Last reviewed August 15, 2026
Pharos Production delivers AI automation services that transform manual business processes into intelligent, self-optimizing workflows.
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Pharos Production partnered with Nextcheck to replace outdated, manual onboarding with a secure, automated KYC/AML platform.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.
Operations automation follows Pharos Verified Delivery with automation-specific gates: discovery maps the current process and baselines cost/cycle time; build combines rules + ML with a human review queue; production readiness measures accuracy against human baselines; support includes monthly accuracy reviews.
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 operations automations where rules and ML work together. Human review is always the last line of defense.
Manual ticket triage by a 6-person team. 18-minute average time to categorize. 14% miscategorization rate.
LLM-based triage with structured output and confidence thresholds. Triage time under 8 seconds, 97% accuracy, human review on low-confidence only. Team redeployed to resolution.
Low-confidence cases (below 85% model confidence) stayed in the human queue. We measured weekly against a held-out eval set; accuracy held steady across 9 months of production use.
Accounts payable team manually keyed invoices from 40+ vendors. 22 minutes per invoice average. 8% data entry error rate.
OCR + layout-aware model for extraction, rules for validation, human review on mismatches. Processing time down to 40 seconds per invoice, 99.1% accuracy post-review.
The rules layer catches impossible values (date in 2099, amount with wrong currency symbol) that the ML layer would happily accept. Rules + ML together beat either one alone.
Compliance team manually screened outbound communications for regulatory violations. 8% of messages reviewed; the rest unmonitored.
NLP classifier + rules with full coverage. 100% of messages scanned, 92% reduction in reviewer workload, 3x more actual violations caught.
Rules handle the deterministic cases (sanctions names, MNPI keywords); the classifier catches novel phrasings. Every flagged message has both a rule trace and a classifier confidence score for human review.
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:
Many automation requests are actually process-redesign requests in disguise. Before automating, we ask whether the underlying workflow even makes sense. Automating a broken process produces a broken process at higher throughput. Fix the process first, then automate.
Pharos AI automation portfolio
Ranges we consistently see across 20+ automation engagements.
55-75% of eligible workflows run straight-through after stabilization; remainder escalated to human approval via documented criteria.
6-12 weeks for mid-complexity agentic workflow with tool integration, observability and human escalation; includes 2-week monitored rollout[5].
$0.02-$0.25 typical per automated run depending on model size and tool count; measured and tracked per workflow[7].
8-15% of workflow deploys rolled back within first 30 days due to edge cases; shadow-mode rollout standard to minimize customer impact.
5-10% sampling of straight-through workflows for quality audit; higher on regulated or high-value workflow classes.
Three shifts are reshaping AI automation programs.
Large language model agents with tool access and retrieval-augmented context automate multi-step back-office workflows that previously required rigid RPA bots. Teams without agent tier automation face measurable cost and speed disadvantage[5].
Per-tool invocation tracing, prompt versioning and outcome labelling shift from optional to baseline. AI automation platforms without structured observability cannot debug production failures[11].
Risk-classified workflows default to agent-proposes, human-approves rather than full automation. Platforms without structured approval gates cannot operate in regulated environments[8].
Every AI automation engagement we ship runs against the same four-dimension readiness evaluation before handover.
Input, output, success metric, risk classification
Each automated workflow has documented input schema, output schema, measurable success metric and NIST AI RMF risk classification[8].
Blast radius, rollback, approval gates, idempotency
Per-workflow blast radius documented; rollback plan tested; approval gates for high-risk actions; idempotency enforced on external side effects[9].
Trace coverage, latency, drift, cost per run
Per-invocation tool trace with prompt and response logging; latency SLO per workflow; drift detection on input distribution; cost-per-run tracked[11].
Escalation criteria, audit trail, feedback capture
Documented escalation criteria per workflow; audit trail for every handoff; feedback from human reviewer captured as labelled data[10].
Production post-mortem
A procurement-automation agent we shipped in Q2 2025 had access to an ERP tool for product lookup. Under unusual input phrasing, the agent hallucinated a SKU that did not exist and attempted to add it to a purchase order. The tool invocation layer validated the SKU against the ERP before write, caught the hallucination, returned a structured error to the agent, and the agent re-prompted with the available SKU list. No invalid write occurred. The incident was logged and became a fixture in the safety-control test suite.
All external side effect tool invocations now require explicit pre-write validation against source-of-truth systems. Agent responses that fail validation are logged, not retried indefinitely. Human escalation triggers on third consecutive hallucination pattern per session.
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, AI discovery and PoC, Production AI system and Enterprise AI platform, with typical budgets from $15,000 to $500,000+ depending on scope and complexity.
Feasibility study, prototype on your data and integration roadmap in four to eight weeks.
Full model development, API layer, cloud deployment and MLOps with monitoring.
Multi-model architecture, custom data infrastructure, compliance and hybrid or on-prem delivery.
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.
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No. RPA (robotic process automation) typically means screen-scraping UIs to automate clicks. We do not build that - when a real API exists, API integration is always better than RPA. We build automations that combine API integration, ML for fuzzy decisions, rules for deterministic cases and a human review queue for edge cases.
When you have a high-volume repetitive decision with a measurable baseline of cost and cycle time, where humans are spending time on cases the machine can handle and where the business can define acceptable accuracy trade-offs. When one of those is missing, we recommend process redesign or workflow consolidation instead.
Every automation ships with a human review queue for low-confidence cases. Confidence thresholds are set during discovery based on the business cost of false positives vs false negatives.
High-volume low-stakes decisions (ticket categorization) can ship at 85% confidence auto-approval; high-stakes decisions (compliance flags, medical dosing) require 95%+ confidence or always-human-review.
Automation MVP 5-9 weeks: 1-2 weeks discovery and baseline measurement, 3-5 weeks build, 1-2 weeks production hardening. Production system with full monitoring 3-5 months.
The biggest variable is the discovery phase - understanding the current workflow properly is non-negotiable.
Automation MVP $25,000-$70,000. Production system $70,000-$180,000+.
Cost drivers: integration complexity, ML model training, human review queue design, audit logging requirements, compliance scope. Typical ROI payback 4-9 months from operations cost reduction.
We decline automations without a human review queue, RPA where real APIs exist, automations without a measured baseline and projects where the underlying process is broken. Automation on a broken process produces broken output faster.
AI automation rewards teams that treat safety controls and observability as first-class engineering, not afterthought. Pharos ships AI automation with documented blast radius, structured approval gates and tool-level validation before write[8].
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