AI consulting that turns decisions into measurable business impact.
StratAI helps enterprises move beyond the AI hype: finding real opportunities, building responsible strategies and delivering measurable outcomes. Five service lines, end to end, from use case discovery to managed deployment.
Built for enterprise AI realities, not ideals.
StratAI is designed for the messy, constrained environment of a real company, not a perfect theoretical sandbox.
Business-first lens
We start with your P&L, not the technology. Every recommendation is grounded in commercial value and feasibility, not vendor roadmaps.
Vendor-neutral
Our job is to find what is right for you, not what earns the largest referral fee.
Structured methodologies
A repeatable, proven framework rather than improvisation, so you get speed, consistency and predictable outputs.
Governance by design
Responsible AI thinking from the first workshop. Compliance and ethics are load-bearing pillars, not an afterthought.
Long-term partnership
Engagements evolve with your organisation's AI maturity instead of ending with a one-off strategy document.
Practitioner-led
Our consultants have held operational roles inside enterprises, including heads of data, digital leaders and technology executives.
StratAI tuned to your industry.
Each sector carries different regulation, data boundaries and ROI expectations. We align use-case portfolios, governance and vendor selection to those realities—vendor-neutral and evidence-based.
Public sector
Citizen-service automation, policy and procurement document intelligence, multilingual knowledge assistants and sovereign deployment options—with audit trails leadership and auditors expect.
Why consider ScalovateData residency, transparent open-source stacks, independent RFP scoring and air-gapped paths without betting the nation’s data on a single hyperscaler AI API.
Energy and resources
Operations knowledge RAG over HSE, maintenance and engineering corpora, field-friendly assistants on private infrastructure, and FinOps to stop runaway inference spend across scattered pilots.
Why consider ScalovateProtect IP and operational data, work in disconnected or hybrid sites, and prioritise use cases with measurable uptime and safety impact—not generic chatbots.
Retail and consumer
Store and HQ assistants on product and policy data, demand and markdown analytics support, customer-care knowledge bases with citations, and fast MVP cycles tied to margin and conversion KPIs.
Why consider ScalovateMove from hype to ranked use cases in weeks, control token and licence cost, and integrate with existing commerce and ERP data without lock-in.
Healthcare providers
Administrative and operational AI—scheduling, prior-auth document triage, internal clinical policy Q&A with citations, and governed assistants that never train on patient data in public clouds.
Why consider ScalovateHIPAA-aligned private stacks, explainability for oversight committees, and governance frameworks that scale beyond a single pilot ward or clinic.
Banking and capital markets
Regulatory and policy RAG, KYC and onboarding document intelligence, internal risk and audit assistants, and AI FinOps across models, APIs and vendor contracts.
Why consider ScalovateMeet examiner expectations with sovereign deployment, model risk discipline, and TCO models procurement and the board can defend.
Insurance
Claims and policy document intelligence, broker and adjuster knowledge assistants, underwriting research with sourced answers, and portfolio rationalisation across overlapping AI tools.
Why consider ScalovateImprove loss ratios and cycle time with cited outputs, reduce vendor sprawl, and embed responsible-AI policies before models touch customer-facing workflows.
AI use case identification
Stop guessing where AI can help. A structured discovery methodology surfaces high-impact, feasible use cases aligned to your strategy, data and operations. A prioritised portfolio typically takes 4 to 6 weeks.
Most AI initiatives fail before they begin. Without a disciplined approach, teams spend months on use cases that are technically unfeasible, commercially immaterial or strategically misaligned.
Four-phase discovery process
Strategic alignment workshop
Map strategic priorities, OKRs and the business problems leadership most urgently needs to solve.
Opportunity landscape scan
Scan Finance, Operations, HR, Sales, Supply Chain and Customer Experience for horizontal and vertical AI opportunities.
Data and feasibility assessment
Assess data assets, quality and infrastructure readiness to see what is actionable today.
Value scoring and prioritisation
Score each use case on financial impact, complexity, time-to-value and risk, then sequence a roadmap.
