Reviewed by Dr. Dmytro Nasyrov, Founder and CTO
Pharos Production provides AI Governance consulting services that help organizations deploy artificial intelligence responsibly, transparently and in compliance with evolving regulations.
Aligned with these frameworks. Audit reports available on request.
Reviewed by Dmytro Nasyrov
Founder and CTO
23+ years in custom software development. Led 110+ projects across FinTech, healthcare, Web3 and enterprise, ISO 27001-aligned team.
Governance engagements follow a phased approach: discovery maps AI systems and risk categories; build implements controls (model cards, data lineage, audit logs, bias testing); production readiness delivers the first governance board review; support includes quarterly control audits and regulatory change monitoring.
Pharos Verified Delivery applied to 110+ production applications since 2013
Three AI governance engagements across regulated industries. Each surfaced a different control gap during the review.
Underwriting model deployed without formal documentation. EU AI Act high-risk classification triggered documentation requirements the client could not produce in a week.
Model cards, data lineage, bias testing and human-review flow documented to the regulator's satisfaction. Classified as compliant in the first review round. Governance board established for ongoing AI oversight.
We worked backwards from the EU AI Act Article 13 transparency requirements. Every existing deployment got a model card with the same structure; new deployments are blocked until the model card exists.
Clinical support model deployed with no bias testing across demographics. Client health system raised equity concerns before going live.
Bias testing across 9 demographic slices, with documentation of any disparity above 5%. Two disparities surfaced and corrected before production. HIPAA-compatible audit logging deployed across all model inference calls.
The bias testing is not a one-off - it runs nightly against a fixed demographic test set. Any disparity above threshold pages the governance board automatically.
Fraud detection model in production for 3 years with no model risk management (MRM) documentation. OCC examiner flagged the gap.
Full MRM package: model development documentation, validation report, ongoing monitoring plan, change management. OCC examiner cleared the finding at the next visit. Process now scales to new models.
MRM documentation was the deliverable; the process to produce it for future models was the real work. We built a template that the bank's model developers fill in at build time, not after regulators ask.
Client names anonymized under NDA. Full case studies at /cases/.
We decline roughly 30% of RFPs we receive. Forcing a bad fit costs both sides 3-6 months and damages outcomes. Here is how we think about scope:
Governance work that stops at policy documents fails during the first real incident. Effective governance means the controls are implemented in code - automated bias testing, audit logs on every inference, model cards generated from CI, explainability surfaced in the product UI. PowerPoint governance is theater; implementation-led governance is the actual work.
Pharos AI governance portfolio
Ranges we consistently see across 30+ governance engagements.
Typical 5 business days for risk classification, 3-4 weeks for first full documentation set (model card, datasheet, runbook).
Approximately 35-50% of engagements involve high-risk AI systems requiring full EU AI Act documentation scope[5].
Approximately 20-35% of reviewed models have at least one statistically meaningful subgroup disparity requiring mitigation or documented accept-with-disclosure.
Typical 10-30 business days from governance package delivery to procurement acceptance; faster than starting documentation from scratch at the procurement gate.
60-80% of clients retain us for quarterly governance reviews, incident response and regulation delta propagation post-baseline delivery.
Three shifts are reshaping AI governance engagements.
High-risk AI system classification, model cards and incident response procedures become binding EU regulation. Organizations without documentation processes cannot deploy regulated AI systems in EU markets[8].
Enterprise buyers require published model evaluation, bias testing and datasheet artifacts as contract deliverables. Vendors without structured governance documentation fail procurement review[6].
AI incident runbooks, rollback procedures and post-mortem discipline adopt patterns from cybersecurity incident response. Organizations without structured AI incident response accumulate silent failures[9].
Every AI governance engagement we ship runs against the same four-dimension readiness evaluation before handover.
Production post-mortem
A healthcare AI client engaged us in Q1 2025 for a governance review of a clinical decision-support model. Procurement had stalled because no structured classification existed. Our four-week review classified the system as high-risk under EU AI Act scope, produced a model card, documented bias testing results and published an incident runbook. The procurement team accepted the package in under 10 business days.
Governance baseline now leads with risk classification before model documentation, because classification determines which documentation is mandatory versus optional. Standard engagement scope includes 5 business day classification plus 3-4 weeks of documentation work.
Published record
Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.
Trusted by Coinbase, Consensys, Core Scientific, MicroStrategy, Gate.io and 10+ more Web3 and enterprise platforms
16+ partnersOur 16 technology partners include:
Founder and CTO Pharos Production
I design and build reliable software solutions - from lightweight apps to high-load distributed systems and blockchain platforms.
PhD in Artificial Intelligence, MSc in Computer Science (with honors), MSc in Electronics & Precision Mechanics.
13 years in architecture of great software solutions tailored to customer needs for startups and enterprises
23 years of practical enterprise customized software production experience
Lecturer at the National Kyiv Polytechnic University
Doctor of Philosophy in Artificial Intelligence
Master's degree in Computer Science, completed with excellence
Master's degree in Electronics and precision mechanics engineering
Pharos Production works in three engagement models, from a focused PoC to a production MVP to a full enterprise platform, with typical budgets from $10,000 to $400,000+ depending on scope and complexity.
