Human judgment and proportionality
iPulse AI supports research and comparison. It does not execute trades, connect to brokers, or replace a user's judgment, risk limits, or qualified professional advice.
Responsible AI policy
Responsible AI and AI Ethics Policy
This policy explains how Future Edge Group FZE governs the design, operation, evaluation, and public communication of AI within iPulse AI, an Open Agentic Investment Research Platform.
Version 1.0Effective August 1, 2026Owner: Future Edge Group
Policy status: This is a public company policy and operating commitment. It is not an external audit, government approval, safety guarantee, or certification against NIST, ISO, OECD, UAE, or any other framework.
Purpose and scope
Responsible AI must be specific to the product and its users
This policy applies to AI capabilities designed, developed, configured, evaluated, operated, or publicly documented by Future Edge Group for iPulse AI. It covers model-assisted market research, structured forecast generation, independent AI Agent perspectives, consensus and disagreement analysis, risk interpretation, public methodology, and related internal development workflows.
It also applies when third-party foundation models, cloud services, data providers, or development tools form part of the system. Using an external provider does not transfer Future Edge Group's responsibility for the way a capability is designed, presented, monitored, or constrained.
Policy commitments
Eight commitments guide iPulse AI development and use
Accountability and governance
Future Edge Group remains accountable for product design, documented AI boundaries, risk decisions, material corrections, and periodic review of this policy.
Transparency and explainability
We expose public methodology, structured forecast fields, evidence context, advisor perspectives, uncertainty, disagreement, and limitations wherever these can be shared responsibly.
Validity and reliability
AI outputs are treated as fallible. We use schema validation, consistency checks, comparative analysis, versioned methodology, historical records, and evidence-led evaluation instead of claiming guaranteed accuracy.
Safety, security, and resilience
We apply managed cloud controls, authentication, authorization, least privilege, environment separation, monitoring, and protected disclosure boundaries appropriate to the product and its risks.
Fairness and harmful-bias management
We assess whether data, model behavior, prompts, analytical personas, and presentation choices could create misleading or systematically harmful outcomes, while recognising that bias cannot be eliminated by policy wording alone.
Privacy and data minimization
The current product does not require broker credentials, bank-login details, private portfolio holdings, or automated trading authority. We limit personal-data processing to what is needed to operate, secure, support, and improve the service.
Contestability and improvement
Users can question an output, report a concern, or request a correction. Material issues are assessed, corrected on the affected public surface where appropriate, and used to improve controls and documentation.
Financial research boundary
AI output is evidence to inspect, not an instruction to trade
iPulse AI produces educational market intelligence and decision-support research. Forecasts, ratings, Top Picks, consensus scores, risk signals, and AI Agent reports are model-generated research outputs. They are not personalized financial, investment, legal, tax, accounting, or brokerage advice.
AI models can be wrong, stale, inconsistent, overconfident, or sensitive to incomplete data and prompt framing. iPulse AI therefore compares independent perspectives, preserves structured assumptions and risk fields, surfaces disagreement, and publishes methodology rather than presenting a single model answer as objective truth.
The current product does not place trades, connect to brokerage accounts, request bank-login details, or require users to upload private portfolio holdings. See the investment-research disclaimer for the detailed interpretation boundary.
Lifecycle governance
Govern, map, measure, and manage AI risk continuously
Our operating approach is informed by the NIST AI Risk Management Framework's Govern, Map, Measure, and Manage functions. We apply those ideas proportionately to iPulse AI's current scale, architecture, and investment-research context.
Govern
Set ownership and boundaries
Define intended uses, prohibited uses, responsible owners, data and model dependencies, review expectations, and the level of evidence required before making a public claim.
Map
Understand context and potential harm
Assess who may rely on a feature, how financial research could be misunderstood, which data and providers influence the result, and where human review or stronger warnings are necessary.
Measure
Test outputs and controls
Use validation, comparative model views, forecast dispersion, consistency checks, risk signals, historical evidence, monitoring, and targeted review to identify failure modes and uncertainty.
Manage
Respond, correct, and retire
Prioritize risks, constrain or pause problematic capabilities, correct material public errors, update methodology, review provider changes, and retire features that cannot be operated responsibly.
Models, data, and evaluation
Third-party models remain governed dependencies
iPulse AI may use multiple foundation models and data services. Provider reputation alone is not evidence that every output is reliable or appropriate. Model behavior, availability, safety controls, terms, and capabilities can change; material provider or configuration changes should therefore be reviewed and reflected in product controls or public methodology where appropriate.
Evaluation may include schema validation, date and horizon checks, forecast-path consistency, rating logic, comparative advisor analysis, dispersion, risk indicators, evidence review, historical outcome analysis, and targeted human inspection. No individual control proves that an output is correct. Controls work together to reduce risk and make limitations easier to detect.
We do not publish licensed data, private prompts, confidential provider terms, personal data, secrets, or security-sensitive production details merely to appear transparent. The data provenance methodology explains the public lineage boundary.
Acceptable use
Uses that require caution or remain outside the product boundary
- Do not treat an AI forecast, score, ranking, or summary as guaranteed or personalized investment advice.
- Do not use iPulse AI to manipulate markets, misrepresent evidence, facilitate fraud, or evade legal obligations.
- Do not use research outputs as the sole basis for consequential decisions affecting another person's rights, access, employment, credit, health, or legal status.
- Do not enter secrets, broker credentials, bank-login details, unlawful content, or unnecessary sensitive personal information into product or support fields.
- Independently verify material facts and seek qualified professional advice when a decision has significant financial, legal, tax, or personal consequences.
Incidents and corrections
Concerns should lead to review, correction, and learning
Users, researchers, and partners can report a suspected harmful output, factual error, privacy concern, security issue, misleading claim, or policy question by emailing support@ipulseai.com.
Reports are prioritized according to potential impact. Appropriate responses may include clarification, correction, additional warning text, configuration change, access restriction, provider review, temporary pause, feature retirement, or an update to this policy and the related methodology.
Reference frameworks
The policy is informed by public responsible-AI guidance
This policy is informed by the UAE AI Ethics Principles and Guidelines, the NIST AI Risk Management Framework, the NIST Generative AI Profile, and the OECD AI Principles. These references help structure our thinking; they do not imply certification or full conformity assessment.
Ownership and review
A public policy must remain current
Policy owner: Future Edge Group FZE
Version: 1.0
Effective date: August 1, 2026
Review cadence: At least annually and after material change
Responsible reviewer: Russlan Ramdowar, Founder and Hands-On Technical Architect
Material changes to the product, model architecture, data practices, risk profile, legal requirements, or external guidance may trigger an earlier review. The effective date and version will be updated when the policy changes materially.