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Solvios is the leading IT company in the business to give you growth, revenue, and a global platform.
We’re a US-based production-grade AI consulting company helping startups, scale-ups and enterprises move beyond AI pilots and POCs into AI systems that run in production, generate measurable returns and compound in value over time. Strategy, readiness assessment, generative AI consulting, agentic workflow design, MLOps architecture and the honest advisory that tells you what AI should and shouldn’t do in your business, delivered by senior AI engineers and consultants who’ve shipped AI in production across real industries.
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Most AI strategies fail because they start with the technology, not the business. We build AI strategies the other way around: starting with your business model, your operational bottlenecks, your competitive pressures and your data reality, then mapping the AI investments that will generate measurable returns within a defined timeline. Use-case prioritization scored by impact, feasibility and data readiness, a phased implementation roadmap with honest resource requirements, build-versus-buy decisions made without vendor bias and the executive advisory that gets your board, your CTO and your operations team aligned on the same AI direction.
AI readiness isn’t a binary. Most organizations are ready for some AI investments and genuinely not ready for others, and the difference between the two is usually data quality, infrastructure maturity and organizational change readiness, not budget. Our AI readiness assessments evaluate your data estate, your existing tech stack, your MLOps maturity, your team’s AI depth and the process landscape that AI would operate in. The output is a clear, honest picture of where AI will deliver value today, where it needs groundwork first and where it isn’t the right answer at all.
Generative AI consulting is where the gap between vendor marketing and production reality is widest. We consult on generative AI without the hype: LLM selection matched to your use case and your data sensitivity, RAG architecture designed for your actual knowledge base, prompt engineering frameworks that survive edge cases in production, evaluation harness design, hallucination mitigation strategies and the cost modeling that tells you what a generative AI feature actually costs to run at scale before it’s in front of your users. From customer-facing AI features to internal knowledge systems and AI-assisted workflows, grounded in what models can reliably do today.
Agentic AI is the most overhyped and most genuinely transformative category in enterprise AI right now, and the difference between a useful agent and an expensive failure usually comes down to the architecture decisions made before the first line of code. We consult on agentic AI systems: use-case viability assessment for multi-step autonomous workflows, agent architecture design across single-agent and multi-agent patterns, tool and API integration planning, human-in-the-loop design for the tasks where full autonomy isn’t appropriate yet and the evaluation framework that tells you whether your agent is actually performing reliably before you scale it.
A model that can’t be deployed, monitored or retrained reliably isn’t a business asset, it’s a liability. We consult on the MLOps and AI infrastructure layer that production AI actually demands: model registry design, inference infrastructure sizing and cost modeling, monitoring for model drift and performance regression, retraining pipeline architecture, GPU governance and the FinOps discipline that keeps your AI infrastructure bill from quietly tripling between board meetings. Cloud-agnostic across AWS SageMaker, Azure ML, Google Vertex AI and self-managed infrastructure where the use case justifies it.
Most AI value sits at the integration layer, where AI capabilities connect to the systems, workflows and data sources your business actually runs on. We consult on AI integration architecture: wiring LLM APIs into existing applications, connecting AI systems to CRM, ERP, data warehouse and communication platforms, designing the data pipelines that feed production AI and the API architecture that keeps AI features isolated, observable and replaceable as the model landscape evolves. Salesforce, HubSpot, SAP, Microsoft 365, Snowflake, Databricks and the long tail of enterprise systems AI needs to work alongside.
AI governance isn’t optional in regulated industries, and it’s becoming a procurement requirement everywhere else. We consult on responsible AI and governance frameworks: bias audit methodology, fairness metric design, model explainability approaches, data privacy architecture for AI workloads, regulatory compliance mapping for GDPR, HIPAA, EU AI Act and sector-specific requirements, AI risk register development and the internal governance model that keeps AI deployments defensible when a regulator, an auditor or a board member asks the hard questions.
Most AI consultants walk you through frameworks and leave. We stay through implementation because strategy without production delivery isn’t consulting, it’s commentary. Every layer below represents how we bring AI thinking into the actual work of building and operating AI systems, not just advising on them from a distance.
We evaluate potential AI use cases against your data reality, your operational context and your competitive position to surface the investments with genuine ROI, not the ones that make the best demo.
