Reviewed by

NLP Development Services

Pharos Production delivers custom Natural Language Processing (NLP) development services that help machines understand, generate and act on human language.

SOC 2 GDPR Compliant ISO 27001 HIPAA-ready
  • 25+ AI projects delivered
  • 90+ engineers
  • 96 Clutch reviews
96 reviews 138 reviews 11 reviews 54 reviews 19 reviews 5.0 318+ verified reviews

Your business results matter

Achieve them with minimized risk through our bespoke innovation capabilities

Your contact details
Please enter your name
Please enter a valid email address
Please enter your message
* required

We typically reply within 4 hours. Prefer email? [email protected]

SOC 2 Type II GDPR ISO 27001 NDA Protected

Aligned with these frameworks. Audit reports and certifications available on request.

Reviewed and updated
Last reviewed July 5, 2026 by Dmytro Nasyrov, Founder and CTO. Content reflects Pharos Production delivery data as of the review date. Editorial policy.
Dmytro Nasyrov - Founder and CTO of Pharos Production

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.

What is NLP development?

Natural language processing (NLP) development is the engineering of systems that understand, extract and generate structured information from human language. It covers classification (sentiment, intent, category), named entity recognition, information extraction, document understanding, search and retrieval, summarization, translation and text generation. Modern NLP combines classical techniques (tokenization, embeddings, graph-based algorithms) with transformer-based models (BERT, RoBERTa, T5) and large language models (GPT, Claude) for fuzzy reasoning. Pharos has shipped NLP pipelines for document extraction, search relevance, customer support routing, compliance monitoring and content classification since 2020.
Authoritative citations 12 sources
  1. Stanford AI Index The Stanford AI Index tracks multi-year movement on ML benchmarks, training compute, responsible AI metrics and enterprise adoption across industries, making it the most cited yearly reference for grounding ML investment cases. aiindex.stanford.edu
  2. Papers With Code Papers With Code maintains live state-of-the-art leaderboards for ML tasks across image classification, object detection, NLP and tabular prediction, which we use to pick baselines before committing to a model family. paperswithcode.com
  3. arXiv, Chen and Guestrin 2016 The XGBoost paper by Chen and Guestrin remains the most cited gradient boosting reference and underpins tabular ML baselines we still ship in FinTech and logistics systems a decade after publication. arxiv.org
  4. arXiv, LightGBM Microsoft Research LightGBM introduced leaf-wise tree growth and histogram-based splits, giving lower latency and memory footprint than XGBoost on wide tabular data, which is why our fraud detection stack defaults to it. arxiv.org
  5. McKinsey State of AI McKinsey documents annual enterprise ML adoption across functions like marketing, service operations and supply chain, and consistently reports that scaled ML correlates with higher EBIT contribution versus pilot-only organizations. mckinsey.com
  6. Gartner AI Hype Cycle Gartner maps enterprise ML techniques across the hype cycle phases, flagging which capabilities are production-ready for mid-market adoption versus still speculative, which we cross-check before recommending a build path. gartner.com
  7. IDC Worldwide AI Spending Guide IDC publishes the worldwide AI spending guide with multi-year forecasts by industry, use case and geography, which we reference when sizing three-year total cost of ownership for ML platform engagements. idc.com
  8. NIST AI Risk Management Framework The NIST AI RMF defines a govern, map, measure and manage lifecycle for AI systems that we apply to production ML including model cards, bias testing and incident response procedures for regulated deployments. nist.gov
  9. OWASP ML Security Top 10 OWASP maintains a ranked list of the top machine learning security risks including input manipulation, training data poisoning, model theft and adversarial attacks, which we use as a threat model checklist before exposing any ML endpoint. owasp.org
  10. O'Reilly AI Adoption in the Enterprise The O'Reilly AI adoption survey tracks ML maturity stages across enterprises, reporting on deployment percentages, skills gaps and the most common production blockers which consistently include data quality and monitoring rather than model choice. oreilly.com 2022
  11. Google Cloud MLOps Architecture Google Research published the canonical MLOps continuous delivery reference describing three maturity levels from manual to fully automated pipelines, which we use as the template for client MLOps roadmaps and capability gap assessments. cloud.google.com
  12. PyTorch Blog The PyTorch engineering blog tracks the 2.x production tooling surface including torch.compile, TorchServe updates and quantization workflows, which shape our default serving stack for sub-50ms p99 inference on GPU and CPU targets. pytorch.org
What we do not do
  • Search use cases where Elasticsearch or Typesense would deliver better relevance at lower cost
  • Structured classification with enough labeled data that traditional ML wins
  • Projects without a labeled evaluation set for the core task
  • Real-time systems with sub-50ms latency budgets for long-text processing

