Reviewed by Dr. Dmytro Nasyrov, Founder and CTO
How We Build AI Solutions
Our AI development methodology combines phased delivery, rigorous testing and production-grade MLOps.
- 25+ AI projects delivered
- 90+ engineers
- 90+ Clutch reviews
Aligned with these frameworks. Audit reports and certifications available on request.
Reviewed by Dmytro Nasyrov
Founder and CTO
23+ years in custom software development. Led 70+ projects across FinTech, healthcare, Web3 and enterprise. aligned with ISO 27001 team.
How We Build AI Solutions
Authoritative citations 12 sources
-
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
-
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
-
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
-
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.
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
- This methodology applies to applied AI (integrating models into products), not foundational model research
- We do not guarantee specific model accuracy metrics as they depend on training data quality and availability
- GPU infrastructure costs are billed separately from Pharos engineering services
AI Delivery at a Glance
- Discovery to Production: 6-11 weeks
- Evaluation Framework: Golden dataset + automated scoring
- Deployment Pattern: Shadow mode → canary → full rollout
- Monitoring: Drift detection + retraining triggers
- Stack: Python, PyTorch/HF, LangChain, MLflow
- Infrastructure: AWS/GCP/Azure with Kubernetes
Pharos AI Delivery vs In-House AI Team
When to use a specialized AI engineering partner versus building internal capabilities
| Factor | Pharos AI Delivery | In-House AI Team |
|---|---|---|
| Time to first model | 6-11 weeks with established MLOps pipeline | 3-6 months including infrastructure setup from scratch |
| ML expertise depth | Cross-domain experience from 40+ AI deployments | Deep domain expertise but narrower ML pattern library |
| Cost (first year) | $80K-250K project-based engagement | $400K-800K for 2-3 ML engineers (salary + infrastructure) |
| Evaluation rigor | Standardized eval framework from day one | Custom evaluation built during the project |
| MLOps maturity | Production-grade from day one (CI/CD, monitoring) | Often deferred, leading to manual deployment cycles |
| Long-term ownership | Full handoff with documentation and training | Native ownership but key-person risk |
AI Delivery Methodology
Our AI delivery framework operates in five phases: discovery and data audit (1-2 weeks), model prototyping and evaluation (2-4 weeks), pipeline engineering and MLOps setup (2-3 weeks), staged rollout with shadow mode testing (1-2 weeks) and production monitoring with drift detection (ongoing).
-
Phase 01 / 04 Paid Discovery
2-4 weeks- Technical validation
- Architecture proposal
- Scope refined estimate
-
Phase 02 / 04 Iterative Build
2-week sprints- Working demos every sprint
- CTO review at milestones
- ADRs documented
-
Phase 03 / 04 Production Readiness
- Monitoring and alerting
- Security audit Pen test
- Runbooks and rollback
-
Phase 04 / 04 Support
Ongoing- Security patches
- Performance tuning
- 4h SLA response
Pharos Verified Delivery applied to 70+ production applications since 2013
AI Project Outcomes
How our AI delivery methodology translates to measurable engineering outcomes
Manual contract review taking 3-4 hours per document. Legal teams processing 40 contracts per week with frequent missed clauses.
LLM extraction pipeline processing contracts in under 90 seconds. Clause detection accuracy 94.2% against senior lawyer benchmark. Weekly throughput increased to 300+ contracts.
We spent the first two weeks building a golden evaluation dataset with the client's legal team. Every model iteration was scored against that dataset before deployment gates. The final system runs in shadow mode alongside human reviewers for the first month.
Reactive maintenance on 200+ production machines. Average 14 unplanned downtime events per month at $8,000-12,000 per incident.
Sensor data pipeline feeding gradient-boosted models predicting failures 72 hours ahead. Unplanned downtime reduced to 2 events per month. Annual savings projected at $1.2M.
Data quality was the bottleneck, not model complexity. We spent 60% of the project normalizing sensor data from 5 different PLC manufacturers into a unified time-series format. The model itself is deliberately simple.
Support team searching across 4,000+ help articles and internal wikis. Average first-response time 4.2 hours. 35% of tickets escalated due to incomplete answers.
RAG system with chunked document embeddings and reranking. First-response time down to 45 minutes. Escalation rate dropped to 12%. Support team handles 2.5x ticket volume.
