DevPrism's answer

Because the question most teams now have is not "how fast do we ship" but "what did the AI investment change, and what did it cost". DevPrism answers it on your own data: adoption of GitHub Copilot, Cursor, Claude Code, Codex and Devin Desktop next to delivery metrics, quality signals and token spend, per team and per period.

It also acts. Beyond dashboards, a policy engine applies rules in three execution modes - suggest, act with approval, or act automatically - with dry run enabled by default, so automation is introduced at the pace the organisation is comfortable with.

Practical points: a free plan for up to seven managed contributors with no credit card, an interface in English, French, Spanish and German, and hosting in the European Union with a GDPR native design.

Typo's answer:

• Typo not only provides reports but also recommends actionable insights and real-time goals, helping you enhance the software development process continuously.
• All our insights are co-related to provide a holistic view of your teams to drive overall change.
• Typo's AI performs automated code reviews in real-time and suggests auto-fixes, significantly improving code quality & velocity.
• Typo also provides insights on developer experience & alerts you with potential burnout zones.
• We offer customisations as per your processes and a dedicated support manager with priority support over email/Slack/call ensuring the success of the program.

DevPrism's answer

Engineering leadership in software organisations that have already rolled out AI coding assistants: VPs of Engineering, CTOs and Engineering Managers who are asked to justify the spend and to show what changed in delivery and quality.

Platform and developer experience teams use it too, for the delivery metrics and the developer experience surveys. It fits teams from a handful of developers on the free plan up to several hundred contributors.

Typo's answer:

Typo is most suitable for these managerial roles: CTO (Chief Technology Officer), VP of Engineering (Vice President of Engineering), Head of Engineering, EM (Engineering Manager), TL (Technical Leader)

DevPrism's answer

It started from a gap encountered while leading engineering teams. Copilot, Cursor and Claude Code get rolled out, productivity feels better, and then someone asks for the return on the investment - and there is nothing to show. Suggestion acceptance rate measures usage, not impact, and no dashboard connects AI usage to delivery outcomes.

Meanwhile the data already exists, scattered across GitHub, Jira, SonarQube and the assistants themselves, and teams rebuild the same spreadsheet every quarter. DevPrism was built to bring those sources together and, past the measurement, to act on what they reveal.

Typo's answer:

Our founders, Kshitij and Varun, have first-hand experience with well-functioning engineering teams that consistently ship products and those that face challenges.

They've gone from being developers to engineering managers and now founders, recognizing that the best teams prioritize transparent workflows, continuous improvement, and the use of effective tools to make it happen.

In their roles leading and scaling engineering teams, they shared a common frustration - every other business team had access to metrics & workflows that efficiently resolved their operational issues, thereby showing their teams's impact on the business - the same was not true for engineering teams.

Typo was created to support engineering teams with the visibility & automation they need to build efficient software delivery & high-performing teams.

Our unwavering mission is to support in building high-performing dev teams & revolutionising the software delivery cycle - ship reliable software fast. This requires complete visibility, fast dev cycles, streamlined processes, and high-quality code - with an amazing developer experience.

By seamlessly plugging into your dev tool stack, Typo leverages AI to generate real-time engineering insights, automating code reviews & improvement goals, and bringing a unique way to measure & improve developer experience. This transformation fuels development, ensures game-changing business outcomes and propels unprecedented growth.

DevPrism's answer

Three axes that usually live in separate tools are correlated on the same data: delivery speed (DORA), product quality (churn, duplication, coverage, code smells) and the cost of AI coding assistants, reported per pull request rather than as a licence total.

The second part is what the correlation is for. Agents investigate why a metric moved, and a policy engine can act on the answer - assigning a reviewer, flagging a risky pull request, escalating a stale one - writing back to GitHub, Azure DevOps or GitLab at the level of autonomy the organisation grants, from observation only to automatic remediation.

Pricing follows managed entities rather than headcount: humans, AI assistant seats and autonomous agents are counted separately, because not every contributor is a person any more.

Typo's answer:

• Typo provides insights on engineering metrics, developer experience, and code reviews - all in one place!
• Typo connects with your tool stack (Git, issue tracker, etc.) in 30 seconds & generates insights under 10 minutes.
• We continuously expand our range of integrations, ensuring you always have the tools you need at your fingertips.
• Customers love our email & chat support.

Typo's answer:

• Mountain Network, USA
• Transfeera, Brazil
• Prendio, USA
• Method, Canada
• TrueNorth, USA
• BidOut, USA
• Billog
• Semaai, Indonesia
• Cashify, India

DevPrism's answer

Backend in C# on .NET, with ASP.NET Core minimal APIs, Entity Framework Core, MediatR and Hangfire for background processing. PostgreSQL with the pgvector extension stores both relational data and embeddings for semantic search; Redis handles distributed caching.

The web application is React with TypeScript, built with Vite and styled with Tailwind CSS. Everything runs on Azure Container Apps in the France Central region, orchestrated with .NET Aspire.

Typo's answer:

• For the part you see and interact with, we use React.js
• The behind-the-scenes operations are handled by Node.js
• We store all our data in a database called MongoDB and MySQL
• To manage and process tasks efficiently, we use a system called RabbitMQ
• For advanced analytics and artificial intelligence, we use Python