Knowledge continuity
Experienced engineers held context that was difficult to transfer through static documentation alone.
Representative engagement 01 · Risk analytics software
Support knowledge assistant grounded in years of service history
A governed internal assistant designed to help support engineers find related incidents, ask better clarifying questions, and validate suggested resolutions against source material.
Why TemenTemen combines service operations, Microsoft Copilot engineering, information architecture, permission design, AI evaluation, and operational handoff. That matters when the challenge is not simply finding text, but determining whether prior evidence safely applies to the current case.Why nowEngineers spent valuable time reconstructing prior context, while a superficially similar historical case could lead to an unsafe or irrelevant remediation.
Case executive summary
A cited support assistant with the engineer still in control.
A software provider with years of distributed support history and a growing engineering team.
The source record defines the corpus, implementation phases, controls, evaluation method, and success criteria. A final benefits-realization report was not present, so operational value is described as designed value rather than a measured result.
- Outcome
- Knowledge continuity
- Engagement
- Advisory and project
- Artifact
- Retrieval, confidence, escalation, and verification workspace
01 What was the issue?
Institutional knowledge lived across service tickets, thousands of documents, archived email threads, screenshots, and diagnostic attachments. New engineers needed a safer way to reason across similar, but not identical, support issues.
Start with the situation itself. The technology request mattered, but so did the workflow, constraints, ownership, and condition the customer needed to change.
The support organization had accumulated valuable troubleshooting history, but the knowledge was distributed across ticketing and document repositories. Search could find words, but not reliably connect symptoms, environment details, prior actions, and verified resolutions.
The risk was not merely slow search. A superficially similar incident could lead an engineer toward the wrong remediation. The proposed system therefore had to retrieve evidence, explain uncertainty, preserve source visibility, and keep an engineer responsible for the final decision.
THE REAL ISSUEThe request could not be solved safely by selecting a tool alone. The operating path and the technical response had to be designed together.
02 What was the impact?
Engineers spent valuable time reconstructing prior context, while a superficially similar historical case could lead to an unsafe or irrelevant remediation.
The impact explains why the issue deserved action. It connects the technical problem to the people, customers, continuity, cost, risk, and ownership affected by it.
Resolution quality
Similar symptoms did not guarantee an identical cause, tenant, version, or safe next action.
Data boundaries
Internal support knowledge and customer-sensitive artifacts required different access and sanitization treatment.
Operational ownership
Administrators needed to maintain sources and thresholds after the initial build.
03 How did we solve it?
We built the knowledge, retrieval, evaluation, and ownership layers together.
Temen connected discovery, design, implementation, control, testing, and handoff. Each phase produced evidence that made the next decision safer and kept the customer's operating owner visible.
Discover and classify
Inventory sources, permissions, ticket taxonomy, attachment formats, sensitive-data concerns, and the questions engineers actually ask.
Evidence: Source inventory, access matrix, evaluation planNormalize the corpus
Extract and standardize useful text and metadata while preserving the link back to the original service record.
Evidence: Ingestion map, metadata schema, indexing-health recordGround the assistant
Configure retrieval, response instructions, citations, confidence behavior, Teams access, and escalation boundaries.
Evidence: Agent configuration, prompt controls, source citationsTest outside the training set
Withhold representative historical tickets, compare responses with known resolutions, and have senior engineers review usefulness and safety.
Evidence: Evaluation set, reviewer scoring, defect logTransfer and operate
Train administrators, document source maintenance, and establish the path for tuning or expanding the system.
Evidence: Admin runbook, knowledge transfer, change pathSee the solution logic at work
Inspect how a support question becomes a cited recommendation.
Choose a simulated incident. The workspace shows retrieval, evidence quality, clarifying questions, and the point where an engineer must decide.
Policy sync failure
Several managed endpoints stopped receiving the current security policy after a connector change.
No customer, tenant, or production data is used.KBEvidence appears hereRun retrieval to inspect the recommendation path.
This demonstration uses staged, synthetic data. It explains the solution pattern without connecting to a customer environment or reproducing private customer records.
How the solution connects
The components only matter when the operating path connects.
