Meraat works as part of your team, helping you connect your organization’s knowledge to intelligent tools and create value securely.
“Meraat” means mirror in Persian, and the name sums up how we approach consulting: a clear, honest picture of where an organization stands today, and a practical path to where it wants to be.
We see three connected disciplines together: the knowledge an organization builds, the intelligence it can draw from that knowledge, and the security that protects both. Our focus is on solutions designed and delivered around each organization’s real context.
49% of 164 participants in APQC’s 2026 survey named AI and smart technology a top KM priority.
Source: APQC · 2026-01-07In APQC’s 2025 survey of 1,000 respondents, only 30% reported doing this most of the time or always.
Source: APQC · 2025-08-20Stanford AI Index 2026, reporting 2025 data; use does not imply proven ROI or enterprise-wide deployment.
Source: Stanford HAI — AI Index 2026 · 2026-04-13 (report release)Within Verizon’s 2026 DBIR dataset, using 2025 data; not an individual organization’s probability of a breach.
Source: Verizon — DBIR 2026 · 2026-05-19Statistics are findings from named studies, not promised Meraat outcomes or Iran-specific estimates.
From assessment and strategy to implementation and team enablement.
Knowledge creates value when it improves the next decision, bid or project. Meraat approaches knowledge management as a working system, not a file repository: identify critical know-how, retain expert experience, organize trusted content and put it in the right hands at the right moment. We use ISO 30401 principles and measures such as time to find an answer, lessons reused and time to proficiency for new staff.
Assess maturity, interview stakeholders and prioritize knowledge risks; deliver a knowledge map, accountable owners and a phased action plan.
Use structured expert interviews, decision narratives and transfer plans before role changes or retirement, with review and approval by the knowledge owner.
Design after-action reviews and learning checkpoints; turn experience into guidance, checklists and corrective actions with named owners.
Define taxonomy, metadata, content ownership, review cycles and access permissions so users can distinguish approved knowledge from drafts and obsolete material.
Design a retrieval-augmented generation pilot on selected documents, with source citations, user-level permissions and accuracy tests; uncertain answers should be flagged and escalated to an expert.
Build communities of practice and knowledge-champion networks, provide role-based training and track participation, reuse and impact on selected processes.
In APQC’s 2026 survey of 164 participants, 49% named AI and smart technology a top KM priority. Our implication: design knowledge quality and ownership alongside tool selection.
Source: APQC · 2026-01-07In APQC’s 2025 survey of 1,000 respondents, only 30% reported capturing departing retirees’ knowledge most of the time or always. Knowledge transfer should begin before departure and within everyday work.
Source: APQC · 2025-08-20In the barriers question of APQC’s 2026 survey, 40% of 162 respondents said their culture did not incentivize knowledge sharing and reuse. Roles, incentives and leadership support belong in the program.
Source: APQC · 2026-01-07Effective AI starts with a business problem, not a model. Meraat helps leaders prioritize valuable use cases, assess data and process readiness, and move from a testable pilot to controlled deployment. Governance, security, human oversight and real operating costs are designed in from the start, using NIST AI RMF and ISO/IEC 42001 principles in the context of each organization.
Identify business problems, assess data and skills, and rank use cases by value, risk and feasibility; deliver a roadmap, scenario-based budget and business owner for each use case.
Create an AI inventory, acceptable-use policy, accountabilities, risk register and approval process aligned with NIST AI RMF and ISO/IEC 42001; this is advisory work, not certification.
Set a baseline, Persian and domain-specific test sets, acceptance thresholds and cost limits; assess quality, time, errors and value before deciding to scale.
Design document assistants, analytics and agentic workflows with least privilege, activity logs and human approval for sensitive actions; integrate with existing systems in stages.
Compare models and cloud or on-premises architectures against quality, confidentiality, availability and cost; design monitoring for quality changes, versions, incidents and fallback procedures.
