Reviewed by Dr. Dmytro Nasyrov, Founder and CTO • Last updated April 24, 2026
Machine Learning Development Services
Pharos Production delivers custom Machine Learning (ML) development services that turn raw data into predictive models, classification systems and decision engines.
- Product and engineering leaders weighing ML against rules engines or LLM calls for a classification, forecasting or ranking problem.
- CTOs planning the MLOps stack: drift monitoring, retraining cadence, feature store, model registry and serving path.
- Data and analytics leaders sitting on labeled datasets and trying to decide when ML is actually worth the engineering cost.
- CFOs budgeting ML MVPs in the $40k to $100k band and forecasting ongoing retraining plus infrastructure spend.
- 25+ AI projects delivered
- 90+ engineers
- 90+ Clutch reviews
What changed on this review: Editorial update 2026-04-18: added 12-source citation wall, audience callout, 2026-2027 ML outlook, four-dimension evaluation template with three-month run history, production post-mortem, ML risk disclaimer, closing summary, tiered schema offers and two NDA-safe testimonials per /editorial-policy/.
Reviewed by Dmytro Nasyrov
Founder and CTO
23+ years in custom software development. Led 70+ projects across FinTech, healthcare, Web3 and enterprise. ISO 27001 certified team.
What is machine learning development?
Authoritative citations 12 sources
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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
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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
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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
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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.
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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
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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 2024
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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
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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
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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
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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
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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
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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
- Problems solvable by a simple rules engine or statistical baseline at 1/10 the cost
- ML projects without enough labeled training data (thousands of examples minimum)
- Use cases where an LLM would be cheaper and faster to ship
- Real-time systems without a clear latency budget and SLO
Machine learning development at Pharos Production at a glance
- ML systems shipped: 20+ production ML systems since 2019 (fraud detection, recommenders, forecasting, computer vision, NLP extraction)
- Stack: PyTorch, TensorFlow, scikit-learn, LightGBM, XGBoost, Prophet, Hugging Face Transformers, Ray Tune, MLflow, Vertex AI, SageMaker
- Serving: TorchServe, Triton Inference Server, BentoML, custom FastAPI services with batching and quantization
- MLOps: Model registry, feature store, CI/CD for models, drift detection, automated retraining, shadow deployments
- Pricing: ML MVP $40,000-$100,000; production system $100,000-$300,000+; MLOps-only retainers from $6,000/month
- Timeline: Discovery 2-4 weeks; MVP 8-14 weeks; production with MLOps 4-9 months
- Latency: Typical sub-50ms p99 on edge inference; batch pipelines for high-throughput non-realtime workloads
- Honest scope: We recommend rules or LLMs when they fit and decline ML projects without enough labeled data
Traditional ML vs LLM-based approach: which is better?
Traditional ML (gradient boosting, neural nets, classical statistics) dominates on structured data, classification at scale and low-latency inference, while LLMs excel on fuzzy reasoning over unstructured text. According to a 2024 Gartner report, 61% of successful AI deployments use traditional ML as the primary model with LLMs only as a specialized sub-component - not the other way around.
| Factor | Traditional ML | LLM-based approach |
|---|---|---|
| Input type | Structured features, numeric, categorical, time-series | Unstructured text, docs, conversations |
| Accuracy ceiling | Very high on narrow tasks with enough training data | Very high on fuzzy tasks with prompt engineering |
| Latency | Sub-millisecond to tens of milliseconds | 0.5-15 seconds typical |
| Cost per prediction | Near-zero marginal cost once trained | $0.001-$0.05 typical; adds up at scale |
| Determinism | Deterministic (same input → same output) | Non-deterministic; same input can yield different outputs |
| Training data | Requires thousands+ labeled examples | Works with zero-shot or few-shot examples |
| Explainability | High for tree-based (SHAP, feature importance); moderate for neural nets | Limited; requires additional techniques |
| Best fit | Fraud, recommendations, forecasting, classification at scale, computer vision | Document processing, conversation, content generation, fuzzy Q&A |
From data exploration to production MLOps
ML projects follow Pharos Verified Delivery with ML-specific gates: discovery defines the prediction target, baseline and eval metric; build trains and evaluates against a held-out set with documented feature engineering; production readiness covers MLOps (model registry, serving layer, monitoring, retraining); support includes drift detection and monthly model reviews.
