other is a specification.
Capture
A new idea, or an existing Jira ticket from any board. Ideas are saved and titled so nothing gets lost.
Briefing
The AI probes for user value and proposes the best fit for the existing app — challenging requirements only when it matters.
Designs
Optional. Request Figma work from the designer, or skip it entirely.
Details
Product questions only, one at a time, grounded in the code and designs. It looks up the technical answers itself.
Estimate & pre-code
Points into your Jira field, then a branch with the spec and scaffold already on it.
Every version is kept
Your own description is v0. Each draft and edit adds a version, and you choose which is current — that's the one Jira receives.
Local until you say so
Run the whole process on an idea that isn't in Jira yet. Push it when it's ready and the ticket is created complete.
A branch, already waiting
The spec committed next to the code, the files it touches stubbed, and pending tests named after the acceptance criteria.
Agents that remember, investigate, and report back.
Most "AI alerts" are a cron job with a prompt attached — they fire, they forget, and by Thursday you're muting the channel. A Flosis agent keeps its own memory of what it has already seen, so the third time a symptom appears it says so, and tells you what the first two turned out to be.
What memory actually changes
A stateless alert would have raised three unrelated bugs. This one raised a design problem — which is the difference between noise and something worth reading.
What you can put an agent on
Pick up an incoming ticket, reproduce the reasoning against the repo, find the likely commit, and report to the channel with a draft bug for approval.
Read what came in this week, group the duplicates, match each against what already exists in the code, and hand back a ranked list instead of a pile.
QA queues that stopped moving, sprint scope creeping mid-flight, pull requests going quiet, tickets entering a sprint with no acceptance criteria.
Rules are plain sentences, not query syntax. If you can explain it to a new hire, you can hand it to an agent.
Connected by default — extend with your own sources
Every agent starts with access to your repository, Jira, GitHub and your chat. Data sources are pluggable, so the tools your team actually lives in can join the same reasoning.
Solid borders ship today. Dashed are the pluggable slots — tell us which one you need and it moves up the queue.
Support triage
● Reported · 3rd occurrence"When a support ticket mentions wrong numbers, investigate against the repo, check whether we've seen it before, and post findings to #support."
Feature request sorting
● 14 grouped into 6"Every Friday, group this week's feature requests, drop the duplicates, and note which ones the code already half-supports."
QA backlog watch
● Sent · 2 flagged"If more than 4 tasks have been in QA for longer than 3 days, send a notification."
Spec-readiness gate
● 3 tickets thin"Before sprint planning, list any ticket in the next sprint with no acceptance criteria or no estimate."
Review on every PR, from the first hour.
Connect the repository and it starts working — no prompt library to assemble, no per-seat maths as the team grows. Flosis polls for new and updated pull requests, reads them against the codebase it already knows, and comments directly on GitHub.
One yardstick across the whole board.
Human estimates drift — by who's in the room, by how the week is going, by who wants the task. That drift is why comparing two developers' output usually starts an argument. Flosis sizes every task the same way, so the numbers underneath your performance view mean something.
Consistent numbers make performance legible
Once every task is sized on the same scale, "23 points this sprint" is comparable to last sprint and across the team — not an artefact of who estimated it.
Configure when it runs
Estimate on entering a sprint, on reaching a status, or on demand. Re-estimate a single task or the whole board when scope changes.
It's an input, not a verdict
The estimate lands in its own field beside your team's. Where they disagree is usually the most useful conversation in planning.
When bugs pile up, the useful question is why.
Is it one developer who needs support? An area of the code that fights everyone who touches it? Or are features going out faster than review can keep up? Flosis reads the bug, the fix, the blame history and the change that caused it — then looks for the pattern across all of them.
Patterns it surfaces
"Nine of the last fourteen bugs touch the export pipeline. Four different authors. This is the code, not the team."
"Bugs cluster in changes merged within two hours of opening. The reviews were thin, not absent."
The AI judges whether each "bug" was ever built in the first place — a surprising share are missing features filed as defects.
And when it really is one person, you find out from a trend across weeks — not from one bad week.
Attribution, with the confidence shown
Created and fixed, per person
Both sides of the ledger, per sprint or month, so the developer clearing everyone's bugs is as visible as the one filing them.
Trend over two years
Bugs created against bugs fixed over time. A rising gap is the earliest warning you'll get that quality is slipping.
Reasoning you can inspect
Every attribution opens up to show the evidence chain. If you disagree, you can see exactly where it went wrong.
What shipped last sprint, in words and in numbers.
Velocity charts tell you a number went up. They don't answer the question you actually get asked — what did we get for this month? Flosis puts the delivered features next to the points and the hours, per sprint or per month, for the team or one person.
Sprint or month, up to two years
Switch the period and the whole view follows. Weekly resolution when you need detail, two-year ranges when you're looking for a trend.
Team or one developer
Filter to a person or a selection of them; every number, chart and list re-scopes. Useful for reviews, and for spotting who is carrying too much.
Features, not just points
A plain list of what actually reached master this period, who shipped it and what it was worth. The answer to "what did we get for this month?"
You'll care about different halves of this.
If you're the Product Owner
If you're the CTO
Matching this takes four tools that can't see each other.
