Reviewed by Dr. Dmytro Nasyrov, Founder and CTO
AI Product Engineering
Pharos Production offers AI Product Engineering services that take AI-powered products from concept to market.
- 25+ AI projects delivered
- 90+ engineers
- 96 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 110+ projects across FinTech, healthcare, Web3 and enterprise, ISO 27001-aligned team.
What is AI product engineering?
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
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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
- Pure research projects with no production target
- Engagements where product and engineering report into different leaders without alignment
- AI features with no instrumentation plan
- Prototypes that the client expects to ship without hardening
- Pods where the client has not committed dedicated review capacity
AI product engineering pod at a glance
- Pods run: 12+ AI product engineering pods since 2022 across SaaS, marketplaces and FinTech
- Pod composition: 2 engineers, 1 ML engineer, 1 PM, 0.3 designer; rotating Pharos founder review
- Cadence: At least one user-facing release per quarter with telemetry; weekly internal demos
- Pricing: Pod from $48,000/month; minimum 4-month engagement; quarterly outcome reviews shared with the client
- Default stack: OpenAI, Anthropic, Vertex; product side in React, Next.js, Vue; data side in BigQuery, Snowflake, Postgres
- Telemetry: Every release ships with telemetry from day one; Datadog, Mixpanel, Amplitude or client stack
- Honest default: We propose a single embedded engineer when a full pod would be overkill
How we run an AI product engineering pod
Pharos Verified Delivery applied to AI product engineering means every quarter ships at least one user-facing release, every release ships with telemetry, and every kill decision is a written document, not a quiet abandonment.
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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 110+ production applications since 2013
AI product engineering, sliced into shipped releases
AI product engineering is judged by what shipped, not by demos. Each engagement below shipped a release inside its first quarter.
Search relevance team experimenting in notebooks; nothing shipped to users in 9 months.
Pharos pod shipped 4 user-facing releases in 12 weeks. Search-to-purchase conversion rose 17% on the target cohort.
We did not change the team. We changed the cadence. Notebook research became weekly releases as soon as the eval pipeline was stable.
Client building pricing intelligence in spreadsheets and ad-hoc scripts.
Production pricing model wrapped in a versioned API with audit trails; quote-to-close time dropped 31% after rollout.
The model was 20% of the work. The other 80% was integration, telemetry and audit trails. AI product engineering is mostly the boring 80%.
Recommendations system 4 years old with declining click-through.
Replaced the model and rebuilt the eval pipeline with offline+online metrics. CTR rose 22% after a 6-week ramp.
We kept the old model running in shadow mode for 4 weeks before cutover. Cutover-day surprises are how production AI gets a bad name.
Client names anonymized under NDA. Full case studies at /cases/.
When AI product engineering is the wrong investment
A pod is overkill when the AI work is small and well-scoped, and underkill when the work needs a multi-team ML platform investment. Both ends are wrong fits.
- The AI feature is small and a single engineer could ship it in 2-3 weeks
- The work needs an ML platform team, not a feature pod
- The client has not aligned product and engineering leadership
- There is no shipping cadence the client team can sustain after handoff
- The pod would replace a still-functional in-house team
For small features, a single embedded engineer is cheaper. For large platform investments, a dedicated ML platform engagement is the right shape. AI product engineering is the middle ground when you need cross-discipline ownership of a single feature surface and shipping cadence.
Pharos AI product portfolio
Pharos AI product engineering delivery portfolio observations, 2022-2026
Ranges we consistently see across 25+ AI product engagements.
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Weekly eval runs catch 15-30% of regressions invisible to behavioural metrics alone; catch rate higher on language-model feature changes.
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10-18 weeks from scope to MVP production deploy for mid-complexity AI-native product feature[5].
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Weekly eval runs + bi-weekly deploys on stable products; daily experiments during active iteration windows.
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35-60% typical activation rate on new AI features after 30-day onboarding; measured via product telemetry.
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5-15% measurable retention uplift on AI-native cohorts versus non-AI controls on successful features; measured over 90-day windows.
AI product engineering outlook 2026-2027
Three shifts are reshaping AI product development.
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The PM and ML engineering role boundary collapses for AI-native products. Product decisions require model-aware intuition; ML engineering requires product-metric accountability[5].
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AI product teams iterate on evaluation set performance rather than feature flags. Shipping an AI feature requires demonstrating eval uplift, not just behaviour toggle[2].