This view shows where information enters, what coordinates the work, where authority lives, and what the customer can continue operating after implementation.
Service historyApproved ticket fields, comments, and resolution records.
↓Document archiveWord records, email exports, images, logs, and extracted attachments.
↓Knowledge layerNormalized metadata, permissions, indexing, and retrieval.
↓Copilot StudioBounded response behavior, citations, confidence, and escalation.
↓Microsoft TeamsA familiar internal channel for engineer use and feedback.
How the work stays controlled
Useful work must also be reviewable work.
Controls define what the solution may do. Verification shows whether the intended behavior occurred and whether the customer is ready to own the result.
OPERATING CONTROLS01 02 03 04
Source-level access
Retrieval must respect the audience approved for each repository and content class.
Citation before confidence
The response exposes its evidence so the engineer can inspect the underlying record.
Human resolution authority
The assistant can recommend questions and next steps, but the engineer owns remediation.
Client-facing boundary
An unsanitized internal corpus is not repurposed for public or customer-facing answers.
VERIFICATION PLAN
Retrieval coverage
Confirm approved sources are indexed, current, permission-aware, and traceable.
Out-of-sample cases
Evaluate withheld incidents across common, ambiguous, and high-risk scenarios.
Source fidelity
Check that citations support the response and do not merely share similar words.
Administrator independence
Verify designated owners can maintain normal sources and thresholds without a developer.
04 What was the outcome?
A cited support assistant with the engineer still in control.
The designed solution turns approved service history into reviewable recommendations, clarifying questions, confidence signals, and source citations without granting the assistant production authority.
HOW TO READ THIS OUTCOMEThe source record defines the corpus, implementation phases, controls, evaluation method, and success criteria. A final benefits-realization report was not present, so operational value is described as designed value rather than a measured result.Faster path to relevant history
Engineers spend less time locating prior cases and more time validating applicability.
Cited, reviewable recommendations
A useful response includes the evidence and uncertainty needed for accountable judgment.
Maintainable knowledge system
The client team can add approved sources, monitor indexing, and request governed changes.
Clear scope boundaries
What this outcome did not quietly become.
Boundaries protect the customer from hidden assumptions, unapproved authority, and work that belongs in a different engagement.
×No autonomous production remediation×No public access to internal service history×No confidence claim without inspectable evidence×No silent expansion into restricted repositories
Real work, responsibly represented
Built from engagement evidence.
Customer identity, private infrastructure, personal information, commercial terms, and other sensitive details are deliberately excluded from this public narrative.
- Custom AI agent statement of work
- Knowledge-ingestion and sanitization scope
- Evaluation, UAT, and administrator-handoff criteria
Source-backed scope signalThe engagement record defines a corpus of thousands of support documents and attachments, source citation, out-of-sample testing, confidence calibration, and administrator handoff.
05 Why choose Temen for you?
Choose Temen when support knowledge needs AI without surrendering support judgment.
Temen combines service operations, Microsoft Copilot engineering, information architecture, permission design, AI evaluation, and operational handoff. That matters when the challenge is not simply finding text, but determining whether prior evidence safely applies to the current case.
Support logic before chatbot polish
The design starts with how engineers investigate, compare, validate, escalate, and close support work.
Knowledge boundaries by design
Approved sources, restricted content, citations, confidence, and human authority are part of the architecture.
Evaluation beyond demonstration cases
Withheld incidents test whether retrieval and recommendations remain useful outside familiar examples.
An operating model after launch
Administrators receive source-maintenance, tuning, threshold, and change paths instead of a permanent dependency on the project team.
THIS ENGAGEMENT PATTERN MAY FIT YOU IF
Your organization recognizes these conditions.
- You have years of valuable ticket or engineering history
- New team members struggle to find relevant precedent
- The source material includes sensitive or customer-restricted content
- You need inspectable evidence rather than a confident black box
Apply the pattern to your organization
Your issue deserves its own evidence, design, and outcome.
Bring the business impact, current workflow, constraints, environment, decision owners, and desired outcome. Temen will determine whether this engagement pattern fits your operating reality.