Deliverables
START WITH THE FREE AI USE CASE GENERATOR (opens in a new tab)
AI strategy for enterprises
A board-ready AI strategy that turns ambition into an executable plan, with the right foundations, sequencing and governance from day one. Strategy is not a slide deck. It is a system.
What we cover
- AI vision and ambition setting
- Operating model design: central, federated or hybrid
- Technology architecture blueprint, including build versus buy
- Investment planning and business cases over a 3 to 5 year horizon
- Talent and capability strategy: build, hire or partner
- Change management and adoption
The StratAI enterprise AI strategy framework
Value
Where does AI create the most value for your business and customers?
Capability
What people, processes and platforms are needed to deliver that value?
Governance
How do we keep AI responsible, controlled and trusted?
Scale
How do we move from early wins to an enterprise-wide AI capability?
Deliverables
AI governance and responsible AI
Frameworks, policies and oversight that make your AI trustworthy. Ungoverned AI is a liability, not an asset. Done well, governance is the foundation that lets you scale AI with confidence.
Regulatory compliance
EU AI Act, GDPR and sector-specific AI regulation requirements.
Bias and fairness risk
Discriminatory outputs, unrepresentative training data and disparate impact.
Data privacy and security
Protecting sensitive personal and commercial data across training, inference and outputs.
Explainability and auditability
Showing how AI decisions are made, especially in high-stakes domains such as credit, hiring and healthcare.
Five pillars of responsible AI
- Fairness and non-discrimination
- Transparency and explainability
- Human oversight and control
- Privacy and data stewardship
- Accountability and auditability
Deliverables
AI optimisation and cost engineering
AI spend without AI ROI is expensive tech debt. Cloud inference, overlapping tools, underperforming models and opaque contracts push AI cost far beyond the original business case. We fix that, systematically and measurably.
Six levers of AI optimisation
AI cost transparency and FinOps
Attribute compute, storage, API and tooling cost to use cases and business units.
Model performance optimisation
Evaluate accuracy, latency and drift to decide what to retrain, tune or replace.
Infrastructure right-sizing
Remove over-provisioned compute and move to cost-appropriate deployment patterns.
AI portfolio rationalisation
Find overlapping vendors and underused licences to renegotiate or retire.
Prompt and pipeline engineering
Cut token use and API cost for LLM applications without losing output quality.
AI ROI measurement framework
Metrics and reporting that show the commercial return of each AI initiative.
From audit to savings in 8 weeks
Discovery audit
Weeks 1 to 2: inventory AI tools, costs, models, infrastructure and contracts.
Analysis and diagnosis
Weeks 3 to 4: performance benchmarking, cost attribution and waste identification.
Recommendations
Weeks 5 to 6: prioritised roadmap with quantified savings estimates.
Implementation
Weeks 7 to 8: quick wins executed and a longer programme started with tracking in place.
Managed AI consulting services
An embedded, on-demand AI consulting capability: strategic guidance, technical expertise and operational support without the cost or hiring risk of building it in-house.
Strategic Partner
Senior AI advisory for leadership teams at the early stages of their AI journey.
- Monthly strategy sessions with a senior advisor
- Quarterly AI landscape briefings
- On-demand advisory calls, up to 4 hours a month
- Vendor proposal review
- Board presentation support
Active Programme Support
Hands-on support for organisations already running AI programmes. Includes everything in Advisory.
- Dedicated engagement lead
- Weekly check-ins with delivery teams
- Attendance at AI steering group meetings
- Monthly governance and risk review
- Up to 3 specialist consultants on demand
- Quarterly AI maturity assessment
Full AI Consulting Capability
A fully embedded AI consulting function for large transformation programmes. Includes everything in Embedded.
- Dedicated multi-disciplinary team
- Hands-on delivery support for pilots
- Vendor management and procurement support
- Regulatory engagement support
- Training and capability building
- Executive AI education sessions
Why a retained model beats project-by-project
Context compounds
We learn your data, technology decisions and stakeholders over time, and that context stays with you.
