Reviewed by Dr. Dmytro Nasyrov, Founder and CTO
Pharos Production offers AI consulting services that bridge the gap between business strategy and technical implementation.
Aligned with these frameworks. Audit reports 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.
| 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 |
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.
Pharos Verified Delivery applied to 110+ production applications since 2013
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/.
If the answer is obvious before the engagement starts, the engagement is wrong. Examples where we tell the client to skip consulting altogether:
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
Ranges we consistently see across 50+ consulting engagements.
Typical 2-4 weeks from kickoff to a documented build, buy or defer recommendation with cost estimate and data-readiness verdict.
Approximately 40% of discovery engagements end with a do-not-build or build-something-simpler recommendation citing data gaps or unclear success metric[2].
55-65% of discovery engagements that pass the data-readiness gate proceed to a scoped build with Pharos in the following quarter.
Discovery and data-readiness audits $8,000-$25,000; architecture review and roadmap $15,000-$60,000; governance baseline setup $10,000-$35,000.
70% of follow-on engagements include both a build tier and an MLOps or monitoring tier[11].
Three shifts are reshaping AI consulting demand.
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].
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].
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].
Every AI consulting engagement we ship runs against the same four-dimension readiness evaluation before handover.
Production post-mortem
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
Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.
Trusted by Coinbase, Consensys, Core Scientific, MicroStrategy, Gate.io and 10+ more Web3 and enterprise platforms
16+ partnersOur 16 technology partners include:
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
Pharos Production works in three engagement models, from a focused PoC to a production MVP to a full enterprise platform, with typical budgets from $10,000 to $400,000+ depending on scope and complexity.
Focused validation of your riskiest technical assumption with a working spike and a clear build-or-pivot recommendation.
Production-ready first version with core flows, real backend and the integrations to onboard first paying users.
Full-scale build with architecture, DevOps, QA, security and long-term evolution.
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.
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.
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.
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.
Our engineers work with 45+ ai technologies - chosen for production reliability and performance.
Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.
We'll create a complete software solution that is custom-made to meet your exact specifications.
Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.
Enterprise AI adoption in 2026 is at a tipping point. 78% of enterprises report using AI in at least one business function, but only 22% have successfully scaled AI beyond pilot projects, according to the McKinsey Global AI Survey (2024). The gap between experimentation and production deployment is where most AI initiatives fail - not […]
How much does AI development cost in 2026? AI development costs range from $10,000 for simple chatbots to $500,000+ for enterprise multi-agent systems. The final cost depends on four factors: model complexity, data preparation needs, integration scope and ongoing inference costs. AI development cost by project type Simple AI features like FAQ chatbots and basic […]
Quick Comparison: Custom AI vs Off-the-Shelf Factor Custom AI Off-the-Shelf Platforms Time to deploy 3-6 months 1-4 weeks Upfront cost $50K-$500K+ $500-$5,000/month TCO (3 years) Lower at scale Higher at scale Differentiation Full competitive moat Same tools as competitors Data ownership 100% yours Vendor-controlled Customization Unlimited Limited to vendor features Maintenance Your team or vendor […]
Original research based on 25+ Pharos Production AI projects delivered 2023-2026. Cost breakdowns by complexity tier, hidden costs analysis, team composition, ROI timelines and regional variation. Key finding: median AI MVP cost is $42,000 but hidden costs add 28-42% to first-year totals.
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AI consulting services cover AI readiness assessment, use-case discovery, technology and model selection, data infrastructure review, a prioritized roadmap and governance framework design. At Pharos Production we evaluate your existing systems, identify high-impact AI opportunities and deliver an actionable implementation plan with realistic timelines and resource estimates.
AI consulting cost depends on scope. A focused 2 to 3 week AI readiness assessment is the typical entry point, while a full strategy and roadmap engagement runs higher. A proof of concept usually costs 15 to 20 percent of the full project budget. We provide a fixed-price scope before any work begins.
AI strategy consulting defines what to build and why: readiness, use-case prioritization, ROI modeling and a roadmap. AI implementation consulting covers how to build it: architecture, model selection, data pipelines and delivery oversight. Pharos Production offers both, and can stay on to build the solution once the plan is validated.
We run a structured discovery that maps your workflows, data and goals against where AI delivers measurable value. We score candidate use cases on impact, feasibility and data readiness, then prioritize a shortlist.
The output is 3 to 5 use cases with expected outcomes rather than a generic list of AI possibilities.
Yes. We work with startups and small businesses as well as enterprises, scaling the engagement to your stage and budget. For smaller teams we focus on one high-value use case, a lean proof of concept and a pragmatic roadmap, so you validate AI before committing to a large build.
No. You do not need a large data team to begin. We assess your current data and infrastructure, and most engagements start with the data you already have.
Where gaps exist we recommend the minimum pipeline work needed for your first use case rather than a full data platform rebuild.
A readiness assessment takes 2 to 3 weeks and a strategy plus roadmap engagement typically runs 4 to 8 weeks. A proof of concept adds a few weeks depending on complexity. Advisory retainers are available for ongoing guidance, and we can transition into a build engagement when you are ready.
Yes. Unlike advisory-only firms, Pharos Production validates recommendations with working prototypes and can build the full production solution with the same team. This removes the handoff gap between strategy and delivery, so the people who designed your AI roadmap also ship it.
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. Specifically, 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.
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.
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].
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1 dayAfter 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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1-2 daysAs soon as the contract is signed, our dedicated team will jump into action on your project!
Same dayHeadquarters 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.
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