NLP development at Pharos Production at a glance

  • NLP systems shipped: 12+ production NLP systems since 2020 (document extraction, search relevance, compliance monitoring, intent classification, content moderation)
  • Stack: PyTorch, Hugging Face Transformers, spaCy, sentence-transformers, BGE, E5, Cohere, OpenAI embeddings, LayoutLMv3, Ray, MLflow
  • Approaches: Classical rules + gradient boosting, fine-tuned transformers (BERT, RoBERTa, DeBERTa), LLM few-shot, hybrid systems combining all three
  • Deployment: TorchServe, Triton, BentoML, custom FastAPI services with batching, quantization for edge deployment
  • Pricing: NLP MVP $30,000-$80,000; production system $80,000-$250,000+; retainers from $5,000/month
  • Timeline: Discovery 2-3 weeks; MVP 6-12 weeks; production with MLOps 3-8 months
  • Languages: English primary; Spanish, French, German, Russian, Arabic, Mandarin supported with appropriate pre-trained models
  • Honest scope: We recommend search indexes or classifiers when they fit and decline NLP without a labeled eval set

Classical NLP vs LLM-based approach: which is better?

Classical NLP (fine-tuned transformers, gradient boosting, rules) wins on narrow tasks with enough labeled data, low latency requirements and cost-sensitive workloads. LLM-based approaches (prompt engineering, RAG, few-shot) win on fuzzy reasoning, zero-shot tasks and complex instruction following. Most production NLP systems combine both: classical models for hot paths, LLMs for edge cases.

Factor Classical NLP LLM-based approach
Input type Structured text, narrow domain, classification or extraction Unstructured conversation, fuzzy reasoning, instruction following
Training data Requires thousands to tens of thousands of labeled examples Works with zero-shot or few-shot examples
Latency Sub-50ms typical; suitable for real-time 0.5-5 seconds typical; not for sub-second requirements
Cost per request Near-zero marginal cost once trained $0.001-$0.05 per call; adds up at scale
Determinism Deterministic (same input → same output) Non-deterministic unless temperature=0
Accuracy ceiling Very high on narrow tasks with enough training data Very high on fuzzy tasks with good prompting
Development time 6-12 weeks for a production MVP 2-6 weeks for an LLM-based MVP
Best fit Document extraction, search relevance, compliance, content moderation at scale Complex Q&A, conversation, content generation, instruction following

Classical NLP meets LLMs in production

NLP projects follow Pharos Verified Delivery with NLP-specific gates: discovery defines task, labeling strategy and eval metric; build evaluates multiple approaches (classical, fine-tuned transformer, LLM-based) against a held-out test set; production readiness covers serving infrastructure, monitoring, drift detection and retraining cadence; support includes monthly eval refresh and production metric reviews.

Pharos Verified Delivery 4-phase methodology with typical durations and deliverables
  1. Phase 01 / 04

    Paid Discovery

    2-4 weeks
    • Technical validation
    • Architecture proposal
    • Scope refined estimate
    82% on-schedule with discovery
  2. Phase 02 / 04

    Iterative Build

    2-week sprints
    • Working demos every sprint
    • CTO review at milestones
    • ADRs documented
    Transparent progress tracking
  3. Phase 03 / 04

    Production Readiness

    • Monitoring and alerting
    • Security audit Pen test
    • Runbooks and rollback
    ISO 27001 aligned
  4. Phase 04 / 04

    Support

    Ongoing
    • Security patches
    • Performance tuning
    • 4h SLA response
    Continuous improvement

Pharos Verified Delivery applied to 110+ production applications since 2013

NLP systems in production

Three NLP engagements where the hybrid classical + LLM approach moved the accuracy needle.