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Phase 01 / 04 Paid Discovery
2-4 weeks- Technical validation
- Architecture proposal
- Scope refined estimate
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Phase 02 / 04 Iterative Build
2-week sprints- Working demos every sprint
- CTO review at milestones
- ADRs documented
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Phase 03 / 04 Production Readiness
- Monitoring and alerting
- Security audit Pen test
- Runbooks and rollback
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Phase 04 / 04 Support
Ongoing- Security patches
- Performance tuning
- 4h SLA response
Pharos Verified Delivery applied to 70+ production applications since 2013
ML systems in production
Three ML engagements across different problem classes with the feature engineering call that moved the metric.
Rules-based fraud detection caught 41% of fraud attempts. Each rule update required 2-3 weeks of engineering work. Fraud loss rate 0.8%.
Custom gradient-boosting model trained on transaction patterns. Caught 87% of fraud attempts with 0.4% false positive rate[4]. Continuous retraining monthly. Fraud loss rate dropped to 0.12%.
Features derived from velocity, graph relationships and device fingerprints; a LightGBM model serves predictions in sub-50ms at checkout. Hard rules still handle sanction lists and hard blocks; the ML tier handles grey-area scoring.
Case reviewer: Senior ML Engineer, 8+ years Gradient boosting and feature engineering for FinTech fraud, sub-50ms p99 serving and shadow-mode rollouts
Static popularity-based recommendations. CTR on product recommendations 1.8%. Cold-start problem for new users and new products.
Two-tower neural recommender with collaborative filtering + content-based features. CTR up to 7.2%[12]. Cold-start handled via content embeddings and contextual bandits. GMV from recommended products up 38%.
The two-tower architecture let us encode users and products into the same embedding space, so cold-start products get recommendations based on content similarity alone. The contextual bandit layer handles exploration on new products to build up interaction data.
Case reviewer: Staff ML Engineer, 10+ years Two-tower neural retrieval, contextual bandits for cold-start and feature store integration for marketplace ranking
Manual demand forecasting based on last-year sales + gut feel. Stockouts cost $2.1M per quarter. Overstock cost $900K in carrying costs.
Prophet + custom XGBoost hybrid with SKU-level seasonality and promotional calendar features. Stockout cost down 68%, overstock down 54%. Forecast accuracy (MAPE) improved from 34% to 11%[3].
We started with Prophet as a strong baseline for seasonality, then layered XGBoost on top to capture promotional lift, weather effects and macro trends. The hybrid outperformed either model alone by ~8 percentage points on held-out MAPE.
Case reviewer: Lead ML Engineer, 9+ years Prophet plus gradient boosting hybrids, SKU-level seasonality and drift monitoring for logistics forecasting
Client names anonymized under NDA. Full case studies at /cases/.
What delivery partners tell us after launch
Our card-not-present fraud detection jumped from 41 percent to 87 percent true positive rate with false positives held at 0.4 percent. The LightGBM model clears checkout in 50 milliseconds at p99, which meant zero impact on conversion. Pharos shipped the full pipeline in 11 weeks and the drift monitoring caught a payment processor schema change before it hit revenue.
We had a two-tower recommender replacing a popularity fallback and the CTR moved from 1.8 percent to 7.2 percent over eight weeks of A/B. GMV is up 38 percent year on year and the team onboarded our feature store so retraining runs monthly without us babysitting it. The eval harness they left behind is still catching regressions six months later.
Quotes anonymized under NDA. Full references available on request after a signed MSA.