Continuity of governance
Models drift and regulations change. Ongoing oversight keeps AI responsible, not an annual policy review.
Speed of access
When a vendor pitches or a pilot underperforms, expertise is already in your corner.
Cost efficiency
Senior expertise at a fraction of the cost of a full-time Chief AI Officer or principal AI architect.
Procurement, vendor enablement and engineering.
The same team supports buyers choosing technology, vendors positioning products, and organisations building and securing AI.
Evaluation, RFP and TCO engine
Big Four style discipline applied to technology buying: we write the RFP, score the proposals and model the full cost.
- RFP writing and supplier Q&A management
- Independent proposal evaluation and scoring reports
- Five to ten year TCO and ROI models
- Proprietary versus open-source and sovereign comparison
Vendor positioning and MVP lab
We help vendors reach customers with advisory, solution design and working MVPs, documented in reports procurement teams trust.
- Positioning against the alternatives buyers weigh
- Reference architectures and customer solutions
- MVPs for customer proofs of concept
- Customer-ready TCO and ROI reports
AI engineering and cybersecurity
Our engineers build on open-source technology and our security specialists review every stack.
- Open-source LLM fine-tuning, RAG and generative AI
- Custom software in React, Python, NextJS, Flutter, C++ and Rust
- Secure deployment on-premise, in private cloud or hybrid
- Security review, including Wiqaya X static analysis
Our method for navigating AI complexity.
StratAI systematically identifies, evaluates and deploys AI use cases that deliver measurable business value.
Identify
Find candidate use cases.
Validate
Test the business case.
Evaluate
Weigh feasibility and risk.
Prioritize
Rank by impact.
Prototype
Build an MVP fast.
Pilot
Prove it with users.
Scale
Roll out enterprise-wide.
Value calculator.
Estimate the effect of independent advisory on cost and cycle time, from either side of the table.
Illustrative model for discussion only. It is not a quote or a guarantee. A real engagement uses your data.
Automated proposal scorer.
Do not let buzzwords hide cost. We score proposals on data sovereignty, true TCO, security and SLA commitments, then compare them with open-source and sovereign alternatives.
- Reads the fine print and licensing add-ons
- Benchmarks against sovereign open-source options
- Produces a board-ready comparison report
One flexible model. Three ways to build.
Choose how much you own and how much we carry.
License and build
Purchase the source code. Your team owns the roadmap, pace and full control from day one.
Collaborative build
Your engineers and ours work side by side, with built-in knowledge transfer and shared capacity.
Full delivery
End-to-end implementation: build, customise, integrate, deploy, train and hand over, on fixed or milestone pricing.
AI Use Case Generator
Know where AI fits before you invest. Build your use case and generate the strategic groundwork that consulting firms typically take months and six figures to deliver. For Scalovate, that is day one.
Frequently asked questions.
Short answers about StratAI and how we work.
What is StratAI?
StratAI is Scalovate's AI consulting practice and framework. It helps enterprises identify, validate, evaluate, prioritise, prototype, pilot and scale AI use cases, with governance and cost control built in.
How long does AI use case identification take?
A prioritised portfolio of 20 to 60 AI use cases is typically delivered within 4 to 6 weeks.
How long does an AI cost optimisation audit take?
The process runs from audit to savings in 8 weeks: discovery audit in weeks 1 to 2, analysis in weeks 3 to 4, recommendations in weeks 5 to 6 and implementation in weeks 7 to 8.
Does Scalovate recommend open-source and sovereign AI?
Yes. Scalovate is open-source first, which reduces vendor lock-in and total cost of ownership. We also design private and sovereign AI stacks that keep your data under your control.
What managed AI consulting options are available?
Three retained models: Strategic Partner (advisory), Active Programme Support (embedded) and Full AI Consulting Capability (enterprise).
How do I get started?
Try the free AI Use Case Generator to shape your first use cases, then book a conversation with the consulting team. No commitment is required.
Ready to transform your vision?
Tell us what you want AI to achieve. We will help you find the use case, build it and own it.
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