Provide role-based learning for leaders, practitioners and technical teams, focused on approved uses, answer verification, information protection and responsible work redesign.
Stanford’s 2026 AI Index reports AI use in 88% of surveyed organizations in 2025. This is not evidence of proven profitability or enterprise-wide deployment; each use case still needs evaluation.
Source: Stanford HAI — AI Index 2026 · 2026-04-13 (report release)The 2026 AI Index reports generative AI use in at least one business function at 70% of surveyed organizations. Acceptable-use policies, data protection and answer-quality checks should grow alongside employee access.
Source: Stanford HAI — AI Index 2026 · 2026-04-13 (report release)On April 7, 2026, NIST released a concept note for a trustworthy-AI profile for critical infrastructure. It is not final guidance; for industry and energy, safety, human oversight and failure scenarios should be examined before scaling.
Source: NIST · 2026-04-07 (critical-infrastructure concept-note release)Cybersecurity is not just a tool purchase or an audit milestone; it is the ability to keep operating through disruption. Meraat translates information and operational risks into management decisions: what is critical, what to fix first, who is accountable and how recovery will be tested. We use NIST CSF 2.0 for the roadmap and ISO/IEC 27001:2022 principles for security management, tailored to the environment, data sensitivity and applicable requirements.
Identify critical assets and services, assess gaps and map current and target profiles using NIST CSF 2.0; deliver a prioritized plan with action owners and evidence of progress.
Define the ISMS scope, risk register, treatment plan, policies and measures; prepare evidence for management review and independent assessment against ISO/IEC 27001:2022.
Prioritize internet-exposed weaknesses, configuration fixes and privileged access controls; build remediation plans around asset criticality and threat evidence rather than alert counts.
Develop incident scenarios and playbooks, run tabletop exercises and test backup restoration; assign responsibilities and recovery objectives that reflect business processes.
Assess suppliers by service criticality, review contractor access and recommend contractual security requirements; track shared risks throughout the engagement.
Assess data flows and access, prompt-injection and leakage scenarios, dependencies and logging; technical testing requires written authorization and an agreed scope.
In Verizon’s 2026 DBIR dataset, vulnerability exploitation was the starting point for 31% of breaches. The practical priority is visibility of exposed assets and fast remediation of high-risk weaknesses; this is not an individual company’s breach probability.
Source: Verizon — DBIR 2026 · 2026-05-19The 2026 DBIR reports third-party involvement in 48% of analyzed breaches. Contractor access and service dependencies should be managed throughout the risk lifecycle, not only during procurement.
Source: Verizon — DBIR 2026 · 2026-05-19NIST’s preliminary Cyber AI Profile separates three focus areas: securing AI systems, AI-enabled cyber defense and countering AI-enabled attacks. This distinction helps clarify responsibilities; the document is not final.
Source: NIST · 2025-12-16 (preliminary draft)A clear, step-by-step path from understanding to results.
Conversations and a careful assessment of your current state and goals.
A strategy and roadmap tailored to your resources and priorities.
Step-by-step implementation alongside your teams.
Measuring outcomes, transferring knowledge and improving continuously.
Insights are Meraat editorial summaries of external sources, not proprietary Meraat research.
APQC’s 2026 survey puts AI and smart technology at the top of KM teams’ priorities, while 59% of respondents place their programs in the two earliest maturity stages. The practical implication is to improve content ownership, knowledge flows and employee participation alongside any AI pilot.
Source: APQC · 2026-01-07APQC’s October 2026 article highlights cross-functional knowledge flows, trusted knowledge and structured expertise transfer as foundations for organizational transformation. The practical takeaway is to embed KM in workflows and AI-enabled systems rather than isolate it as a documentation exercise.
Source: APQC · 2026-10-06A study in Strategic Management of Organizational Knowledge surveyed 150 managers and experts in Markazi Province government offices and reported positive associations between AI applications, knowledge processes and aspects of employee performance. Its correlational design and snowball sample do not establish causation or guarantee results across organizations.
