SaaS & AI Advisory -
23 Questions Answered.

Mavis Dx helps mid-market ISVs reposition their SaaS product from a feature conversation to a CXO-level business value conversation. This includes building value messaging frameworks, pre-sales playbooks, competitive battle cards, and bid management processes that help your sales team win against larger, better-resourced competitors. The outcome is a repeatable selling system - not a one-off pitch deck.

Technical strength rarely decides enterprise SaaS deals. CXO buyers evaluate risk, vendor credibility, and business outcome evidence - not feature lists. They are asking: Will this vendor still exist in 3 years? Can they handle our scale? What happens when things go wrong? The gap is almost always in how the product is positioned, not what it does. We close that gap by building the commercial and operational narrative that makes enterprise buyers confident enough to sign.

Prolonged sales cycles are almost always caused by unresolved buyer concerns - about security, about operational reliability, about vendor viability, about the commercial model. We work on three levers simultaneously:

• Pre-qualification: Better ICP definition and entry-point messaging that attracts buyers who are already close to a decision.
• Friction removal: Security baselines, compliance evidence, and operational proof points that eliminate the most common delay triggers.
• Deal velocity: Bid management frameworks and executive alignment tools that keep enterprise deals moving through approval layers.

In Reference Profile 5 (healthcare ISV), this combination compressed sales cycles from 18 months to 4–5 months.

Cloud cost reduction and reliability are not in conflict when you have a structured FinOps framework. We start by building a cost-to-serve model at the customer and transaction tier level, identify waste and over-provisioning, then engineer the right infrastructure footprint for actual workload patterns. Reliability is a design constraint, not a variable we trade away. In our AWS loyalty platform engagement, we reduced hosting costs by 30% while simultaneously increasing platform gross margins from 30% to 88%.

A cost-to-serve model maps your actual infrastructure and operational costs to the revenue units that generate them - typically by customer tier, transaction type, or feature set. Without it, you are pricing by instinct and your margins are unpredictable. With it, every commercial decision - discounting, bundling, expansion pricing, customer onboarding - has a financial foundation. It is also the foundation for usage-based and consumption pricing models, which enterprise buyers increasingly prefer.

This pattern typically has three root causes:

• Architecture over-provisioning: Infrastructure sized for peak load rather than actual workload patterns, with no auto-scaling discipline.
• Customer-level cost invisibility: No mechanism to attribute costs to specific customers or segments, so unprofitable customers are subsidised invisibly.
• Discount leakage in commercial agreements: Contracts that allow usage growth without corresponding revenue growth.

We address all three through a Cloud FinOps engagement that includes a cost model, a governance framework, and commercial alignment.

This is one of the most common and fixable problems in mid-market SaaS. Enterprise buyers - especially those in banking, healthcare, insurance, and government - run structured CISO-level security reviews. We build the security posture, documentation, and audit evidence you need to pass those reviews and reduce the security review stage from weeks to days. In our financial services ISV engagement (Reference Profile 2), this reduced the B2B sales cycle duration by 30%.

We work with the major frameworks relevant to mid-market SaaS:

• International: ISO 27001, SOC 2 Type II, GDPR
• Healthcare: HIPAA, NABH digital health guidelines
• Financial Services: PCI-DSS, RBI cloud security guidelines
• GCC-specific: MALAFI and RIYATI sovereign healthcare data exchange, UAE IA standards
• Cloud benchmarks: CIS Controls for AWS, OCI, and Azure environments

The scope depends on your target markets and buyer profile, which we assess during the diagnostic.

A Shared Security Model defines the boundary between what your cloud provider secures and what you, as the ISV, are responsible for. Many mid-market ISVs misunderstand this boundary - assuming their cloud provider covers more than it does, which creates gaps that enterprise buyers and security auditors identify immediately. We review and document this boundary explicitly, which is often the single most credible step an ISV can take in a CISO-level review.

Operational maturity means your platform can meet the availability, recovery, and incident response standards that enterprise buyers require - and that you can prove it with documentation and live evidence. Practically, this means having tested DR and BCP runbooks, tiered SLA definitions, a working incident management playbook, and the operational metrics to show enterprise buyers your platform is reliable enough to stake their business on. It is the operational equivalent of a financial audit - enterprise buyers need it before they commit.

We start with an operational maturity diagnostic that identifies root causes - whether they are in architecture single points of failure, process gaps, tooling inadequacy, or team capability. From there, we build the runbooks, monitoring frameworks, and escalation processes that eliminate recurrence. The diagnostic typically takes 2–3 weeks and produces a prioritised gap list with a Stream-based remediation plan. In Reference Profile 4, this approach eliminated recurring outages and reduced escalations by 90% within the first year.

Disaster Recovery (DR) focuses on restoring technology systems after a failure - your RTO (Recovery Time Objective) and RPO (Recovery Point Objective) targets. Business Continuity Planning (BCP) is broader - it covers how your business continues operating during any disruption, not just technical failures. Enterprise buyers increasingly require both, especially in regulated industries. Most mid-market ISVs have partial DR plans but no tested BCP. We build and test both, and translate the outcomes into pre-sales assets that reduce enterprise buyer anxiety.