Highlights:
We evaluate potential AI use cases against your data reality, your operational context and your competitive position to surface the investments with genuine ROI, not the ones that make the best demo.
Highlights:
AI consulting isn’t one capability. It’s a category spanning strategy, data readiness, model selection, architecture design, infrastructure planning, integration engineering, governance and the change management that makes AI adoption stick. Here are the consulting capabilities we deliver and the standard we hold ourselves to on each one.
AI Opportunity Assessment & Prioritization
Structured methodology for identifying, scoring and sequencing AI use cases by business impact, data readiness and implementation complexity. We evaluate your operational landscape, interview your domain experts and produce a prioritized AI roadmap grounded in what your organization can actually execute, not what AI can theoretically do. Scored against measurable KPIs from day one.
Data Estate & Quality Assessment
AI systems are only as reliable as the data they run on. We assess your data estate against the requirements of your target AI use cases: data availability, quality, labeling coverage, pipeline maturity and governance posture. The output is an honest gap analysis and a data readiness roadmap that sequences the groundwork your AI initiatives actually need before model development begins.
LLM Selection & Evaluation
Choosing an LLM is an architecture decision with long-term cost, performance and compliance consequences. We evaluate models across OpenAI, Anthropic, Google, Meta, Mistral and open-source alternatives against your specific use case requirements: latency tolerance, context window needs, data privacy constraints, cost at scale and the accuracy benchmarks that actually matter to your use case, not the leaderboard results that don’t.
RAG Architecture & Knowledge System Design
Retrieval-Augmented Generation is the dominant pattern for enterprise AI systems that need to work with your proprietary data. We design RAG architectures that match your knowledge base structure, your retrieval accuracy requirements and your latency budget: chunking strategy, embedding model selection, vector database design, hybrid search patterns, reranking and the evaluation harness that measures whether retrieval is actually working before you ship.
Agentic System Architecture
Multi-step autonomous AI workflows require architectural decisions that most AI frameworks gloss over: task decomposition strategy, tool selection and API integration planning, state management across agent steps, error recovery patterns, human-in-the-loop insertion points and the evaluation framework that tells you whether an agent is reliable enough to operate autonomously on a given class of task. We design agentic systems for production reliability, not demonstration fidelity.
MLOps & Model Lifecycle Consulting
A model deployed without an operational framework degrades silently. We consult on the full model lifecycle: experiment tracking, model registry design, CI/CD for ML, inference infrastructure selection, monitoring for drift and performance regression, retraining trigger design and the documentation standards that make an AI system auditable when the business or the regulator needs to understand what it’s doing and why.
AI Governance & Policy Framework
AI governance is the framework that keeps AI deployments defensible as they scale. We design governance frameworks that cover model approval workflows, bias testing standards, explainability requirements, data access controls, incident response procedures for AI failures and the policy documentation that satisfies enterprise procurement, board-level AI oversight and regulatory review across GDPR, HIPAA, EU AI Act and sector-specific frameworks.
Change Management & AI Adoption
The tools are half the work. An AI system your organization won’t adopt is an AI investment that doesn’t pay back. We consult on the change management layer: stakeholder alignment, workflow redesign to accommodate AI handoffs, training programme design for AI-augmented roles, adoption metrics and the internal communication strategy that turns AI sceptics into the engineers and operators who make AI systems actually work.
We’re not just another AI consulting company. We’re the production-grade AI partner you bring in when the pilot has already failed, when the roadmap needs to survive contact with your data reality or when the board is asking why the AI investment hasn’t showed up in the business yet.
Every advisory recommendation we make is grounded in what actually ships and operates in production. We’ve built RAG pipelines, agentic workflows, inference infrastructure and ML systems that run in live environments. The consulting is informed by the engineering, not separated from it.
You work directly with senior AI engineers, ML architects and data scientists who’ve built AI systems in production across regulated industries, not consultants who learned the frameworks from whitepapers. The advice you get is grounded in real deployment experience.
We tell you when AI isn’t the right answer for a use case, when your data isn’t ready to support the model you want to build and when a vendor solution will outperform a custom build for the next three years. That honesty is what makes the recommendations worth acting on.