Document extraction pipeline Q1 2025 · Insurance carrier, EU
Before

Claims processors manually extracted 40+ fields from scanned documents. 22 minutes per claim. 8% data entry error rate from fatigue.

After

Hybrid pipeline: OCR (AWS Textract) + layout-aware transformer (LayoutLMv3) for field extraction + rules for validation. 99.2% extraction accuracy after human review layer. Processing time under 35 seconds per claim.

The key was combining layout-aware NLP with deterministic validation rules. Pure OCR was 78% accurate; adding the LayoutLMv3 model on top reached 94%; adding business rule validation (dates must be valid, amounts must sum) reached 99.2%.

Search relevance Q3 2024 · E-commerce marketplace, global
Before

Keyword-only search using Elasticsearch defaults. Search abandonment rate 38%. Zero-result searches on 12% of queries despite relevant products existing.

After

Hybrid BM25 + dense retrieval with a fine-tuned BGE embedding model + Cohere reranker. Search abandonment dropped to 14%. Zero-result rate down to 2.8%. Click-through rate on search results up 46%.

We did not replace Elasticsearch - we added a second retrieval layer. BM25 handles exact matches; dense retrieval catches semantic matches the keyword layer missed. The reranker combines the two and orders results. Both layers run in parallel so total latency stayed under 120ms.

Compliance monitoring Q2 2025 · FinTech compliance, US
Before

Compliance team manually reviewed outbound communications for regulatory violations. 4 full-time analysts. 8% of messages reviewed; the rest went unmonitored due to volume.

After

NLP pipeline flagging high-risk messages with explainable rules + classifier. 100% of messages scanned, 92% reduction in reviewer workload, 3x more actual violations caught.

Rules + classifier is the right combination for compliance. Rules handle the deterministic cases (sanctions names, MNPI keywords, specific phrase patterns); the classifier catches novel phrasings rules miss. 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/.

When custom NLP is not the answer

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:

Projects we decline
  • Search use cases where Elasticsearch or Typesense would deliver better relevance at lower cost
  • Structured classification with enough labeled data that traditional ML wins
  • Projects without a labeled evaluation set for the core task
  • Real-time systems with sub-50ms latency budgets on long text
  • "NLP for the sake of NLP" projects without a measurable business metric
We recommend the simpler tool when it fits

NLP shines on fuzzy text tasks with unstructured inputs. For high-volume exact-match search, Elasticsearch or Typesense dominates on latency and relevance. For structured classification with enough training data, traditional ML (gradient boosting) outperforms deep NLP at 10x lower cost. For simple intent routing, rules cover 80% of cases. We start every NLP engagement by asking "could this be a search index, a classifier or a rules engine?"

Pharos original research

AI architecture background reading

State of AI Development Costs 2026 Pharos research on AI project costs including NLP pipelines, document processing and the crossover point between classical NLP and LLM approaches. Continue reading

Pharos NLP portfolio

Pharos NLP delivery portfolio observations, 2019-2026

Ranges we consistently see across 20+ NLP engagements.

  • 82-94% F1 on classification, extraction and similar tasks after 4-10 weeks of iteration; below 78% triggers architecture review.

  • Top-3 language parity within 5 F1 points of English on multilingual pipelines; languages with under 1M training examples drop 8-15 points typically.

  • 6-14 weeks for production NLP system including data pipeline, training and monitoring scaffolding[5].

  • $1.5k-$12k per month for classical NLP on mid-volume workloads; $3k-$35k for LLM-backed pipelines depending on token volume[7].

  • Quarterly retraining on stable domains; monthly or triggered retraining on fast-moving domains (support, moderation).

Observations reflect the Pharos Production NLP delivery portfolio 2019-2026. Full case studies in our case studies library.

NLP development outlook 2026-2027

Three shifts are reshaping production NLP system delivery.

