What's new in ResAI

Account Menu and Profile

Your profile and personalization settings now live together in one account menu. You can upload a profile photo and set an organization logo, and your photo carries through to your Ask ResAI conversations so it is clear who is asking. Personal controls and a direct path to help are available from the same menu, so the things that are about you and your workspace are all in one place instead of scattered across the app.

Your Activity Log

You can now see a personal view of your own activity. A new filter in the activity log narrows the record down to just your actions, so you can look back at what you did without wading through everything happening across the organization. This sits alongside a refreshed Settings area that makes these controls easier to find.

Manage Your Automations and Connections

Automations and connections are no longer set-once. You can now edit, pause, and delete them directly from where you manage them. Pause a scheduled automation when you need to hold it, adjust a connection as your sources change, and remove the ones you no longer use, all without having to tear something down and rebuild it from scratch.

More Control Over Ingestion

You now have finer control while data is coming in. You can pause an ingestion that is in progress and resume it later, and you can delete a dataset from any state rather than waiting for it to finish first. Datasets also show clearer status, so you can see at a glance which files are ready, which are still processing, and which need attention.

More Ways to Bring Data In

This release broadens the documents ResAI accepts.

• Word documents. You can now bring in Word files (DOCX and DOC) directly, so Word-based procedures, reports, and reference material go in alongside your other sources.
• Better tables in PDFs. ResAI now reads tables inside PDFs more accurately, so the rows and numbers in a PDF come through cleanly instead of getting jumbled.

Get Help Without Leaving ResAI

A new in-app help option lets you raise a request for our support team from directly inside ResAI. Fill in the form where you are working and it sends your request straight to our support team, so you do not have to switch to email or track down a separate channel when you need a hand.

SOP Library

Standard Operating Procedures, policies, and other reference material now have a durable, organization-wide home. Until now, a reference document had to be re-uploaded or re-attached to every working dataset where you wanted ResAI to draw on it, which meant duplicated effort and procedural context that was inconsistently available. With the SOP Library, a Data Manager or Platform Owner uploads each reference file once, and it becomes individually selectable by anyone in the organization to ground their questions, right alongside their working data. SOP files live in their own dedicated section of the chat, separate from your working datasets, and your selection is remembered from one question to the next. Answers that draw on an SOP cite it the same way ResAI cites any other source, so you can always see where a procedural answer came from.

The SOP Library is the first capability delivered through a new module framework. Administrators turn modules on or off for the organization from Settings. Turning a module off simply hides its content rather than deleting anything, and turning it back on restores it as it was. The framework is built so future capabilities can be delivered the same way.

Voice Capture to Dataset

You can now record audio directly in ResAI and turn it into a dataset, without leaving the app or uploading a file first. Record from within the chat, and ResAI transcribes the capture and runs it through the same pipeline as any other audio, so it becomes searchable, connected knowledge. This is permission-controlled: recording audio into a dataset is a capability administrators grant, so it's available to the people who should have it and off for everyone else.

First-Login Onboarding

New users now get a guided first-login experience. An overlay walks them through Ask ResAI on their first session, so someone signing in for the first time has a clear starting point instead of a blank screen. This pairs with the trial-to-tenant onboarding path introduced in v1.0.2 to make the early experience smoother end to end.

More Ways to Bring Data In

This release broadens the inputs ResAI accepts.

• Hosted PDF connector. A new connector pulls in PDFs from a hosted source, alongside the existing SharePoint and Azure Blob Storage connectors.
• Multi-sheet Excel. Workbooks with multiple sheets are now handled properly. ResAI proposes field definitions per sheet, and nameless or empty columns are handled cleanly rather than tripping up the upload.

Run History

New run-history views let you see how scheduled and automated work has executed over time, so it's easier to confirm things ran and to look back at what happened.

More Resilient Ingestion

We hardened the ingestion experience so a single problem file no longer holds up the rest.

• If one file fails or stalls during ingestion, the dataset stays usable. The files that processed successfully remain queryable instead of the whole dataset being blocked.
• A dataset becomes queryable as soon as it has ready files, rather than waiting for an entire batch to finish, with clearer feedback when a dataset has failed files to address.
• We resolved a set of cases where ingestion problems could fail quietly, so issues now surface where you can see and act on them.

Processing Pipeline

The centerpiece of v1.0.2. Version 1.0 ran each file type down its own fixed path and stopped at extracted fields. The new pipeline runs one intelligent flow that tracks every document through a visible lifecycle and turns supported inputs into searchable, interconnected knowledge, while still producing everything 1.0 did.

For every supported file type, the new pipeline adds:

• A per-document lifecycle. You can see exactly which stage each file is at, rather than a single status for the whole batch.
• Resumability. If a file fails partway through, it restarts from the stage that failed, not from the beginning.
• A review queue. Files that can't be processed automatically are routed to a queue for a person to look at, instead of failing silently.
• Semantic search. Find content by meaning, not just exact keywords.
• A knowledge graph. Entities like people, assets, and events are linked across documents, so the system understands how information in one file relates to another.

The pipeline stays backward compatible. It emits the same extracted-field output as 1.0, and a single flag routes uploads to the old or new pipeline, which lets us roll it out gradually and revert instantly if needed.