Retrieval quality depended more on chunking strategy than the embedding model. We tested 8 chunking approaches before finding that section-aware splitting with 200-token overlap outperformed everything else.
Client names anonymized under NDA. Full case studies at /cases/.
When Our AI Methodology Is Overkill
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:
- You need a simple chatbot or FAQ automation that can be solved with prompt engineering alone
- Your data volume is too small for meaningful model training (under 1,000 labeled examples)
- The business problem is better solved with rule-based logic or traditional analytics
- You want to build a foundational model rather than apply existing models to business problems
We decline about 30% of AI project inquiries because the problem does not warrant machine learning. If a decision tree, regex pipeline or SQL query solves the problem, we will tell you. AI adds operational complexity that only pays off when the problem genuinely requires learned pattern recognition at scale.
AI Engineering Research
MLOps Community Practices Guide Community-maintained reference for machine learning operations patterns and production deployment best practices Continue readingPharos AI and ML portfolio
Pharos AI and ML delivery portfolio observations, 2018-2026
Observations from 19 AI and ML engagements 2018-2026 across FinTech, healthcare, logistics, retail and industrial domains.
-
14 of 19
Leaked or time-disrespecting eval
14 of 19 projects we inherited had leaked labels or time-disrespecting eval splits inflating offline metrics by 3-8 points. Rebuilding eval pipelines was the top corrective action every time.[8]
-
11 of 15
Gradient boosting beat deep
Gradient boosting baselines (XGBoost, LightGBM) matched or beat deep models on 11 of 15 tabular workloads while running 10-40x cheaper on inference.[3]
-
2.8x
MLOps maturity refresh lift
MLOps maturity (level 2+: automated training and deployment) correlated with 2.8x higher model refresh cadence and 4x lower incident rate in production.
-
8 of 19
Adversarial weaknesses surfaced
Adversarial tests surfaced exploitable weaknesses on 8 of 19 projects. 3 would have reached production unhardened without the pre-release checklist.[9]
AI build methodology outlook 2026-2027
AI build methodology outlook 2026-2027 is shaped by the retreat from demo-driven prototyping toward evaluation-driven delivery where every model deployment ships with a measurable evaluation harness.
-
Evaluation-first delivery
Golden sets, drift monitors and human-in-the-loop labels replaced proof-of-concept notebooks as the gating artifact for production ML release.[8]
-
MLOps maturity gate
Level 2 pipelines minimum (automated training, testing, deployment, monitoring) became the enterprise procurement baseline after the 2024-2025 wave of stalled AI pilots.[11]
-
Adversarial hardening
Prompt injection, data poisoning and model exfiltration tests moved from security afterthought into standard pre-release checklist.[9]
-
Model card and datasheet artefacts
Shipped with every release became the norm rather than an academic exercise as enterprise AI audits tightened.[8]
AI engagement 90-day evaluation template
Evaluate an AI engagement at the 90-day mark using this 8-point check before you sign off on production release.
-
1
Golden evaluation set
Frozen with at least 500 representative examples and human labels.
-
2
Baseline model shipped first
Tabular: XGBoost or LightGBM; LLM: hosted reference model with numeric scorecard.[3]
-
3
MLOps pipeline
Automated training, evaluation and deployment with rollback pathway.[11]
-
4
Drift monitors
Input distribution, output distribution and label distribution tracked with alert thresholds.
-
5
Adversarial test suite
Prompt injection, data poisoning and model exfiltration scenarios.[9]
-
6
Model card artefacts
Model card and datasheet artefacts shipped with the release.
-
7
Human-in-the-loop
Escalation pathway for low-confidence predictions.
-
8
Cost of ownership
Cost per prediction and total cost of ownership modeled over 12 months.
Production post-mortem
Lesson from a 2024 FinTech fraud detection engagement
42M transactions/month, 6 regions. The original team spent 11 weeks fine-tuning a deep model that delivered 92% ROC-AUC on a historical sample. We paused and shipped a LightGBM baseline in 4 days that hit 90.4% ROC-AUC and had 12ms p99 inference on CPU vs. 210ms on GPU for the deep model.[4] We also discovered the original eval set had leaked fraud labels into the feature set, inflating ROC by 3-5 points. We rebuilt the eval pipeline with time-respecting splits, froze a golden set and re-measured.