  • Classification, extraction and sentiment tasks move to LLM-based pipelines where per-query cost is acceptable. Classical NLP retains edge on real-time, high-volume or on-device scenarios[1].

  • Large multilingual models deliver production-quality output on 40+ languages. Teams building English-first pipelines rebuild for multilingual by default[2].

  • Buyers require published eval scores on domain-specific NLP benchmarks before contract signing[6].

Our four-dimension NLP evaluation template

Every NLP system we ship runs against the same four-dimension readiness evaluation before handover.

Eval template refreshes quarterly against latest Papers With Code benchmarks, multilingual research and vendor NLP releases.

Production post-mortem

When tokenization changed silently broke downstream parsing

A customer feedback classification pipeline deployed in March 2025 upgraded its tokenizer version during a routine library update. The new tokenizer handled emoji and Unicode variant selectors differently, shifting output token IDs for ~6% of inputs. Downstream rule-based post-processing silently returned incorrect categories for that subset before a week of mis-routed tickets surfaced the issue.

Tokenizer version now pinned and asserted at both train and serve. Tokenization-parity check added to integration tests. Unicode edge case coverage expanded in NLP pipeline test suites.

Important
Pharos Production builds NLP systems. Model accuracy depends on training data quality, labeling protocol and domain distribution. Production NLP systems require ongoing monitoring, retraining and rollback procedures. We do not provide medical diagnosis, legal evidence certification or regulatory approval through NLP systems we deliver.

Published record

Published Pharos research

Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.

Platforms we work with

Trusted by Coinbase, Consensys, Core Scientific, MicroStrategy, Gate.io and 10+ more Web3 and enterprise platforms

16+ partners

Our 16 technology partners include:

  • Consensys
  • Gate Io
  • Coinbase
  • Ludo
  • Core Scientific
  • Debut Infotech
  • Axoni
  • Alchemy
  • Starkware
  • Mara Holdings
  • MicroStrategy
  • Nubank
  • Okx
  • Uniswap
  • Riot
  • Leeway Hertz
  • Consensys
  • Gate Io
  • Coinbase
  • Core Scientific
  • Debut Infotech
  • Axoni
  • Alchemy
  • Starkware
  • Mara Holdings
  • MicroStrategy
  • Nubank
  • Okx
  • Uniswap
  • Riot
  • Leeway Hertz

About the founder and CTO

Dmytro Nasyrov

Dmytro Nasyrov

Founder and CTO Pharos Production

Ask the founder a question

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

Choose your cooperation model

Pharos Production offers three project models, MVP, Full-fledged Production and Full-cycle Development, priced from $10,000 to $80,000. An MVP prototype takes about 3 months.

Pilot
AI discovery and PoC

Feasibility study, prototype on your data and integration roadmap in four to eight weeks.

$14,000 - $30,000
Popular choice
Production
Production AI system

Full model development, API layer, cloud deployment and MLOps with monitoring.

$35,000 - $80,000
Enterprise
Enterprise AI platform

Multi-model architecture, custom data infrastructure, compliance and hybrid or on-prem delivery.

$80,000 - $180,000

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.

Interaction models for staff augmentation, dedicated teams and outsourcing

Request staff augmentation

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.

Popular choice

Hire dedicated experts

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.

Outsource your project

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.

45+ technologies

Technologies, tools and frameworks we use

Our engineers work with 45+ ai technologies - chosen for production reliability and performance.

AI and Machine Learning

LLM Providers 8

OpenAI GPT
Anthropic Claude
Google Gemini
Meta Llama
Mistral AI
Cohere
Ollama
xAI Grok

AI Frameworks 15

LangChain
LangGraph
CrewAI
AutoGen
Hugging Face
PyTorch
TensorFlow
scikit-learn
LlamaIndex
Keras
XGBoost
LightGBM
OpenCV
spaCy
ONNX Runtime

Vector Databases 7

Pinecone
Weaviate
Qdrant
Chroma
pgvector
Milvus
FAISS

MLOps and Infrastructure 11

MLflow
Weights & Biases
DVC
Kubeflow
AWS SageMaker
Azure ML
Google Vertex AI
NVIDIA Triton
Airflow
Ray Serve
vLLM