Most AI pilots fail at the architecture layer, not the model layer. A pilot proves the model can do the task. Production asks different questions: where the data goes and who can see it, how the AI component integrates with the ERP, CRM or core platform, what happens when the model is slow or wrong, and what each transaction costs at ten times the volume. When nobody designed for these questions, they all arrive at once and the initiative stalls. We address this with an AI Architecture Readiness Diagnostic that tests an initiative across five lenses: business alignment, data and integration, security and compliance, operability, and unit economics. See AI-Ready Enterprise Architecture.

Yes, but not the heavyweight version. Mid-size companies do not need a large EA function or months of documentation. They need a small set of architecture principles, a reference architecture that every AI initiative builds on, and a review before each initiative is funded. Without that, three teams build three incompatible AI stacks and the fourth use case costs more than the first. We apply the TOGAF Architecture Development Method in a deliberately lightweight way, led by a TOGAF 10 certified Enterprise Architecture Practitioner, and can act as your fractional chief architect through Architecture Governance as a Service.

They work at different layers and are strongest together. TOGAF sets direction at enterprise level: business capabilities, architecture principles, target state and governance. The cloud frameworks set the standard at platform level: security, reliability, cost, performance and operations on a specific cloud. We use the TOGAF ADM to define what the AI architecture must achieve, then review each platform design against the provider's own framework: the OCI Cloud Adoption Framework (OCAF) and OCI Well-Architected Framework, the AWS Well-Architected Framework with its Generative AI and Machine Learning Lenses, the Microsoft Azure Well-Architected and Cloud Adoption Frameworks, and the Google Cloud Well-Architected Framework. Where you run more than one cloud, one set of principles spans all of them.

By building the business case from the unit up, the way a Deal P&L is built. We first agree a cost-to-serve baseline with finance: the current cost per transaction or process. We then model the full run-cost of the AI version, including inference and token charges, platform and storage, observability, guardrails and the human review that stays in the loop, at today's volume and at three and ten times that volume. Savings are split into cash savings, avoided future cost, and capacity release, because only the first two reach EBITDA directly. The output is an EBITDA bridge with payback, sensitivity to model pricing, and pre-agreed kill criteria. We show every case on EBITDA, EBIT and cash, because capitalised build costs can make an AI investment look better on EBITDA than it is on EBIT or cash.

It is a fixed-fee, two to three week review of one AI initiative, either before it is funded for production or when it has stalled. We assess it across five lenses: business alignment, data and integration, security and compliance, operability, and unit economics. You receive a readiness scorecard, a risk register and a 90-day roadmap. The assessment is independent. It can recommend proceeding, redesigning, pausing, or using a different delivery partner, and any commercial relationship with a recommended implementation partner is disclosed to you in writing.

Yes. This is one of the most common reasons ISVs come to us. Enterprise buyers in banking, healthcare, aviation and pharma now ask specific questions about AI: data residency, model and vendor dependencies, prompt injection and output controls, human oversight, and auditability. We build the reference architecture and documentation that answer those questions, mapped to the relevant cloud Well-Architected Framework and to the customer's questionnaire, and combine it with our Security Compliance work where CIS benchmarks, ISO 27001 or India's DPDP Act 2023 obligations apply.

It depends on volume, growth and how proprietary the workflow is. AI SaaS is all operating expense: per-seat licences and onboarding hit EBITDA immediately, but it deploys fast and suits standard workflows such as HR or basic CRM. A custom agentic build needs engineering investment up front, then runs on tokens, infrastructure and maintenance, so its per-unit cost falls sharply at scale. We model both over three years and calculate the Year 1 volume at which build overtakes buy. We calculate that crossover twice: on EBITDA, and including the capital outlay on EBIT and cash, because capitalising development cost keeps it out of EBITDA and can flatter a build. The recommendation rests on the stricter test. See the build-vs-buy model.

On time-and-materials, the client keeps the efficiency. In an illustrative engagement of 1,000 hours billed at $100 and delivered at $60, revenue is $100,000 and gross margin 40%. If agentic delivery halves the hours, revenue halves and absolute gross profit halves with it. On a $100,000 fixed-price contract for the same scope, delivery becomes 500 hours at $30,000 plus about $3,000 of agent compute, so revenue holds and gross margin rises to 67%. Capturing that requires an agentic delivery stack with human-in-the-loop gates, blended unit economics of labour plus inference, outcome-based contracts backed by automated acceptance tests, redesigned delivery pods, and a plan for the hours released.

Traditional management consultants produce decks and frameworks and disengage. Mavis Dx stays through implementation. Our advisory is grounded in real operational experience running $80M+ SaaS platforms - not academic frameworks. We also operate on a no-lock-in model: minimum one month, outcome-defined, full autonomy to disengage. And we use AI tools deliberately - to accelerate diagnostics, improve framework precision, and speed up deliverables - as a multiplier, not a substitute for the foundational work.

Individual Streams run between 6 and 14 weeks depending on scope. You will see interim deliverables - a playbook, a framework, a cost model, an architecture blueprint - before the Stream closes. The first tangible business impact typically appears within the first 4–6 weeks of a well-scoped Stream. For example, a GTM Stream would produce an early draft of the value messaging framework and battle cards by week 3, with the full pre-sales playbook by week 6.

We offer a no-obligation 30-minute discovery call to understand your situation, identify the highest-impact gaps, and define what an initial Stream would look like. After that conversation, you will have a clear picture of scope, expected outcomes, timeline, and retainer - with no pressure to proceed. If we are not the right fit, we will tell you so directly. The discovery call is not a sales pitch. It is a diagnostic conversation.