Audio Processing

ResAI now accepts audio as a first-class input. Upload an MP3 or WAV file and ResAI transcribes it, plans the work from the transcript, chunks it, and enriches it with entities, the same way it treats any other document. A quality gate based on transcription accuracy diverts poor transcriptions to the review queue so a person can check them before they're used. Because the content of an audio file can't be known until it's been heard, audio takes a slightly different route through the pipeline: it's transcribed first, then the processing plan is built from what was actually said. Once processed, audio becomes searchable, connected knowledge like everything else, which opens up use cases such as quality assurance on recorded calls and reasoning recorded content against your Standard Operating Procedures. Supported audio formats today are MP3 and WAV.

Data Sources

Datasets remain what makes ResAI different from a generic AI assistant: answers are grounded in your curated data, and you can trust where they came from. Beyond extracting fields, supported files are now chunked and embedded for semantic search and linked into the knowledge graph, so a dataset is no longer just a set of values to query but a connected body of knowledge ResAI can reason across.

Human in the Loop

Where a decision carries downstream consequences, ResAI asks for confirmation before it proceeds rather than acting on an assumption. This keeps a person in control of the choices that shape later results. Structured data is the clearest example. When a dataset arrives without field definitions, ResAI proposes a name for each column based on what it sees in the data and asks you to confirm or correct it before the dataset goes live, because an incorrect definition would affect every downstream answer. The same checkpoint applies across the pipeline, so automated steps do not commit to a low-confidence result on their own.

Automation

Automation expands in this release so that more of the work between your data and the people who need answers happens on its own. Scheduled ingestion now includes a SharePoint connector alongside Azure Blob Storage. Point ResAI at a SharePoint library and it checks on the schedule you set, pulls in new files, runs them through the pipeline, and makes them available to Ask ResAI and reporting, with no manual uploads. Reports continue to run a saved query on a schedule and deliver the output; in this release, scheduled runs generate the report and send it by email automatically. Additional output destinations, including SharePoint and Power BI, are planned for an upcoming release.

Ask ResAI

A set of improvements makes everyday work in Ask ResAI smoother, especially around datasets.

• You can now attach datasets directly within the Ask ResAI flow.
• Dataset statuses update on their own. Previously they didn't refresh until you reloaded, so you couldn't see where processing stood.
• Long tables now return in a preview window with virtualized scrolling, so large result sets stay responsive.
• You can export the complete result set to CSV, not just the rows currently on screen.

Authentication and Onboarding

Microsoft Entra remains our primary identity provider. The onboarding flow is now a clear path from trial to a fully managed tenant. Trial users can sign in with a one-time password and start working immediately, without waiting on an app registration. When an organization is ready, an initial user registers their tenant details to connect the organization's own Entra ID, after which the client adds and manages its own users through their existing Microsoft sign-in.

Audit Logs and Settings

Audit logging and role-based permissions from 1.0 carry forward, now covering the new pipeline and automation activity. Every login, dataset change, ingestion run, Ask ResAI query, and automation run continues to be captured with the actor, action, timestamp, and outcome, and Platform Owners continue to manage role-based permissions across the organization from Settings.

Authentication

Most of our customers run on Microsoft, so Microsoft Entra is our primary identity provider. Once your Tenant ID, Client ID, and Client Secret are configured in Settings, your team signs in with the same credentials they already use for everything else. To keep trial momentum, we added one-time password access for trial users. Platform admins can onboard trial users directly and grant them 15 or 30 days of access without waiting on a tenant registration.

Data Sources

Creating and managing datasets is what makes ResAI different from a generic AI assistant. Instead of pasting documents into a chat window, you build a curated dataset that ResAI works against, so answers are grounded in your actual data and you can trust where they came from. In this release you can upload PDFs, CSVs, and Excel files, grouped as a single dataset or uploaded individually. For structured data, human-in-the-loop validation asks you to confirm or correct proposed field definitions before the dataset goes live.

Insights

Insights turns Ask ResAI into a dashboard. Anything you can ask in natural language, you can pin as a widget on a board and watch it update over time. Promptable widgets let you build a chart by describing what you want to see, then choose how to visualize it, from foundational chart types through specialist charts, with a guided five-step flow. The result is a dashboard built by the people closest to the data, in minutes, without a BI team in the loop.

Automation

Two kinds of automations to start, both aimed at removing the manual steps between your data and the people who need answers. Ingest connects Azure Blob Storage and checks on a daily or weekly schedule for new files, adding them to the dataset automatically. Reports run a saved query on a schedule and deliver the output by email or PDF.

Audit Logs

The first batch of audit logs is live. Every login, dataset change, ingestion run, Ask ResAI query, and automation run is captured with the actor, the action, the timestamp, and whether it succeeded. This matters because regulated industries need an audit trail for compliance, and any team operating on shared data needs to understand who changed what.

Settings

Configure your organization profile from Settings, including organization name, primary time zone, and the Entra credentials for SSO. Multi-tenant operators can switch between organizations without signing out. Platform Owners can manage role-based permissions across the org, toggling capabilities like Manage Users, Permissions Management, Add and Delete Datasets, Create Connections, Create Automations, Use Ask ResAI, Saved Queries, and View Audit Logs for each user role.