Coodesh Release Notes — Q1/2026

During the first quarter of 2026, we evolved the Coodesh platform around three major priorities: more intelligence in assessments, greater control and security in evaluation workflows, and a better experience for companies, candidates, and technical teams.

Between January and March, we released major improvements across certificates, result visibility, bulk question import, AI-generated content, proctoring, custom scoring, competency matrices, candidate filters, and the new AI Conversational Interview.

These updates strengthen Coodesh as a complete platform for companies that need to assess, compare, and develop talent with greater precision, scale, and governance.

It is now possible to include AI-led interviews inside Coodesh assessments.

The new AI Conversational Interview allows a virtual interviewer to talk with the candidate in real time, following a natural flow of introduction, topic exploration, and closing. Companies can configure the topics to be covered, choose the interviewer avatar, and include up to 3 conversational sessions per assessment.

Candidates can respond by text or audio, and the conversation can automatically resume if the connection becomes unstable. The experience also includes animated messages, typing indicators, faster initial greetings, and audio playback with waveform visualization.

Traditional interviews consume operational time, require calendar coordination, and make standardization across candidates difficult. With AI Conversational Interview, companies can scale initial or intermediate interview stages while maintaining consistency, traceability, and a smoother candidate experience.

For medium and large companies, this means:

Coodesh now has clearer workflows for sending certificates and assessment results.

Candidates who complete an assessment with a score above the configured minimum can automatically receive their certificate by email. Companies can also manually resend certificates when needed.

Assessment results can now be hidden from candidates and sent later, either individually or in bulk. Attempts with hidden results are identified in the system with the Hidden Results tag.

We also added bulk actions to manage attempts, including reopening, unlocking, editing, sending results, and canceling scheduled invitations.

Companies with more structured hiring processes do not always want to release results immediately to candidates. In many cases, recruiting teams need to review performance, compare candidates, or align communication internally before sending feedback.

With these improvements, companies gain more control over the communication flow and can decide when and how to share results and certificates.

This improves process governance and avoids premature, inconsistent, or poorly timed communication.

The platform received new integrity improvements for online assessments.

Each assessment invitation can now have individual proctoring settings, including webcam, photo, tab switching, and other environment validations. These settings can differ from the assessment’s default configuration.

We also added new monitoring capabilities, including:

Online assessments need to balance candidate experience with process reliability. For companies hiring at scale or evaluating critical roles, it is essential to have evidence about the environment in which the assessment was completed.

These improvements help increase assessment security, reduce fraud risk, and provide more context for hiring decisions.

By allowing settings per invitation, companies also gain flexibility: stricter rules can be applied to sensitive roles, while simpler rules can be used for exploratory, internal, or lower-risk assessments.

It is now possible to import assessment questions in bulk using CSV or JSON.

The import process includes automatic language detection, skill resolution, and support for fields such as weight, question objective, and evaluation criteria.

This feature makes it easier to create or migrate question banks into Coodesh.

Companies that already have internal libraries of questions, cases, technical tests, or department-specific evaluations no longer need to register everything manually.

Bulk import reduces setup time, improves library standardization, and allows HR, Talent Acquisition, Learning & Development, and technical teams to create assessments faster.

For organizations with multiple business units, roles, levels, and languages, this accelerates platform adoption and reduces operational work.

Assessment questions can now have custom minimum and maximum scores.

We also added support for text-based scores for qualitative evaluations, making it possible to represent results more flexibly when a numeric score is not enough.

Not every question has the same weight in an assessment. A practical question, an open case, or a video answer may be more relevant than a simple multiple-choice question.

With custom scoring, companies can calibrate assessments more precisely by assigning different weights to critical competencies.

This improves the quality of analysis and enables assessments that better reflect role level, expected seniority, and job complexity.

Coodesh now supports AI-generated content to help build career structures, tracks, positions, categories, and skills.

AI can generate descriptions, proficiency levels, examples, and requirements related to skills, with multilingual support.

In March, we also improved skill generation so descriptions and levels are processed in the background without blocking the interface.

Mapping skills, roles, and proficiency levels is one of the most time-consuming stages of skills management, career path, and workforce planning projects.

With AI-generated content, companies can accelerate the creation of their skills architecture without starting from scratch.

This reduces dependency on manual spreadsheets, helps standardize information across areas, and enables HR and leadership teams to build more consistent career models.

The Careers area received a new Interactive Competency Matrix.

The interface was redesigned to make it easier to navigate large matrices, with support for:

In addition, the same skill can now be linked to multiple categories, with a visual indicator showing how many categories share that skill.

Medium and large companies often have complex structures of roles, tracks, levels, and competencies. Without the right interface, managing this information becomes slow and difficult to maintain.

The new matrix makes the operation more visual, faster, and more scalable.

For HR, People Analytics, Learning & Development, and technical leadership teams, this makes it easier to build skills maps, career progression models, and workforce planning structures.

Candidate filters were improved across different areas of the platform.

Salary range filtering now correctly differentiates between CLT and PJ compensation. Previously, candidates with only PJ salary information could appear in CLT-filtered searches, and vice versa.

We also fixed Talent Feed filters, including location, careers, contract type, languages, skills, and salary range.

In addition, application fields and filters were expanded with information such as source, campaign, preferences, public links, and clearer invitation flows.

Filter accuracy directly impacts recruiting team productivity.

When searches return candidates outside the desired profile, teams waste time reviewing incorrect data. With more specific and consistent filters, companies can find talent that better matches the role context, contract model, location, and salary expectations.

This improves operational efficiency and shortlist quality.

Coodesh advanced several improvements related to language and localization.

Updates include:

For companies operating in Brazil, Latin America, or global contexts, multilingual experience is essential.

These improvements help provide a clearer journey for candidates and internal users, reducing communication friction in international or distributed processes.

For clients with operations in multiple countries, this also makes it easier to standardize hiring and assessment workflows.

The assessment application now includes full dark mode support, with theme switching and better persistence of user preferences.

We also made visual and usability improvements across recording components, upload flows, navigation, menus, sidebars, transcriptions, answer printing, and assessment screens.

Audio and video recording were also improved, with camera and microphone selection, device preference persistence, and video file upload.

Candidate experience directly influences assessment completion rates.

Clearer, more accessible, and more consistent interfaces reduce friction during the test, minimize confusion, and make the process feel more professional.

For companies, this improves employer brand perception and reduces support requests during assessments.

More question types now support complementary file uploads, including:

We also implemented PDF file validation to prevent errors when candidates upload corrupted or invalid files.

In many processes, candidate responses are not limited to a multiple-choice option or short text. They may involve code, documentation, evidence, technical files, presentations, spreadsheets, or complementary materials.

With broader upload support, Coodesh enables more realistic assessments that better reflect real job challenges.

This is especially relevant for technical, product, design, data, business, and roles that require practical deliverables.

CSV exports were optimized for large accounts, with improvements in indexes, export parallelization, and processing of high volumes of results.

We also optimized queries, uploads, backend streaming, and memory consumption.

Medium and large clients usually handle high volumes of candidates, assessments, results, and historical records.

With faster exports and lighter queries, the platform becomes more efficient for scaled operations.

This reduces waiting time for reports, improves the experience for administrative users, and makes the platform more reliable for larger datasets.

In Q1/2026, Coodesh advanced strategic capabilities for companies that need to assess talent with more precision, scale, and security.

The main highlights were:

These updates reinforce our commitment to helping companies make better talent decisions by combining assessments, artificial intelligence, skills data, and a more reliable experience for everyone involved.