Last reviewed August 15, 2026
Pharos Production provides AI Governance consulting services that help organizations deploy artificial intelligence responsibly, transparently and in compliance with evolving regulations.
5.0/5 · 108 reviews
4.9/5 · 147 reviews
5.0/5 · 27 reviews
282 published client reviews across 3 platforms. Counts are summed; reviewers may appear in more than one source.
Profiles checked . Glassdoor: 46 employee reviews, 5.0/5, reported separately.
See the products we built and the engineering work involved. Each case describes its scope and delivery context.
Pharos Production has partnered with Pleenk to build a secure, scalable payments platform for fast transactions, fraud prevention and seamless integration with digital products. The platform processes payment flows in real time while maintaining high levels of security, transparency and reliability for both businesses and end users. Built on cloud-native infrastructure and an event-driven architecture, Pleenk provides a strong foundation for modern digital payments.
Pharos Production partnered with Nextcheck to replace outdated, manual onboarding with a secure, automated KYC/AML platform. Built on AWS, Kubernetes, Istio, Elixir, RabbitMQ, PostgreSQL and NextJS, the platform provides real-time biometric and document verification, risk assessment and compliance reporting. Since 2019, Nextcheck has reduced onboarding time by 60%, cut manual labor by 70% and expanded to support thousands of checks at once. Today, it powers global banks, FinTechs and crypto firms with a cloud-native, regulation-ready, growth-oriented compliance platform.
Pharos Production partnered with a healthcare organization to design and build MedCore, a comprehensive electronic health record platform that centralizes patient data, streamlines clinical workflows and ensures regulatory compliance. The system unifies medical records, clinical documentation, diagnostics and administrative processes within a secure, scalable digital environment. Built on a cloud-native architecture, MedCore delivers reliable performance, real-time data access and long-term scalability for healthcare providers operating at clinic, hospital and network levels.
Practice led 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.
Technically reviewed by Victor Sineglazov, D.Sc.
Independent AI Scientific Advisor
Professor, Artificial Intelligence Department, Igor Sikorsky Kyiv Polytechnic Institute. Head of the Aviation Computer-Integrated Complexes Department, Kyiv Aviation Institute.
Reviewed for technical accuracy on August 15, 2026. Not an endorsement of any commercial claim on this page.
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.
Week 0
2-4 weeks
82% on-schedule with discovery
Week 2-4
2-week sprints
Transparent progress tracking
Week 8-24
ISO 27001 aligned
Launch day
Ongoing
In progress
Continuous improvement
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.
NIST AI RMF levels mapped to system, legal jurisdiction
NIST AI RMF govern, map, measure, manage lifecycle applied; risk level documented per system; jurisdiction-specific overlays (EU AI Act, US executive order) mapped[8].
Model cards, datasheets, incident runbooks
Model cards published per production model; training data datasheets documented; incident runbook with rollback procedures and notification obligations[6].
Protected attribute evaluation, subgroup performance
Protected-attribute subgroup performance evaluated on holdout; disparity thresholds documented; remediation or accept-with-disclosure path defined[9].
Decision logging, human override, review cadence
Every model decision logged with input hash and version; human override pathway documented; quarterly governance review cadence scheduled[10].
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.
Technology platforms and company references shown for identification. A logo does not establish a commercial partnership or endorsement.
16 references
Consensys
Gate.io
Coinbase
Ludo
Core Scientific
Debut Infotech
Axoni
Alchemy
StarkWare
MARA Holdings
MicroStrategy
Nubank
OKX
Uniswap
Riot
LeewayHertz
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 scopes engagements in three tiers, Proof of concept, MVP build and Enterprise platform, with typical budgets from $10,000 to $500,000+ 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 $50 to $99 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.
We select the stack for your product, integrations and operating requirements.
Selected badges from Clutch, GoodFirms and The Manifest. Open the issuer profiles to review categories and dates.
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.
A real LLM observability cost model built from verified vendor pricing retrieved 2026-08-08, showing why Langfuse, LangSmith, Braintrust and Arize cannot be compared on list price alone and how the OpenTelemetry GenAI conventions' Development status quietly breaks naive cost attribution.
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.
A dated EU AI Act compliance timeline and applicability map after Regulation (EU) 2026/1744, covering what changed in Article 113, Article 6 and Article 50 and why EUR-Lex, the Commission's AI Act tool and the most-cited third-party tracker still show the pre-amendment rules.
What Article 50 of the EU AI Act actually requires for marking AI-generated content, the two mandatory layers, the transitional date hidden in Article 111(4) and the failure modes the Code of Practice admits marking cannot survive.
How to classify an AI system as high-risk under Article 6 and Annex III of the EU AI Act after the Digital Omnibus, with the new 1a to 1c carve-outs, a decision tree and worked examples.
Annex IV of the EU AI Act translated point by point into engineering deliverables, plus the logging and retention duties in Article 12, Article 19 and Article 26 and the unconfirmed SME simplified-documentation route.
What a general-purpose AI model provider owes regulators under Annex XI versus downstream integrators under Annex XII, where the open-source carve-out stops and why GPAI penalties sit in Article 101, not Article 99.
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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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