AI Agent Tools 4

OpenAI Agents SDK
Claude MCP
Semantic Kernel
Haystack
Trusted & Certified

Partnerships and awards

Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence

  • Partner1
  • Partner2
  • Partner3
  • Partner4
  • Partner5
18+ industry awards
Related guides: How to Choose an AI Company How We Build AI

An approach to the development cycle

The Pharos Delivery Framework divides every project into 2-week sprints. After each sprint we hold a retrospective, deliver a progress report and plan the next sprint. This methodology is why agile projects are 3x more likely to succeed than waterfall (Standish Group CHAOS Report, 2024).

  1. Team Assembly

    Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.

  2. MVP

    We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.

  3. Production

    We'll create a complete software solution that is custom-made to meet your exact specifications.

  4. Ongoing

    Continuous Support

    Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.

NLP engineering insights

Minimalist sculptor hands refining a translucent LLM sphere on a workbench with geometric chisels, representing LLM fine-tuning.
Engineering

LLM Fine-Tuning Guide: LoRA, RLHF and DPO Explained

Fine-tuning large language models transforms general-purpose AI into domain-expert systems that understand your industry terminology, follow your output format requirements and achieve accuracy levels that prompting alone cannot reach. This guide covers the three dominant fine-tuning techniques in 2026 - LoRA, RLHF and DPO - with practical guidance on when to use each, how to […]

Dmytro Nasyrov 9 min read
Read
View all articles
Skip glossary

NLP Development Glossary 7

Named Entity Recognition (NER)
An NLP task that identifies and classifies mentions of entities - people, organizations, locations, financial instruments - within unstructured text using sequence-labeling models such as fine-tuned BERT.
Transformer Architecture
A neural network design based on self-attention mechanisms, introduced in 'Attention Is All You Need' (2017), that underpins BERT, GPT, T5 and most state-of-the-art NLP models.
Fine-Tuning
The process of continuing training of a pre-trained transformer model on a labeled task-specific dataset, adapting general language representations to domain vocabulary and target labels.
Tokenization
The step that splits raw text into subword tokens matching a model's vocabulary (WordPiece for BERT, BPE for GPT), a process that directly affects how the model handles out-of-vocabulary domain terms.
Sentiment Analysis
An NLP classification task that assigns polarity (positive, negative, neutral) or aspect-level emotion scores to text, commonly applied to customer reviews, support tickets and social media monitoring.
Abstractive Summarization
A generative NLP task in which a model produces a concise summary using novel sentences rather than extracting original phrases, typically implemented with T5 or BART sequence-to-sequence models.
Intent Detection
An NLP classification task in conversational AI that identifies the user's goal from a natural language utterance, routing the dialogue system to the correct response policy or API action.

Frequently asked questions about NLP Development Services

Last updated:

  • Copy link Copies a direct link to this answer to your clipboard.

    Use classical NLP when you have thousands of labeled examples, need sub-50ms latency, need determinism or your workload scale makes per-call LLM pricing dominate. Use LLMs for fuzzy reasoning, zero-shot tasks, conversation and complex instruction following.

    Most production systems combine both. Pharos default: classical models for hot paths and structured extraction, LLMs for edge cases and natural-language interfaces.

  • Copy link Copies a direct link to this answer to your clipboard.

    Fine-tuning a pre-trained transformer (BERT, RoBERTa) typically needs 1,000-10,000 labeled examples per class for production accuracy. Training from scratch needs 100,000+. Active learning reduces labeling cost by selecting the most informative examples to label next. For new domains, we often bootstrap with LLM-generated labels (weak supervision) then fine-tune on the corrected subset.

  • Copy link Copies a direct link to this answer to your clipboard.

    Pre-trained multilingual models (mBERT, XLM-RoBERTa, LaBSE, Cohere multilingual embeddings) cover 100+ languages out of the box. For specialized domains in non-English, we fine-tune on domain-specific data.

    For languages with limited training data, we use translation to English, process with English models, then translate back - the accuracy tradeoff is usually small and the engineering cost is much lower.

