Fleks in Action

01 — 01 - Fleks introduction.mp4 Fleks extracts data and context from everyday interactions, structured and governed by your data models. Creating the trusted foundation for turning context into action. Everyday interactions. Your data models. Trusted context. Governed actions.

02 — 02 - Meet Maya.mp4 For a sales rep like Maya, days are filled with customer conversations. But every meeting creates more work: organizing notes, updating the CRM, and making sure that she documents and follows through on her commitments and next steps.

03 — 03 - Meet Alex at Northstar.mp4 Today, Maya is meeting with Alex, a potential buyer at Northstar, one of her prospect accounts.

04 — 04 - Maya uses Fleks.mp4 Maya uses Fleks to extract data from conversations like this one, capturing the context, commitments, and next steps that matter to her and her team.

05 — 05-07 - The initial data model.mp4 Maya's ops team defined their initial Fleks data model using plain language schema to include four data objects and three relationships. The four objects include interactions, people, organizations, and tasks. The three relationships are owner, participant, and employee.

08 — 08-10 - Discovery call.mp4 Let's return to Maya's discovery call with Alex. Alex, what would you need to see to feel confident moving forward? We'd like to explore a proof of concept. We'll need to define what success looks like. First, we'll need to complete a security brief. I'll send it over by Friday.

11 — 11 - From transcript to facts.mp4 After the call, Fleks analyzes the transcript using the team's data model. Fleks identifies the interaction: a discovery meeting. The meeting date is September second, twenty twenty-six. The call details identify Maya and Alex as people, and Northstar as an organization. Alex's request becomes a task: define POC success criteria. Its owner and due date aren't specified. Maya's promise becomes a task to send the security brief. Fleks uses the conversation's context to resolve references and clarify meaning. It refers to the security brief. Maya is the owner. Friday sets the due date: September fourth. Five of these six cards contain proposed facts, pending the governance steps of review and acceptance. Maya's person record already exists, so Fleks tags it as a reference.

12 — 12 - Meet the FleksScene.mp4 Fleks brings these facts and relationships together into a FleksScene, capturing the context of Maya's conversation with Alex. Fleks adds relationships to show that Maya and Alex participated in the meeting. Alex is an employee of Northstar. And, Maya owns the security brief task. For each proposed fact, Maya checks the source evidence. She can edit, reject, or accept. She accepts the meeting interaction. The person Alex. And, the organization Northstar. Her existing person record is included in the scene as a reference. She also accepts her participant relationship with the meeting. Next, she accepts the security brief task, its due date, and her owner relationship. She reviews the success-criteria task last and adds the owner and due date.

13 — 13 - Maya sees the POC gap.mp4 As Maya reviews the success-criteria task, she recognizes a recurring need. POCs have their own progress, people, and work to track. They need an object in the team's data model. POCs are a common part of our sales cycle. We need to track each POC for an organization, with a sponsor and related tasks, like defining success criteria. A POC object would let you track that work together. Here's a proposed schema for your ops team to review. Maya accepts the proposal for submission. Her ops team reviews and approves the change to the shared data model. For Northstar's POC, Alex is the sponsor. Link the success-criteria task to it. With the approved data model, Fleks connects the POC to the account, sponsor, and work. Maya reviews and accepts the remaining facts and relationships.

21 — 21 - The connected result.mp4 Maya now has a connected view of the conversation: the people involved, the commitments made, and the POC they discussed. In FleksScene, she can inspect the facts, their source evidence, and their review status. Across her customer interactions, Maya can see her open tasks together, with the context she needs to follow through.

22 — 22 - The foundation for action.mp4 Fleks extracts meaning from everyday interactions and structures it around your data models. As your needs evolve, your team governs changes to the data model and decides which facts to accept. Less time reconstructing conversations. Clearer context for what comes next. Fleks provides the foundation for turning trusted context into governed action.

01. Fleks introduction Fleks extracts data and context from everyday interactions, structured and governed by your data models. Creating the trusted foundation for turning context into action. Everyday interactions. Your data models. Trusted context. Governed actions.