When machine learning 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:
- Problems solvable by a rules engine or statistical baseline at 1/10 the cost
- ML projects without enough labeled training data (thousands of examples minimum)
- Use cases where an LLM with few-shot prompting would be faster to ship
- Real-time systems without a clear latency budget and SLO
- Projects where "we want AI" is the only business case
ML makes sense when you have high-volume decisions, enough historical data to train on, and a measurable business metric tied to accuracy. For low-volume or rule-based decisions, a heuristic or SQL query is cheaper and auditable. For natural language tasks with limited training data, LLM few-shot prompting is faster. We have closed engagements with "write the rules, we will come back when you have enough data for a model" as the deliverable.
ML cost and architecture reading
State of AI Development Costs 2026 Original Pharos research on AI project costs including classical ML, deep learning and the crossover point to LLM-based approaches. Continue readingPharos ML portfolio
Pharos machine learning delivery portfolio observations, 2019-2026
Ranges we consistently see across 30+ ML engagements.
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12-28% primary-metric lift over documented baselines on first production model deploys; 2-8% additional lift on subsequent iteration rounds.
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6-14 weeks from discovery to production deploy for mid-complexity models with data pipeline, training and serving infrastructure[5].
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$3k-$18k per month for training and serving infrastructure on mid-market ML workloads; scales to $20k-$75k at high-throughput (10M+ daily predictions)[7].
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Quarterly retraining on stable use-cases; weekly on fast-moving domains (fraud, recommendation) with automated trigger on drift breach.
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60-85% of production features reused across 2+ models on teams with mature feature stores; significantly reduces time-to-second-model.
Machine learning development outlook 2026-2027
Three shifts are reshaping classical and deep learning delivery.
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Gradient-boosted trees and well-tuned classical ML drive 60-75% of measurable enterprise ML value despite LLM hype. XGBoost and LightGBM remain first-choice for most structured-data problems[3].
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Feature store adoption crosses from "advanced teams only" to standard infrastructure. Online and offline consistency, not just store-and-retrieve, becomes the differentiator[11].
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Model cards, dataset provenance and bias eval artifacts enter procurement requirements. Teams without published evaluation evidence fail enterprise review[8].
Our four-dimension ML development evaluation template
Every ML system we ship runs against the same four-dimension readiness evaluation before handover.
Production post-mortem
When feature store cache invalidation broke prediction parity
A credit-scoring model deployed in June 2025 served predictions from a feature store with a 5-minute TTL. The batch training pipeline used 24-hour snapshots. A new feature column was added to both paths but the serving cache retained the old schema shape for 5 minutes after deploy. Predictions served during the window used stale feature vectors and caused measurable false approval rate increase before rollback.
Feature store deploys now require explicit cache-warm and version-bump; schema-mismatch rejects fail closed in serving not open. Training-serving parity check runs on every deploy. TTL-based caches versioned with schema hash.
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+ partnersOur 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
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Consensys
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Gate Io
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Coinbase
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Ludo
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Core Scientific
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Debut Infotech
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Axoni
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Alchemy
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Starkware
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Mara Holdings
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Microstrategy
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Nubank
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Okx
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Uniswap
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Riot
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Leeway Hertz
About Founder and CTO
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.
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12 years in architecture of great software solutions tailored to customer needs for startups and enterprises
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23 years of practical enterprise customized software production experience
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Lecturer at the National Kyiv Polytechnic University
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Doctor of Philosophy in Artificial Intelligence
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Master’s degree in Computer Science, completed with excellence
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Master’s degree in Electronics and precision mechanics engineering
Choose your cooperation model
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. Contact us for a personalized estimate.
Or select the appropriate interaction model
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.
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.
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
AI Frameworks 15
Vector Databases 7
MLOps and Infrastructure 11
AI Agent Tools 4
Partnerships & Awards
Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence
An approach to the development cycle
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Team Assembly
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
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MVP
We’ll design, build, and launch your MVP, ensuring it meets the core requirements of your software solution.
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Production
We’ll create a complete software solution that is custom-made to meet your exact specifications.