Nothing here needs replacing — keep Jira, keep GitHub, keep your coding agent. The difference is that in Flosis the reviewer, the estimator, the agents and the reporting all read the same repository and share the same project memory.
| Category | What it does well | What it leaves to you |
|---|---|---|
| AI code review CodeRabbit, Copilot review | Comments on pull requests at scale, catches edge cases before merge. | Starts at the PR, and charges per seat. Nothing upstream, nothing after the merge. |
| Modern trackers Linear and similar | Fast, opinionated issue tracking with agents built into the seat price. | Asks you to migrate off Jira, and still expects a human to write the spec. |
| Autonomous coding agents Devin, background agents | Take a well-scoped ticket end to end and open a pull request. | Need precise acceptance criteria. Vague tickets in, vague pull requests out. |
| Engineering analytics Jellyfish, LinearB | Throughput, cycle time and AI-adoption reporting for leadership. | Measures the output. Doesn't improve the specs that produced it, and can't tell you why bugs cluster. |
| Native Jira AI Atlassian Intelligence | Summaries, field suggestions and drafting with zero setup. | Doesn't read your repository, so it can't ground a spec — or an investigation — in real files. |
| Flosis | Agents with memory, PR review, balanced estimates, delivery metrics, bug root-cause analysis and implementation-ready specs — all reading the same repo. | The decisions. You approve every brief, spec, push and merge. |
Six capabilities, one line item.
We won't pretend to measure your re-work for you. But the arithmetic is worth doing out loud, because it's the reason per-project pricing works at all.
A day of re-work across a developer and a reviewer, at a blended €70/hour. One ticket. One time.
PR review seats plus tracker AI seats on public list prices — before any analytics tool. Grows every time you hire.
All six capabilities. Per project. Unlimited people.
Competitor figures are public list prices converted for comparison, not quotes — check current vendor pricing before deciding. The re-work figure is illustrative arithmetic, not a measured claim about your team.
Live on your repo the same afternoon.
No migration, no data import, nothing for your team to learn before it starts helping. The heaviest task on your side is writing a short architecture note, once.
Connect GitHub and Jira Cloud
Token-based, per project. Pick the repository and the boards that matter.
Write the architecture note
A few paragraphs on how your app is put together. Flosis scans the repo daily to keep the rest current.
Turn on PR review
The fastest thing to judge — it starts commenting on the next pull request that opens.
Add one agent, then more
Start with the sweep you most resent doing by hand. Each capability is independent — add the rest once they've earned trust.
The boring guarantees, in writing.
It never merges
Flosis comments on pull requests and pushes branches. Approving and merging are human actions, always.
Agents ask before they act
An agent can investigate and report freely. Anything it wants to create — a bug ticket, a description — waits for your approval.
Scoped per project
Each project holds its own repository, boards, credentials, agents and memory. Switching projects swaps all of it — no cross-project blending.
Least privilege by role
Administrators configure integrations, credentials, prompts and estimation targets. Product Owners use everything else and see none of the secrets.
Your history stays yours
Brief and spec versions live in the application database with full history. Jira only ever receives the version you mark as current.
The model is a dependency
Claude Opus today, treated as swappable by design. You're not buying a bet on one vendor's roadmap.
We built this for ourselves first.
Ruby on SaaS is a software house. We spend our days inside other people's codebases, and the pattern never changed: the ticket said one thing, the code said another, and somebody lost two days finding out which. Then the bug arrived and nobody could say where it came from.
We tried the obvious fixes. Better templates didn't help — people filled them in. Faster ticket generators just produced tidier guesses. Alerting tools became noise inside a week, because they couldn't remember what they'd already told us. What actually worked was making the AI read the repository before it opened its mouth, and letting it keep what it learned.
That's Flosis. We run it on our own client work every week, which is also why it refuses to approve or merge anything — we wouldn't trust a tool that did, and neither should you.
One price per project. Add the whole team.
Per-seat pricing punishes you for involving people — the designer, the QA lead, the second PO. Flosis charges for the project and lets everyone in.
One project, one team, unlimited members. Administrators configure it; Product Owners use everything except the credentials.
Book a walkthroughWhy it's €200, and what changes later
This is an introductory price while we learn what real usage looks like across different codebases. As the product matures we'll move to pricing that tracks token usage more accurately, so a small project isn't subsidising a monorepo. Existing customers hear about any change from us first, in writing, before it applies — and we're onboarding a deliberately small number of projects at this price while we tune it.
Compared with paying per seat
| Ten developers, PR review only (typical seat pricing) | ~€240 / mo |
| Ten developers, tracker seats (typical) | ~€100–160 / mo |
| Engineering analytics (typically quoted, per dev) | on request |
| Flosis — all six capabilities, unlimited members | €200 / mo |
Competitor figures are public list prices converted for comparison, not quotes.
Before you get in touch
What makes your agents different from scheduled alerts?
Memory. An agent remembers what it has already reported, so it can tell you this is the third time a symptom has appeared and what the first two turned out to be. It can also act — pick up a support ticket, investigate against the repo, and report findings rather than just raising a flag.
Do we have to leave Jira?
No. Flosis sits on top of Jira Cloud and GitHub, reading boards and writing to fields you control — including its own AI estimation and AI actions fields. Your team keeps working where it already works.
Can we connect our own data sources?
That's the direction the product is built for. Repository, Jira, GitHub, Figma and chat are connected today; support desks, error monitoring and product analytics are the next slots. Tell us which one matters to you and it moves up the queue.
Will it merge something we didn't review?
Never. Flosis comments on pull requests and pushes branches; approving and merging are human actions. Agents investigate and report freely, but anything they want to create waits for your approval.
How does it know our codebase?
Your repository is checked out server-side with a token you provide, and master is refreshed daily. The AI reads whatever files it needs, alongside a short architecture description you write once.
Can we run more than one project?
Yes — each project has its own repository, boards, credentials, agents, memory and reporting, and switching projects swaps all of it at once. Pricing is per project, so you add them as you need them.
Pick the job you most resent doing by hand.
Half an hour on your own repository. Bring your vaguest ticket, your noisiest bug pattern, or the Friday sweep you keep putting off — whichever you'd most like to stop doing. That's a fair way to judge it.