02. Meet Leah. For a project manager like Leah, a delivery review is where a plan becomes real work. One broad task can turn into several commitments, with different owners and dates. Keeping those parts connected takes more than a list of meeting notes. Today, she is reviewing the customer portal launch with Sam, the engineering lead at Northstar.

03. The initial data model Leah's team starts with interactions, people, organizations, tasks, and projects. The plain language schema defines the attributes and relationships for each type. Tasks have descriptions, statuses, and due dates, with links to owners and projects. Leah, Sam, Northstar, and the portal project already exist. So does an open task called complete launch checks. The model has no relationship for subtasks yet.

04. Delivery review Let us listen to part of the conversation. Leah says: For our complete launch checks task, what are the next pieces of work? Sam says: I'll validate the migration by Friday. That is one part of the launch checks. Leah says: I'll review launch readiness on Monday. Let's track those as two subtasks of complete launch checks, with our separate owners and dates.

05. From transcript to proposed facts Fleks analyzes the transcript using the adopted data model. It proposes a delivery-review interaction dated September second, twenty twenty-six. Sam's commitment becomes a task to validate the migration by September fourth. Leah's commitment becomes a task to review launch readiness by September seventh. The known people, project, organization, and complete-launch-checks task remain references to existing records.

06. Meaning comes from context The conversation identifies who promised each piece of work. Friday and Monday become dates relative to the meeting. Fleks can propose the two tasks with their owners and project links. The transcript also explains that both are parts of complete launch checks. That meaning is preserved in the evidence, but the current schema cannot yet express the parent and subtask connections.

07. A FleksScene captures context. The FleksScene brings the meeting, people, and tasks into a connected view. Leah and Sam participated in the review. Sam is an employee of Northstar. Each new task has a proposed owner and concerns the portal project. The existing launch-checks task is visible too. Leah can see the pieces of work, but their relationship to the larger task is missing.

08. Review the facts and their connections. Leah opens Sam's proposed task and checks the source evidence beside the graph. She reviews its due date and owner relationship, then accepts them. She reviews her own task and the meeting in the same way. These commitments can be recorded under the existing schema. Their acceptance does not create the missing subtask relationship or mark the parent task complete.

09. The data model can evolve. Leah wants the model to reflect how the team actually organizes the work. Complete launch checks is the parent task; validating the migration and reviewing readiness are its subtasks. They are all tasks, with the same basic attributes. She asks Fleks for a relationship that connects one task to its parent, so the hierarchy can be represented explicitly.

10. A governed change to the data model. Fleks proposes a parent-task relationship from Task to Task. No new object type is needed. Each subtask can point to its parent, while retaining its own owner, status, and due date. Leah submits the change. The team's schema stewards review and adopt it. Their approval makes task hierarchy available; the particular parent links from this meeting still require review.

11. The FleksScene gains connected context. With the relationship adopted, Fleks proposes two links: validate migration has complete launch checks as its parent, and review launch readiness has the same parent. Leah inspects both against the conversation and accepts them. The FleksScene now shows the larger task and its parts together, with separate owners and dates. Existing task identities are preserved throughout the change.

12. The conversation, connected. Leah can now inspect the parent task, follow either subtask, and return to the conversation that established the work. Each person keeps a clear commitment, while the project retains a connected view of the whole. Future updates can describe progress at the appropriate level. The hierarchy adds context without silently completing tasks or changing the team's responsibilities.

13. The foundation for action Fleks extracts meaning from everyday interactions and structures it around your data models. As your needs evolve, your team governs changes to the data model and decides which facts to accept. Less time reconstructing conversations. Clearer context for what comes next. Fleks provides the foundation for turning trusted context into governed action.

01. Fleks introduction Fleks extracts data and context from everyday interactions, structured and governed by your data models. Creating the trusted foundation for turning context into action. Everyday interactions. Your data models. Trusted context. Governed actions.

