Genchi vs Allstacks
Allstacks and Genchi both try to tell you whether a project will land on time, from opposite inputs. Allstacks is a software engineering intelligence platform: it connects your tracker, code and CI/CD tools, and uses machine learning on that activity to forecast delivery dates and flag risks. Genchi gives engineering leaders a project delivery prediction from the people doing the work: an anonymous, 1-click confidence vote from everyone on the project, combined by project and tracked over time. Allstacks forecasts from what your tools recorded. Genchi asks the people who know what hasn't been recorded yet.
What Allstacks does well
- Forecasts from real data. Machine-learning delivery forecasts and risk alerts, based on activity across your tracker, code and CI/CD tools.
- Traceability. From business initiatives down to individual commits and pull requests.
- Engineering metrics. DORA, SPACE and flow metrics, with industry benchmarks.
- Beyond delivery. AI coding-tool impact, investment allocation and automated R&D cost capitalization.
- Enterprise-ready. SOC 2 Type II, role-based access control and an MCP server.
Where it differs
- The same question, opposite inputs. Both are forward-looking. Allstacks forecasts from historical throughput and the work already in your tools. Genchi's project delivery prediction comes from the people doing the work, who usually know about the slipping dependency, the requirement that turned out to be three requirements, or the vendor who went quiet, before any of it shows up in a ticket or a commit.
- Each misses something different. A model misses risks that haven't reached the tracker. A team vote misses what the whole team is collectively wrong about. That's why the two can work together.
- Independent judgments, combined. Genchi asks everyone on the project, anonymously, and combines their votes. Independent judgments from the people closest to the work tend to beat a single forecast, because individual errors cancel out. Does asking the team actually work? →
- Measurement vs a direct question. Allstacks computes metrics from engineering activity. Genchi has no Git, CI or ticket telemetry and calculates nothing from past activity. It asks one question and shows the answers.
- Setup and scope. Allstacks is an enterprise analytics platform, connected across your toolchain. Genchi is self-serve: set up a project, invite the team, and the first confidence vote goes out tomorrow.
- AI assistants see the prediction. Both offer an MCP server. Genchi's connector is published in the OpenAI directory and renders the portfolio and confidence views as interactive visuals inside Claude, ChatGPT and Grok.
Could you use Allstacks to get the team's own delivery prediction?
Not directly. Allstacks' forecasts come from tool data, not from asking the team. It lists developer surveys among its features, but its delivery forecasts are built from activity. Genchi is built for one job: a consistent project delivery prediction from everyone working on each project, across the portfolio.
What it costs
Allstacks publishes volume pricing for teams of at least 50 contributors. Its Growth tier is $400 per contributor per year up to 500 contributors, about $3,300 a month for 100 people. Its Enterprise tier and a bundle with cost capitalization cost more. Genchi is self-serve: free for teams of up to 10, then $2.50 per user per month, billed in blocks of 10, about $250 a month for 100 people. The two are priced for different jobs: Allstacks for a full engineering analytics platform, Genchi for one signal from the team.
Side by side
| Allstacks | Genchi | |
|---|---|---|
| Question it answers | When will this ship, based on our activity data? | Will we achieve the goal by the deadline, in the team's judgment? |
| Source of the signal | Tracker, code and CI/CD data | Everyone working on the project |
| Direction | Forward-looking forecast, trained on history | Forward-looking by design |
| Effort per person | None; data is collected automatically | One click, about two seconds |
| Consistency | Consistent model output | The same question on the same scale, every time |
| Independence | Not applicable; no one votes | Each vote anonymous and cast independently |
| What it can miss | Risks not yet in the tools | Risks the whole team is wrong about |
| Unit scored | Initiatives, down to commits and pull requests | A project, from one vote per person per project |
| Output | Forecasts, risk alerts, metrics dashboards | Score, trend and individual votes, across a color-coded portfolio |
| Setup | Enterprise integration across the toolchain | Self-serve; first vote goes out tomorrow |
| Price | From $400 per contributor per year (Growth), minimum 50 contributors | Free up to 10; $2.50 per user per month |
When to use which
- Use Allstacks for an analytics view of engineering: forecasts from your data, delivery metrics, AI-tool impact and cost capitalization.
- Use Genchi for a standing project delivery prediction from the people doing the work, one leaders can read at a glance and that tells them which teams to leave alone.
- Use both. When the model says a project is on track but the team's confidence is falling, or the other way round, that's the conversation worth having.
What Genchi won't do
- Tell you the truth if the whole team is confidently wrong. Genchi measures belief, not reality.
- Work without a clear goal. If people can't say what "done" means, their confidence can't mean much either.
- Work as well without a deadline. Without a date, confidence has less to anchor to, though the trend still shows whether it's rising or falling.
- Tell you why. A falling score shows you where to look. Comments, blockers and a conversation supply the reason.
- Measure engineering activity. It has no Git, CI or ticket telemetry, and offers no DORA metrics or benchmarks.
Genchi is our product. Allstacks is a capable engineering intelligence platform, and the two do different jobs.
Ask the people who know what the data can't see
Set up a project, invite the team, and your first confidence vote goes out tomorrow.
START FREE TRIAL2-week free trial. Teams of up to 10 stay free; larger teams pay $2.50/user/month for every billed user (~$250/month for a 100-person org). No credit card to start.