  • Copy link Copies a direct link to this answer to your clipboard.

    Yes. Pipeline: OCR (AWS Textract, Google Document AI, Tesseract) + layout-aware transformer (LayoutLMv3) for fields + business rule validation + human review queue for low-confidence.

    We have shipped invoice extraction, insurance claims, legal document review, financial statements and medical records (with HIPAA compliance). Typical accuracy 95-99% after the validation layer.

  • Copy link Copies a direct link to this answer to your clipboard.

    Hybrid BM25 + dense retrieval. BM25 (via Elasticsearch or OpenSearch) handles exact matches; dense retrieval (via sentence-transformers or Cohere embeddings) catches semantic matches.

    A reranker (Cohere, Voyage, BGE reranker) combines both and orders results. Latency stays under 120ms if the two retrieval layers run in parallel.

  • Copy link Copies a direct link to this answer to your clipboard.

    NLP MVP $30,000-$80,000: 2-3 weeks discovery + eval set creation, 4-6 weeks build, 2-3 weeks production serving. Production NLP with MLOps: $80,000-$250,000+.

    Labeling budget separate, typically $3,000-$30,000 depending on volume and complexity. Ongoing retainer from $5,000/month for monthly eval refresh and drift monitoring.

  • Copy link Copies a direct link to this answer to your clipboard.

    Instrument input distributions (text length, vocabulary, topic mix) and prediction distributions on every inference. Compare week-over-week to baseline and alert when KL divergence exceeds threshold.

    For supervised models where ground truth is delayed, track accuracy on the lag. Automated retraining on a monthly schedule; more frequent for fast-moving domains like social or news content.

  • Copy link Copies a direct link to this answer to your clipboard.

    We decline search use cases where Elasticsearch dominates, structured classification where traditional ML wins, projects without a labeled eval set, real-time systems with sub-50ms latency budgets on long text and "NLP for the sake of NLP" projects without a measurable business metric. "Let us add NLP to it" is not a use case.

The Pharos takeaway on NLP

NLP rewards teams that treat tokenization, multilingual coverage and evaluation rigor as first-class concerns[10]. LLM vs classical selection, multilingual-by-default architecture and published eval evidence are the three areas that separate NLP systems that scale from pipelines that silently drift.

Book a 30-minute NLP readiness call
Dmytro Nasyrov, Founder and CTO at Pharos Production
Dmytro Nasyrov Founder & CTO Let's work together!

Your business results matter

Achieve them with minimized risk through our bespoke innovation capabilities

Your contact details
Please enter your name
Please enter a valid email address
Please enter your message
* required

We typically reply within 4 hours. Prefer email? [email protected]

What happens next?

  1. Contact us

    Contact us today to discuss your project. We're ready to review your request promptly and guide you on the best next steps for collaboration

    Same day

  2. NDA

    We're committed to keeping your information confidential, so we'll sign a Non-Disclosure Agreement

    1 day

  3. Plan the Goals

    After we chat about your goals and needs, we'll craft a comprehensive proposal detailing the project scope, team, timeline and budget

    3-5 days

  4. Finalize the Details

    Let's connect on Google Meet to go through the proposal and confirm all the details together!

    1-2 days

  5. Sign the Contract

    As soon as the contract is signed, our dedicated team will jump into action on your project!

    Same day

Our offices

Headquarters in Las Vegas, Nevada. Engineering office in Kyiv, Ukraine.

We also work with clients through dedicated local teams in Las Vegas, New York and San Francisco.

Las Vegas, United States

Headquarters PT
5348 Vegas Dr, Las Vegas, Nevada 89108, United States

Kyiv, Ukraine

Engineering office EET (UTC+2)
44-B Eugene Konovalets Str. Suite 201, Kyiv 01133, Ukraine

Let's work together!

Leave a request and we will contact you as soon as possible

Contact form for Pharos Production. Submit your name, email and message to reach our team directly. Free consultation, no commitment.

Your contact details
Please enter your name
Please enter a valid email address
Please enter your message
* required

We typically reply within 4 hours. Prefer email? [email protected]