02. Meet Jordan. For a help desk analyst like Jordan, solving a support issue sometimes means knowing when to bring in a specialist. The handoff needs the symptoms, the work already agreed, and a clear next owner. Otherwise the user may have to tell the story again. Today, Avery, an operations coordinator at Northstar, is calling about an open ticket for repeated VPN disconnects.

03. The initial data model Jordan's team starts with interactions, people, organizations, tasks, and tickets. The plain language schema defines a ticket's description, status, and priority, plus task owners, due dates, and links to tickets. Jordan, Avery, Northstar, and the ticket already exist. Morgan, a network specialist, also has a person record. There is no escalation object in the team's initial model.

04. Support call Let us listen to part of the conversation. Jordan says: The VPN is still disconnecting after the standard checks? Avery says: Yes. I'll send connection logs tomorrow. Jordan says: I'll review them by Friday. This needs a specialist. I'm requesting an escalation of this ticket to Morgan, our network specialist, with the reason that the disconnects persist after the standard checks.

05. From transcript to proposed facts Fleks interprets the transcript using the adopted schema. It proposes a support-call interaction dated September second, twenty twenty-six. Avery's task is to send connection logs by September third. Jordan's task is to review them by September fourth. The existing ticket and known people remain references. The request for specialist help is retained in the source conversation.

06. Meaning comes from context When Jordan says he will review them, the conversation identifies the connection logs. Tomorrow and Friday become proposed calendar dates. Jordan names Morgan as the intended specialist and explains the reason for escalation. Those details give the handoff meaning. His request does not establish that Morgan has acknowledged it, and it does not say the ticket has been resolved.

07. A FleksScene captures context. The FleksScene connects Jordan and Avery to the support call, Avery to Northstar, and each follow-up task to its owner and the open ticket. Jordan can inspect the evidence beside the graph. The ordinary commitments fit the current model. The requested escalation is still described only in the conversation; it has no record of its own to connect.

08. Review the facts and their connections. Jordan checks the proposed tasks, dates, and owner relationships against the source, then accepts the reviewed facts and links. The ticket stays open. He still owns his promise to review the logs. The next step needs to capture a different responsibility: who requested specialist involvement, why it is needed, and to whom the request should be directed.

09. The data model can evolve. A follow-up task alone does not capture the escalation. Jordan wants a record of the requested handoff, with its reason, status, date, and recipient. He asks Fleks to propose an escalation object. This would connect the request to the ticket and preserve the context that Morgan will need, instead of burying that information in another ticket note.

10. A governed change to the data model. Fleks drafts an escalation object with a reason, status, and requested date. Relationships connect it to the ticket, the person who raised it, the intended assignee, and supporting tasks. Jordan submits the schema proposal to the service-management team. They review and adopt it. Defining this structure does not send a notification or establish that a specialist has taken responsibility.

11. The FleksScene gains connected context. Using the adopted model, Fleks proposes a requested escalation for the VPN ticket, raised by Jordan and assigned in the request to Morgan. It includes the reason and September second as the requested date, with links to the log tasks. Jordan reviews and accepts the record and its relationships. The escalation remains requested; acknowledgement and the actual handoff follow the team's process.

12. The conversation, connected. The FleksScene now connects the user conversation, open ticket, agreed tasks, and specialist escalation. Jordan can show who raised the request, who it is directed to, and the evidence behind it. Morgan has a clearer starting point when reviewing the handoff. The team gains continuity without confusing a recorded request with a completed escalation or a resolved incident.

13. The foundation for action Fleks extracts meaning from everyday interactions and structures it around your data models. As your needs evolve, your team governs changes to the data model and decides which facts to accept. Less time reconstructing conversations. Clearer context for what comes next. Fleks provides the foundation for turning trusted context into governed action.

01. Fleks introduction Fleks extracts data and context from everyday interactions, structured and governed by your data models. Creating the trusted foundation for turning context into action. Everyday interactions. Your data models. Trusted context. Governed actions.

