Reviewed by Dr. Dmytro Nasyrov, Founder and CTO
AI Consulting
Pharos Production offers AI consulting services that bridge the gap between business strategy and technical implementation.
- 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.
What is AI consulting?
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
- Pitch decks that conclude "buy AI from us" regardless of the problem
- Generic AI readiness reports copy-pasted from a previous client
- Multi-month strategy retreats with no working pilot
- Engagements where the client wants validation, not assessment
- Vendor comparisons for a RFP we are also competing in
AI consulting at Pharos at a glance
- Engagements: 40+ AI consulting engagements since 2020 across SaaS, retail, insurance and manufacturing
- Kill rate: Roughly 1 in 3 engagements ends in a kill or scale-down recommendation. We publish this on purpose.
- Duration: 3-week standard assessment; 6-week deep dive with pilot scoping; 1-week vendor review
- Team: Founder-CTO plus senior ML engineer; no junior staffing on consulting
- Pricing: 3-week assessment from $18,000; 6-week deep dive from $42,000; vendor review $6,500
- Deliverable: Written decision document + a baseline measurement + a kill criterion. Slides only as a summary, never the primary artefact.
- Refund policy: Money-back if the engagement does not change a business decision; applied twice since 2020
AI consulting engagement vs pilot engagement: which should you buy?
| Factor | Standalone AI consulting assessment | Consulting bundled into a pilot engagement |
|---|---|---|
| Cost | $18K-$42K fixed fee | Consulting folded into pilot budget |
| Outcome | Written decision document | Running pilot with measurable impact |
| Risk of fake progress | Low (no code delivered) | Medium (pilot can ship ugly) |
| When to buy | You are not sure if AI fits the problem | You already know AI fits and want to prove it |
| Independence | High; no implementation incentive | Lower; vendor benefits from recommending build |
How we run an AI consulting engagement
Pharos Verified Delivery applied to consulting: every recommendation ties to a measurable baseline, a smallest-useful pilot and a written kill criterion. Nothing leaves the engagement without a decision owner.
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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
Consulting engagements that changed the decision
A 3-week consulting engagement should change a decision. If it does not, we refund the assessment fee. These engagements did.
Client ready to invest $1.8M in a demand-forecasting ML pipeline.
Baseline analysis showed their existing SARIMA model was within 3% of what ML could deliver. Kill recommendation saved $1.8M; we proposed a 4-week feature engineering refresh instead at $28K.
We measured the baseline before scoping the solution. Most AI consulting skips this step because a kill recommendation ends the retainer.
"We want AI" with no specific workflow, no dataset inventory and no success metric.
Scoped a claims triage pilot with 4-week delivery and a single metric (median handling time). Pilot shipped in 5 weeks; handling time dropped 31% before any model was trained.
The model was a tiny part of the value. Workflow redesign and data cleanup did most of the work, which is the honest story almost no AI vendor tells.
Evaluating 11 AI platforms with no clear selection criteria.
Reduced the shortlist to 2 platforms in a week using a weighted matrix tied to the client's actual workload. $220K annual license saved by rejecting the expected winner on TCO grounds.
We did not recommend our own platform. Independence is how AI consulting is supposed to work.
Client names anonymized under NDA. Full case studies at /cases/.
When AI consulting is a waste of money
If the answer is obvious before the engagement starts, the engagement is wrong. Examples where we tell the client to skip consulting altogether:
- The problem is clearly rules-based and a simple script would fix it
- The dataset does not exist and there is no plan to collect it
- Leadership wants a report to justify a decision they already made
- The expected ROI is smaller than the cost of the consulting engagement
- The team has no capacity to operate whatever we recommend
Before hiring any AI consultant, run a 2-day internal audit: list the top 5 workflows consuming the most analyst time, then ask whether any of them would be fundamentally changed by perfect prediction. If the answer is no, AI is not the right tool and consulting will not change that.
Pharos AI consulting portfolio
Pharos AI consulting delivery portfolio observations, 2020-2026
Ranges we consistently see across 50+ consulting engagements.
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Typical 2-4 weeks from kickoff to a documented build, buy or defer recommendation with cost estimate and data-readiness verdict.
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Approximately 40% of discovery engagements end with a do-not-build or build-something-simpler recommendation citing data gaps or unclear success metric[2].
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55-65% of discovery engagements that pass the data-readiness gate proceed to a scoped build with Pharos in the following quarter.
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Discovery and data-readiness audits $8,000-$25,000; architecture review and roadmap $15,000-$60,000; governance baseline setup $10,000-$35,000.
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70% of follow-on engagements include both a build tier and an MLOps or monitoring tier[11].
AI consulting outlook 2026-2027
Three shifts are reshaping AI consulting demand.
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Enterprise buyers now hire AI consultants for risk classification, model cards and evaluation artifacts, not framework choice. Procurement cycles penalise vendors without published governance documentation[8].
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Most enterprise AI budgets allocate to structured-data prediction and retrieval-augmented generation over foundation model training. Consultants who default to frontier models lose deals to teams that lead with the cheapest sufficient technique[5].
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Procurement teams require model cards, evaluation reports and bias audits as deliverables. AI consultants without structured evaluation templates fail enterprise review regardless of technical strength[6].
Our four-dimension AI consulting evaluation template
Every AI consulting engagement we ship runs against the same four-dimension readiness evaluation before handover.
Production post-mortem
When a data-readiness audit saved a client from a six-month wasted build
A FinTech client engaged us in Q2 2025 for a fraud detection ML build with an estimated 18-month timeline and $280k budget. Our four-week data-readiness audit found labeled fraud examples were one-twentieth of the volume the proposed model family required, and historical data had significant label drift from a 2024 policy change. We recommended against the ML build and instead shipped a rules-and-statistical-baseline approach at 10% of the original cost.
Standard consulting retainer now always starts with a data-readiness audit before build scoping. Approximately 40% of discovery engagements end with a "do not build" or "build something simpler" recommendation, preventing sunk cost on under-specified data.
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
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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
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
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
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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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Yes, and we do so in roughly a third of engagements. The kill recommendation is the most valuable outcome when the data, problem or timing is wrong.
That number is public because it is how honest AI consulting is supposed to work.
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Strategy consulting produces slides and frameworks. Our AI consulting produces a working baseline measurement, a scoped pilot and a written go or no-go.
We are engineers writing recommendations we could also build, which keeps the advice grounded.
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Week 1 is problem framing and data inventory. Week 2 is baseline measurement and feasibility analysis.
Week 3 is pilot scoping and written recommendation. The deliverable is a decision document signed off by a named client decision maker.
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We run second-opinion engagements compressed to 1-2 weeks. We will not take the engagement if there is an active RFP we are competing in, because independence matters more than a deal.
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We decline when the answer is obvious (skip AI, use a script), when the client wants validation rather than assessment, when there is no dataset and no plan to collect one, or when leadership has already made the decision and wants a report to justify it.
The Pharos takeaway on AI consulting
AI consulting rewards honest assessment over cheerleading. The Pharos approach leads with data-readiness, governance baseline and measurable outcomes before recommending a build path, and declines projects where the simpler tool fits better[10].
Book a 30-minute AI readiness call
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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
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.