We don’t stop at the roadmap. Our AI consulting engagements connect directly to AI development, integration and MLOps delivery so the strategy your team aligns on is the same one the engineers execute. No translation loss between advisory and implementation.
Responsible AI, bias auditing, explainability requirements, GDPR, HIPAA and EU AI Act compliance are designed into the advisory, not added as a governance workstream after the model is already in production. Particularly important for healthcare, fintech and enterprise deployments.
We consult on the AI capabilities that are production-ready today, agentic workflows, multimodal systems, real-time RAG, on-device inference, and maintain an honest view of what’s still maturing, so your AI roadmap is timed to the actual state of the technology, not the hype cycle.
Most AI consulting engagements fail not because the strategy was wrong but because it was never grounded in the organization’s actual data, actual infrastructure and actual change capacity. Our AI consulting services follow a structured, evidence-based delivery methodology designed to surface those gaps early, when they’re cheap to address.
Here’s exactly how it works.
We map your business model, operational workflows, competitive landscape and strategic priorities to understand where AI can generate genuine value and where it’s being considered because a competitor announced it, not because the economics support it.
We assess your data estate, technology infrastructure, team AI maturity, process landscape and organizational change readiness against the AI use cases under consideration. The output is an honest gap analysis, not an optimistic readiness score.
We identify the AI use cases with the highest business impact, the best data support and the most realistic implementation path, then sequence them in a phased roadmap that builds organizational AI capability while delivering near-term value.
We design the AI architecture for the priority use cases: model selection, data pipeline design, RAG or fine-tuning strategy, inference infrastructure, integration architecture and the MLOps framework the system will need to operate reliably over time.
We validate critical architecture assumptions on a contained workload before full-scale development begins. The PoC answers the questions that matter: does the model perform well enough on your data, does the latency hold under realistic load and does the cost model work at the volume you need.
We oversee or directly deliver the AI system implementation, depending on your in-house engineering capacity: sprint reviews, technical architecture governance, integration quality assurance, model evaluation against the KPIs defined in the roadmap and the course corrections that keep production delivery on track.
We operate the AI layer in production or advise your in-house team on doing so: model performance monitoring, drift detection, retraining cadence management, cost optimization as usage scales and the quarterly AI architecture reviews that keep your AI estate aligned with both the evolving model landscape and your evolving business requirements.
Your AI consulting engagement doesn’t fit a template, and the contract shouldn’t either. The right model depends on your AI maturity, your in-house engineering depth and whether your need is strategic advisory, hands-on implementation oversight or both. Three models, all built for production-first AI delivery.
You’re running a multi-quarter AI transformation, building out an enterprise AI capability from the ground up, scaling AI across multiple business units or augmenting your in-house AI team without the cost and lead time of full-time specialist hires.
Hand-picked AI consultants, ML engineers, data scientists and a delivery lead working only on your AI programme. Strategy and delivery in the same team. AI architecture decisions made by the same people who build the systems.
Monthly retainer. No surprise invoices, no scope-creep billing. Team composition flexes as your AI roadmap progresses.
3 to 10 consultants and engineers
6-month minimum
Scales with 30-day notice
Most organizations start with a focused readiness assessment and evolve into a broader consulting and delivery engagement as the AI roadmap clarifies. Let’s figure out the right starting point together.
Building AI Foundations for the Businesses That Will Define the Next Decade. The organizations investing in production-grade AI consulting now won’t be the ones explaining to their board in two years why the AI programme generated impressive demos but no measurable business outcomes.
AI consulting looks different across industries. The regulatory posture, the data landscape, the risk tolerance and the use cases that actually generate business value all change the advisory. These are the verticals where we’ve consulted on and delivered AI systems in production.
Honest answers to the questions every CEO, CTO, head of product and head of data asks before they hire an AI consulting company. If something isn’t covered here, our AI consultants will walk you through it on a discovery call, no sales pitch, no frameworks for the sake of frameworks.
Look beyond the AI capability logos and the case study deck. The right AI consulting company asks harder questions than it answers in the first conversation: about your data quality, your infrastructure maturity, your team’s actual AI depth and what a failed AI investment would cost you in time and credibility. Evaluate on production delivery experience, not just strategic advisory credentials. Anyone who recommends an AI use case before they’ve assessed your data landscape isn’t the right partner.