02. Meet Elena. For a procurement officer like Elena, a purchase moves through supplier conversations, internal reviews, and commitments on both sides. The details rarely arrive together. She needs to understand the request, the agreement behind it, and who is responsible for the next step. Today, she is speaking with Leo, the account manager at Harbor Supply, about a requisition for warehouse scanners.

03. The initial data model Elena's team begins with interactions, people, organizations, tasks, and requisitions. A requisition has a description, status, and amount. A task has a description, status, and due date. Relationships identify participants, employees, owners, and the requisition that work concerns. The team defines its schema in plain language. Elena, Leo, Harbor Supply, and the scanner requisition already exist.

04. Supplier review Let us listen to part of the conversation. Elena says: What do we need before the scanner purchase can move forward? Leo says: We'll need a supply agreement. I'll send the draft by Friday. Our quote is for thirty thousand dollars. Elena says: I'll route the draft for internal review on Monday. This is still subject to approval. We have not agreed a signature date.

05. From transcript to proposed facts Fleks analyzes the transcript with the adopted schema. It proposes a supplier-review interaction dated September second, twenty twenty-six. Leo's task is to send the draft agreement by September fourth. Elena's task is to route it for review on September seventh. The quoted amount is proposed for the requisition, with its source visible. The existing people and organization remain references.

06. Meaning comes from context The draft means the supply agreement under discussion. Each speaker's commitment supplies a proposed owner, while Friday and Monday resolve against the meeting date. Thirty thousand dollars is a quoted amount, not an approved spend. The source explicitly says the purchase still needs approval. No signature date is supplied, and Fleks does not invent one to fill the gap.

07. A FleksScene captures context. The FleksScene connects Elena and Leo to the supplier review. Leo is an employee of Harbor Supply. Each follow-up task has an owner and concerns the scanner requisition. Elena can see the commercial conversation and the work it created together. The agreement is mentioned in the evidence and task descriptions, but the initial schema has no contract object to connect.

08. Review the facts and their connections. Elena checks the proposed tasks, dates, amount, and relationships against Leo's words and her own. She accepts the reviewed changes while keeping the requisition under review. An accepted fact that a supplier quoted an amount is not authority to place an order. The scene makes that distinction visible: record acceptance and purchasing approval are separate decisions with separate responsibilities.

09. The data model can evolve. Elena has seen agreements recur across earlier supplier conversations. A contract needs more structure than a mention in a task description. She asks Fleks to help propose a contract object, so the agreement can be connected to its supplier, the purchase it governs, and the work needed to move it forward. The extension stays focused on that missing object.

10. A governed change to the data model. Fleks drafts a contract object with a description, status, signature date, and a document attachment field. Relationships connect it to an owner, supplier, requisition, and related tasks. Elena submits the proposal for review. The procurement schema stewards adopt the change. Adding a contract type does not accept this particular agreement, supply a missing attachment, or grant anyone permission to sign.

11. The FleksScene gains connected context. Under the adopted schema, Fleks proposes a draft scanner supply agreement linked to Harbor Supply, the requisition, and the two follow-up tasks. Elena confirms herself as its owner and reviews the proposed connections. The signature date and attachment remain empty until supplied. She accepts the reviewed record. Earlier retained interactions can also be analyzed for contract references, generating new proposals rather than silently rewriting accepted history.

12. The conversation, connected. Elena now has the purchase, supplier, commitments, and draft agreement in one connected context. When the document arrives, it has a clear place to belong. Reviewers can trace the conversation behind the request and understand what is still pending. The team spends less time reconstructing the commercial story, while the authority to approve or sign remains where it belongs.

13. The foundation for action Fleks extracts meaning from everyday interactions and structures it around your data models. As your needs evolve, your team governs changes to the data model and decides which facts to accept. Less time reconstructing conversations. Clearer context for what comes next. Fleks provides the foundation for turning trusted context into governed action.