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Per-user context, retrieval and fine-tuning-alternatives move from nice-to-have to baseline in AI products. Generic-output AI products face commoditization pressure[11].
Our four-dimension AI product engineering evaluation template
Every AI product engagement we ship runs against the same four-dimension readiness evaluation before handover.
Production post-mortem
When eval-driven iteration caught a regression feature flags missed
An AI product we shipped in Q3 2024 had a recommendation feature protected by feature flag and A/B test. Behavioural metrics (click-through, session time) showed neutral or slightly positive results. The weekly eval run found a 7% regression in recommendation precision on labelled fixtures due to a minor prompt change. The regression would have been invisible to click metrics alone, but eval-driven iteration caught it before wider rollout.
Eval-first deploy gates became mandatory for every AI product feature: both behavioural metrics and eval-set performance must pass before wider rollout. Eval fixtures refreshed quarterly with flagged examples from production; feature flag alone never shipped.
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
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.
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13 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.
AI product engineering glossary 6
- Data Flywheel
- A product growth dynamic where user interactions generate labeled data that improves model quality, which attracts more users and generates more data - creating a self-reinforcing competitive advantage.
- Foundation Model
- A large-scale model pre-trained on broad data - such as GPT-4o, Claude or Gemini - that serves as a starting point for fine-tuning or prompt-based adaptation to specific product use cases.
- Evaluation Harness
- A structured testing framework that measures AI model performance on curated benchmark datasets across accuracy, safety and reliability dimensions, enabling regression testing across model versions.
- Model Card
- A standardized document describing a machine learning model's intended use, training data, performance metrics across demographic groups, known limitations and ethical considerations.
- Fine-Tuning
- The process of continuing training a pre-trained foundation model on a domain-specific dataset to adapt its outputs to a particular task, tone or knowledge domain with relatively few examples.
- Red-Team Testing
- A structured adversarial evaluation in which a dedicated team attempts to elicit harmful, biased or incorrect outputs from an AI model to identify vulnerabilities before production deployment.
Frequently asked questions about AI Product Engineering
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Full-stack AI product engineering spans every phase from validated concept to a live product: user research and problem framing, product design and UX prototyping, AI model development and evaluation, backend API and data pipeline engineering, frontend implementation and go-to-market preparation including app store or SaaS launch. Pharos provides a cross-functional team rather than hand-offs between separate agencies.
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An AI-native product is architected so that model inference, embedding pipelines and feedback loops are first-class infrastructure components - not added features. Data flywheel design, model versioning and evaluation harnesses are built in from day one, allowing the product to improve with usage rather than requiring periodic manual updates.
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A focused MVP with defined scope typically launches in 16 to 24 weeks: 3 to 4 weeks of discovery and product definition, 4 to 6 weeks of design and architecture, 8 to 12 weeks of engineering and 2 weeks of go-to-market preparation. Products requiring proprietary model training or complex data pipelines add 4 to 8 weeks.
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Model development covers dataset sourcing and preparation, selection between fine-tuning an existing foundation model and training a task-specific model, evaluation framework design with held-out test sets, hyperparameter tuning and red-team adversarial testing. Pharos delivers a model card documenting performance, limitations and intended use for each production model.
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AI product user research includes discovery interviews to identify workflow pain points, prototype testing of AI-assisted flows versus current workflows and measurement of task completion time and error rate changes. Because AI output is probabilistic, research explicitly tests user trust calibration - whether users over-rely on or inappropriately dismiss AI suggestions.
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Go-to-market support covers positioning and messaging aligned to the product’s AI differentiation, launch checklist preparation (compliance review, privacy policy, terms of service for AI features), integration into app stores or SaaS billing platforms and a 30-day hypercare period with on-call engineering support post-launch.
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Post-launch monitoring tracks model accuracy drift, user correction rates, latency percentiles and inference cost per active user via a real-time dashboard. Automated alerts trigger when accuracy falls below baseline or cost per request exceeds budget thresholds.
Pharos recommends quarterly model re-evaluation cycles with structured evaluation datasets.
The Pharos takeaway on AI product engineering
AI products reward teams that iterate on evaluation rigor as much as user metrics. Pharos ships AI products with eval-first deploy gates, weekly eval-set runs and product metrics aligned to business outcomes[2].
Book a 30-minute AI product readiness call
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
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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
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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!
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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.