End-to-end AI consulting services cover AI opportunity assessment and use-case prioritization, AI readiness assessment across data, infrastructure and team capability, AI strategy and roadmap development, generative AI consulting including LLM selection and RAG architecture, agentic workflow design, MLOps and AI infrastructure advisory, AI integration consulting, responsible AI and governance framework development and implementation oversight. The best engagements also include a proof of concept on the highest-priority use case before full-scale development begins.
A focused AI readiness assessment or strategy engagement: $15,000 to $50,000. A mid-complexity AI consulting programme covering strategy through to PoC delivery: $75,000 to $250,000. An enterprise AI transformation programme with ongoing implementation oversight and delivery: $300,000 to $1M+. Ongoing AI consulting and delivery retainers typically run $20,000 to $100,000 per month depending on team size and scope. We provide detailed estimates after a discovery session and won’t give you a number before we can stand behind it.
AI consulting covers the advisory layer: use-case identification, readiness assessment, strategy, architecture design, model selection, governance framework and implementation oversight. AI development covers the build layer: data pipeline engineering, model training and fine-tuning, system integration and MLOps. The two are most valuable when they’re delivered by the same team, because architectural decisions made in consulting have direct consequences for what’s buildable in development. We deliver both, deliberately connected.
Generative AI consulting starts with the use case, not the model. We assess whether generative AI is the right approach for the problem at hand, which model family fits your data sensitivity, latency and cost requirements, what RAG or fine-tuning strategy best suits your knowledge base and how to build an evaluation harness that measures whether the system is actually reliable before it reaches production users. We don’t lead with model recommendations. We lead with the business requirements that should drive the model decision.
Three models: Dedicated AI Consulting and Delivery Team (for multi-quarter AI programmes and enterprise AI transformations), Time and Material (for exploratory AI work, active discovery and evolving scope) and Fixed Cost (for well-scoped deliverables like readiness assessments, strategy documents and PoC builds). We recommend honestly based on your situation, not based on which model generates the most billing.
Responsible AI and governance are designed into the advisory from day one, not added as a workstream after the model is already in production. We build AI governance frameworks that cover model approval workflows, bias testing standards, explainability requirements, data privacy architecture, incident response procedures and regulatory compliance mapping for GDPR, HIPAA, EU AI Act and sector-specific requirements. For healthcare and fintech clients, governance design is a non-negotiable part of every AI consulting engagement.
Yes, and it’s one of the more common engagements we run. Failed AI pilots usually trace back to one of three root causes: data that wasn’t ready to support the model, architecture decisions that worked in the demo but didn’t survive production load or organizational change management that was never part of the plan. We conduct a post-mortem assessment, identify the actual failure mode and design the path forward that addresses the real problem rather than repeating the same approach with a different model.
A focused AI readiness assessment: 2 to 4 weeks. An AI strategy and roadmap: 4 to 8 weeks. A PoC build and validation: 6 to 12 weeks. A full AI transformation programme covering strategy through to production delivery: 6 to 18 months, executed in phases with measurable milestones. We provide milestone-based timelines in writing before the engagement begins.
Healthcare, fintech, retail and ecommerce, logistics and SaaS product are our primary verticals, where we have both AI consulting depth and production delivery experience. We also consult on AI for manufacturing, real estate technology, professional services and enterprise platforms where the use case is well-defined and the data infrastructure supports it.
Against the business outcomes defined at the start of the engagement, not against the quality of the deliverables produced. For strategy engagements: quality of the use-case roadmap, organizational alignment achieved and accuracy of the readiness assessment as validated by the implementation that follows. For delivery engagements: model performance against pre-defined accuracy and latency KPIs, production reliability, cost per inference against the model and the business metric the AI system was built to move. We set success metrics before the engagement starts and report against them throughout.
Yes. The nature of the engagement differs significantly. For startups, AI consulting typically focuses on use-case selection, avoiding over-engineering, making the most of limited data and choosing AI investments that generate value before Series B rather than after. For enterprises, it focuses on governance, integration with existing systems, change management across large organizations and the AI infrastructure that scales with the business. We adjust the consulting approach to match the organization’s stage and constraints.
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