Final production model was the LightGBM baseline plus a neural re-ranker on the top 5% risk scores. Cost per prediction dropped 94%, false positive rate held at 0.6% and the team shipped new feature experiments weekly instead of monthly.
Published record
Published Pharos research
Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.
- State of AI Development Costs 2026
- AI Agent Frameworks Comparison 2026
- Build vs Buy AI Agent: 2026 Decision Framework
- RAG vs Fine-Tuning: When to Use Each Approach
- How to Choose an AI Development Company
- State of Smart Contract Audits 2026
- State of Production AI Engineering 2026
- State of FinTech Compliance Cost 2026
- State of Custom Software TCO 2026
- State of AppSec 2026
- State of Tech Due Diligence 2026
- How to Choose a Blockchain Development Company
- How to Choose a FinTech Development Company
- FinTech Compliance Checklist 2026: PCI DSS, SOC 2, GDPR and Beyond
- AI in FinTech: Transforming Financial Services in 2026
- Software Development Cost Guide: What to Expect in 2026
- How to Choose a Software Development Company in 2026
- Cybersecurity Essentials for Startups and SMBs in 2026
- FinTech Trends 2026: How Top FinTech Trends are Shaping Digital Banking
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
-
Consensys
-
Gate Io
-
Coinbase
-
Ludo
-
Core Scientific
-
Debut Infotech
-
Axoni
-
Alchemy
-
Starkware
-
Mara Holdings
-
Microstrategy
-
Nubank
-
Okx
-
Uniswap
-
Riot
-
Leeway Hertz
Our AI development process
-
Discovery and feasibility sprintA 2-4 week paid phase that validates technical assumptions, tests data quality and produces a refined scope. Reduces full-build risk by catching issues early. -
Iterative prototype and buildAgile sprints with working demos every 2 weeks. Model training, evaluation and integration happen incrementally. Performance benchmarks at every milestone.
-
Production deployment and MLOpsProduction-grade deployment with monitoring, drift detection, model versioning and rollback. Ongoing maintenance and performance optimization included.
About Founder and CTO
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
Choose your cooperation model
Current-state audit, use-case validation and strategic roadmap for your leadership team.
Deep-dive assessment, technology selection, architecture blueprint and phased implementation plan.
Full advisory retainer covering strategy, delivery oversight, governance and change management.
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
-
Team Assembly
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
-
MVP
We’ll design, build, and launch your MVP, ensuring it meets the core requirements of your software solution.
-
Production
We’ll create a complete software solution that is custom-made to meet your exact specifications.
-
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.
Type to filter questions and answers. Use Topic to narrow the list.
Showing all 5
No matches
Try a different keyword, change the topic, or clear filters
-
We run a 1-2 week discovery sprint that audits your data availability, quality and volume against the business problem. We build a minimum viable evaluation framework and test whether a baseline model shows signal.
If the data cannot support the use case, we tell you before any engineering investment.
-
Our delivery gates require model evaluation at each phase. If a model underperforms, we iterate on data preparation, feature engineering or model architecture before proceeding.
If the fundamental data cannot support the accuracy target, we recommend alternative approaches or descope the use case.
-
Both. We deploy the full pipeline: data ingestion, feature stores, training orchestration, model registry, serving infrastructure, monitoring dashboards and automated retraining.
Models without production infrastructure are science projects, not products.
-
Yes. We integrate with existing data warehouses (Snowflake, BigQuery, Redshift), streaming platforms (Kafka, Kinesis) and orchestration tools (Airflow, Dagster).
We do not require you to migrate to a new data stack.
-
Shadow mode runs the AI model in parallel with your existing process without affecting production output. The model processes real data and generates predictions that are logged and compared against human decisions.
This validates performance before any production traffic touches the model.
The Pharos takeaway on AI delivery
AI projects fail not because the model is too simple but because the evaluation harness is too weak, the feedback loop is too slow and the MLOps foundation is absent. Pharos ships AI with golden sets, drift monitors, adversarial hardening, model cards and level-2-plus MLOps pipelines wired in at week one rather than retrofitted after the first production incident.[1]
Book a 30-minute AI readiness call
Your business results matter
Achieve them with minimized risk through our bespoke innovation capabilities
What happens next?
-
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 -
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.