01. Fleks introduction Fleks extracts data and context from everyday interactions, structured and governed by your data models. Creating the trusted foundation for turning context into action. Everyday interactions. Your data models. Trusted context. Governed actions.

02. Meet Marcus. For a scrum master like Marcus, a team conversation can reveal a defect that deserves more than a passing mention. There is the observed behavior, the work to investigate it, and the sprint it affects. He wants that context to stay connected as the team decides how to respond. Today, Nina, a developer at Northstar, reports unexpected behavior in checkout.

03. The initial data model The team's initial model contains interactions, people, organizations, tasks, and sprints. A sprint has a name, goal, and status. Tasks have descriptions, statuses, and due dates, with links to their owners and the sprint they concern. The schema is defined in plain language. Marcus, Nina, Northstar, and the active checkout sprint already have records. Bug is not yet an object type.

04. Team coordination Let us listen to part of the conversation. Marcus says: What did you find while testing checkout? Nina says: When I change the quantity from one to two, the checkout total stays the same. I'll capture reproduction steps tomorrow. I'll take ownership of investigating this bug. Marcus says: I'll arrange triage by Friday so the team can decide severity and priority. Let's connect the bug to this sprint and our follow-up work.

05. From transcript to proposed facts Fleks analyzes the transcript against the adopted model. It proposes a team-coordination interaction dated September second, twenty twenty-six. Nina's commitment becomes a task to capture reproduction steps by September third. Marcus has a task to arrange triage by September fourth. The existing people, organization, and sprint remain references. Nina's description of the observed behavior stays attached to the source conversation.

06. Meaning comes from context Fleks uses the conversation to connect the reproduction steps to the checkout behavior Nina described. Tomorrow and Friday resolve against the meeting date. Nina volunteers to investigate the bug; Marcus promises to arrange triage. Severity and priority have not been decided. The interpretation preserves those distinctions so the team can review the actual report without adding an invented assessment.

07. A FleksScene captures context. The FleksScene connects Marcus and Nina to the conversation, Nina to Northstar, and both proposed tasks to their owners and the checkout sprint. The graph shows who committed to what and why the work belongs together. The defect itself is still present only in the evidence and task descriptions. It has no structured identity in the initial schema.

08. Review the facts and their connections. Marcus reviews the meeting and proposed commitments using the evidence panel beside the graph. He accepts the facts and relationships that match the conversation within the team's review process. Nina owns the reproduction work, and Marcus owns arranging triage. Accepting these records does not decide severity, alter the sprint goal, or mean the reported behavior has already been fixed.

09. The data model can evolve. The team needs to discuss the bug as something that persists while investigation and repair tasks change. A task describes work to do; a bug records the defect that motivates that work. Marcus asks Fleks to help propose a bug object. It should hold the description and status, with a place for severity once the team has assessed it.

10. A governed change to the data model. Fleks proposes a bug object with a description, status, and severity. Relationships connect a bug to an owner, the affected sprint, and related tasks. Marcus submits the change to the team's schema stewards, who review and adopt it. The structure is now available. The particular bug, its ownership, and its connections still need to be checked against the conversation.

11. The FleksScene gains connected context. Fleks now proposes a bug for the stale checkout total, linked to the sprint and the two follow-up tasks. Nina is the proposed owner because she volunteered to investigate it. The status is open, and severity is left unspecified pending triage. Marcus reviews and accepts the supported facts and relationships. The observed behavior now has a connected record of its own.

12. The conversation, connected. Marcus and the team can move from the bug to its owner, the sprint, the investigation work, and Nina's original report. Later conversations can add evidence and updates to that same context. The team has a clearer basis for triage and follow-through, while keeping decisions about severity and the sprint plan in its own hands.

13. The foundation for action Fleks extracts meaning from everyday interactions and structures it around your data models. As your needs evolve, your team governs changes to the data model and decides which facts to accept. Less time reconstructing conversations. Clearer context for what comes next. Fleks provides the foundation for turning trusted context into governed action.