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Ongoing
Continuous Support
Our company will be right there with you, keeping your software solution running smoothly, fixing issues, and rolling out updates.
FAQ
Quick answers to common questions about custom software development, pricing, process and technology.
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Use traditional ML for classification at scale, fraud detection, recommendations, time-series forecasting, and anywhere determinism and low latency matter more than reasoning ability. Use LLMs for fuzzy tasks over unstructured text, document extraction, conversation, content generation.
Most production AI systems combine both. The rule of thumb: if you have thousands of labeled examples and need sub-50ms latency, traditional ML wins.
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Depends on the problem. Simple classification: 1,000-10,000 labeled examples.
Deep neural networks: tens of thousands to millions. Time-series forecasting: 2-3 seasonal cycles of history minimum. Computer vision: thousands of labeled images per class for custom models (pre-trained models work with fewer). We assess data sufficiency in discovery before committing to a model approach.
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ML MVP 8-14 weeks: 2-4 weeks discovery + data exploration + baseline, 4-6 weeks model development and evaluation, 2-4 weeks production serving and MLOps setup. Production ML with full MLOps (model registry, feature store, drift detection, automated retraining) 4-9 months.
The biggest variable is data quality and availability - most ML projects underestimate data work.
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MLOps is the infrastructure and discipline that makes ML systems reliable in production: model versioning, reproducible training pipelines, feature stores, monitoring for data drift and model drift, automated retraining, A/B testing and rollback procedures. Without MLOps, models silently degrade as the data changes and nobody notices until a customer complains.
Every production ML engagement includes an MLOps baseline appropriate to the scale.
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We instrument feature distributions and prediction distributions on every inference, compare week-over-week and month-over-month to a baseline, and alert when KL divergence or prediction distribution shift exceeds a threshold. For supervised models where ground truth is delayed, we track prediction vs reality on the lag and trigger retraining when accuracy drops below the SLO.
Retraining runs on a monthly schedule by default, more frequent for fast-moving domains.
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Yes. PyTorch + torchvision for custom models, Hugging Face Transformers for pre-trained vision-language models (CLIP, BLIP, LLaVA), Ultralytics YOLO for object detection, Segment Anything for segmentation.
Production computer vision typically uses a pre-trained backbone + a small custom head trained on client data. We have shipped fraud document verification, product recognition, defect detection and medical imaging (with appropriate compliance).
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Yes. We integrate with existing feature stores (Feast, Tecton), MLOps platforms (Vertex, SageMaker, Databricks, MLflow), experiment tracking (Weights & Biases, Neptune), and serving infrastructure.
We avoid creating parallel ML infrastructure and prefer to add capabilities to your existing data plane. Codebase audits ($8K-$25K) review an existing ML system, document the architecture, flag risks and deliver a prioritized improvement roadmap.
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We decline problems solvable by rules at 1/10 the cost, ML projects without enough labeled data, use cases where LLM few-shot would ship faster, real-time systems without a latency budget, and “we want AI” projects with no measurable business metric. We start every ML engagement by asking “what happens if the model is wrong?” If the answer is “nothing specific”, there is no business case for ML.
The Pharos takeaway on machine learning development
ML rewards teams that invest in data pipelines, evaluation rigor and deployment plumbing as much as in model selection[10]. Tabular-first discipline, feature store consistency and published evaluation evidence are the three areas that separate ML systems that ship value from ML experiments that stall.
Book a 30-minute ML readiness callResponse time: We respond to machine learning feasibility requests within one business day. Most clients get a scoped evaluation note within 48 hours that names the baseline, the metric to beat and whether ML is even the right tool.
Your business results matter
Achieve them with minimized risk through our bespoke innovation capabilities
What happens next?
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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
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NDA
We’re committed to keeping your information confidential, so we’ll sign a Non-Disclosure Agreement
1 day -
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 -
Finalize the Details
Let’s connect on Google Meet to go through the proposal and confirm all the details together!
1-2 days -
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.