Source: فصلنامه مدیریت راهبردی دانش سازمانی / Strategic Management of Organizational Knowledge · پاییز ۱۴۰۴ / Autumn 2025 (volume 8, issue 3)Stanford reports AI use in 88% of surveyed organizations in 2025, while AI-agent deployment remains in single digits across nearly all business functions. For leaders, distinguishing experimentation, pilots and sustainable operations matters more than following each new tool launch.
Source: Stanford HAI — AI Index 2026 · 2026-04-13 (report release)On April 7, 2026, NIST released a concept note for a trustworthy-AI profile for critical infrastructure. It is a guidance-development step, not a final requirement; for industrial organizations, it highlights the need to examine failure consequences, human authority and stop conditions.
Source: NIST · 2026-04-07 (critical-infrastructure concept-note release)On October 26, 2025, Digiato reported that parliament approved the article concerning a national AI organization under the presidency while considering the national AI development proposal. This report alone does not establish final enactment or today’s implementation status; organizations should check the latest official text and sector requirements before compliance decisions.
Source: دیجیاتو / Digiato · ۴ آبان ۱۴۰۴ / 2025-10-26Verizon’s 2026 report, using 2025 data, identifies vulnerability exploitation as the starting point for 31% of breaches and third-party involvement in 48% of analyzed breaches. These findings support exposure management, high-risk remediation and supplier-access controls; the percentages should not be added together.
Source: Verizon — DBIR 2026 · 2026-05-19In its October 2026 report commentary, Microsoft connects agent security to identity, access to data and tools, attribution and the ability to revoke access. Organizations should assess more than the model itself: connections to business systems and experienced security judgment must remain part of the design.
Source: Microsoft Security · 2026-10-01ICTNA’s report of an ISNA interview with the head of AFTA identifies social engineering and supply-chain threats among the trends relevant to Iran. This is an attributed official assessment, not independent incident statistics; its practical implication is to connect ongoing training with system updates and response readiness.
Source: ایستنا، به نقل از گفتوگوی ایسنا / ICTNA, reporting an ISNA interview · ۲۰ مرداد ۱۴۰۴ / 2025-08-11Start with an important, measurable problem, such as slow document retrieval, loss of expert knowledge or risk to a critical service. An initial assessment identifies dependencies and priorities before a technical solution is chosen.
A limited scope can be defined around selected sources and accountable owners. Before the pilot, check quality, authoritative versions, permissions and permitted data use; cleaning all enterprise data is not a prerequisite, but ignoring it is not a solution either.
Define data classification, least-privilege access, supplier terms, processing and retention locations, and logging before integration. Cloud versus on-premises deployment depends on sensitivity and organizational requirements; absolute confidentiality cannot be promised without assessment.
Agree a baseline and acceptance criteria before implementation: time to find an answer, knowledge reuse, answer quality on domain tests, task completion time or recovery-test results. Savings and ROI are calculated from actual evidence and full costs, not guaranteed percentages.
No. These references inform design and assessment; citing them does not claim certification for Meraat or the client. Where needed, certification to certifiable standards such as ISO/IEC 27001 or ISO/IEC 42001 must come from an independent, competent certification body; NIST AI RMF is a voluntary framework.
Include the main problem, desired outcome, process or business-unit scope, existing systems, security constraints and preferred timeframe. Do not include confidential documents, passwords or sensitive personal data in the initial inquiry; agree a secure exchange method afterward.
Choose the path that matches where your organization is today, or we can design a combination together.
For organizations that want a clear picture of their current state and priorities in knowledge management, AI and security before investing.
From problem definition to deployment and handover — one team, a clear path and agreed acceptance criteria.
Ongoing support as part of your team, for better decisions, progress tracking and continuous improvement.
Reach out for an initial consultation or to learn more about our services.