Focused validation of your riskiest technical assumption with a working spike and a clear build-or-pivot recommendation.
Production-ready first version with core flows, real backend and the integrations to onboard first paying users.
Full-scale build with architecture, DevOps, QA, security and long-term evolution.
Prices vary based on project scope, complexity, timeline and requirements. Hourly rates range from $35 to $75 depending on role and seniority. Contact us for a personalized estimate.
Need extra hands on your software project? Our developers can jump in at any stage - from architecture to auditing - and integrate seamlessly with your team to fill any technical gaps.
Whether you're building from scratch or scaling fast, our engineers are ready to step in. You stay in control, and we handle the code.
From first line to final audit, we handle the entire development process. We will deliver secure, production-ready software, while you can focus on your business.
Our engineers work with 45+ ai technologies - chosen for production reliability and performance.
Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.
We'll create a complete software solution that is custom-made to meet your exact specifications.
Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.
AI governance is no longer optional. The EU AI Act enforcement began in 2025, with full compliance requirements taking effect across 2026. Organizations deploying AI systems in production face mandatory risk assessments, bias testing, transparency requirements and incident reporting obligations. This guide provides a practical framework for building responsible AI systems that meet regulatory requirements […]
Enterprise AI adoption in 2026 is at a tipping point. 78% of enterprises report using AI in at least one business function, but only 22% have successfully scaled AI beyond pilot projects, according to the McKinsey Global AI Survey (2024). The gap between experimentation and production deployment is where most AI initiatives fail - not […]
Synthesis of public benchmark data on production LLM costs, eval harness patterns, RAG vs fine-tuning economics, drift retraining cadence and pre-production eval gates - drawn from NIST AI RMF, Stanford AI Index, OWASP LLM Top 10 and named industry cohort.
OWASP Agentic Top 10 explained: the ASI01 to ASI10 taxonomy with real incidents and mitigations for AI agent risks, from a cybersecurity team.
AI agents that read messages, call tools and move money create a new fraud surface. A taxonomy of the attack patterns emerging against agent workflows, prompt injection, deepfake-assisted social engineering, memory poisoning, tool abuse, with the defenses that work, grounded in Gartner's 2025-2026 incidence data.
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An AI governance framework covers the policies, roles, controls and technical measures that manage AI risk across a model's full lifecycle - from data sourcing and training through deployment, monitoring and retirement. It defines who approves model changes, how bias is measured, what audit trails are maintained and how the organization demonstrates compliance to regulators or auditors.
Engagements map to the EU AI Act (risk tiers, conformity assessments and post-market monitoring obligations), NIST AI RMF (Govern, Map, Measure and Manage functions), ISO/IEC 42001 (AI management system standard) and sector-specific overlays including HIPAA for health AI and SR 11-7 / SS1/23 for financial model risk management.
Bias detection applies statistical fairness metrics - demographic parity, equalized odds and calibration - across protected attribute groups defined by your jurisdiction. Pharos builds automated bias evaluation pipelines that run on each model version and produce structured reports suitable for internal review boards and regulatory submissions.
Explainability techniques - SHAP, LIME, attention visualization and counterfactual explanations - make a model's predictions interpretable to human reviewers. The EU AI Act requires explanations for high-risk AI decisions affecting individuals (credit, hiring, medical diagnosis). GDPR Article 22 grants individuals the right to a meaningful explanation of automated decisions.
Data privacy controls span pseudonymization and anonymization of training sets, differential privacy for sensitive datasets, data minimization reviews to remove unnecessary PII and contractual data processing agreements with model API providers. For inference, PII detection layers can redact sensitive fields before data reaches a third-party model endpoint.
Audit trails record model version, training data lineage, evaluation metrics at release, changes to prompts or hyperparameters, production inference logs and human override events. Retention periods depend on regulation - the EU AI Act mandates at least 10 years for high-risk systems; financial sector requirements typically follow SR 11-7 model inventory rules.
A focused gap assessment against NIST AI RMF or EU AI Act takes 4 to 6 weeks and delivers a prioritized remediation roadmap. Full framework implementation - policy authoring, tooling integration, staff training and audit-ready documentation - typically runs 12 to 20 weeks depending on the number of models in scope and existing data governance maturity.
We decline compliance-theater engagements, governance without engineering implementation capacity, projects without leadership commitment, governance triggered only by regulator notice and one-time audits with no ongoing monitoring plan. Governance that stops at PowerPoint fails during the first real incident.
We implement controls that satisfy EU AI Act technical requirements (Articles 9-15 for high-risk systems). Legal classification of whether a system is high-risk, prohibited or limited-risk is a legal question for qualified counsel - we do not provide that opinion. Once classification is known, we implement the required technical controls.
Governance rewards teams that treat AI risk as architecture, not paperwork. Pharos leads with NIST AI RMF classification, structured model cards and documented incident response, and declines engagements where the goal is theatre rather than measurable risk reduction[8].
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