Research-backed concept · Validation stage
Build a safer path to production—for applications and AI agents.
Peak Consulting is testing an Azure-first engineering practice for lean teams. One track creates repeatable application delivery and cloud operations. The second productionizes agentic AI with evaluation, controlled tools, tracing, cost, and human approval.
The scope and price bands below are hypotheses under active customer discovery—not a claim that product-market fit has already been proven.
Market evidence
The operating gap is real. The exact offer still needs proof.
Current data supports the category: cloud operations are costly, platform practices are mainstream, and senior technical coverage is expensive. These figures justify discovery; they do not substitute for buyer commitments.
Survey samples differ from the proposed ICP. The Census figure is a broad account universe, not a count of qualified buyers. See the internal market model for assumptions and caveats.
The problem
Lean teams inherit enterprise-grade consequences
The target company runs systems that customers depend on, but one or two people carry cloud, delivery systems, observability, incidents, recovery, and cost. The problem is not a lack of tools. It is a lack of durable operational ownership.
Reliability work loses every sprint
Alert cleanup, recovery tests, IaC repair, and deployment hardening stay behind product work until an incident or customer deadline forces action.
One expert becomes the operating system
Context lives in one person’s head. Hiring another senior specialist is slow and still does not create coverage, standards, or a review system.
Tools surface work; they do not own it
Monitoring, cloud-cost, and platform products generate findings. Someone still has to rank the risk, implement changes, document recovery, and close the loop.
Broad providers can be the wrong shape
A global MSP brings scale and procurement comfort, but a smaller team may need senior product-aware engineering—not a new ticket queue or a wholesale tooling migration.
Two delivery tracks
One Azure production foundation, two focused outcomes
Both tracks reuse identity, network, policy, IaC, CI/CD, observability, cost, recovery, and client-owned operating evidence. They are researched and sold separately so the promise remains specific.
Azure Platform Engineering
Give product teams a paved road from repository to production using approved CI/CD, infrastructure, managed Azure services, AKS where justified, release telemetry, rollback, and clear ownership.
- Azure delivery-platform baseline
- Application golden path
- App Service, Container Apps, or AKS
- 90-day platform stabilization
AI Production Engineering
Move one Microsoft Foundry agent workflow from prototype to controlled production with versioned deployment, approved data and tools, evaluation gates, tracing, cost controls, human approval, and a kill switch.
- Agent production-readiness baseline
- One-workflow launch platform
- Evaluation, tracing, and tool boundaries
- 90-day AgentOps stabilization
Shared Azure production foundation
Landing zonesIdentityNetworkIaC
CI/CDObservabilityCostRecovery
Proposed commercial path
Start with outcomes. Earn the recurring model.
The relationship earns trust in stages. Each step is useful on its own and has a clear end. The first sale is not a subscription. Price bands are working hypotheses to be tested with real approval processes and delivery data.
Reliability Baseline
$12k–$18k working range
Ten business days: dependency map, SLO and incident baseline, recovery and IaC review, cost/control gaps, ranked risk register, and a 90-day plan.
Read-only first. Useful even if the client buys nothing else.
Managed Reliability Project
$25k–$45k working range
Six to eight weeks implementing the two or three highest-value items: a golden deployment path, actionable SLOs, tested recovery, IaC repair, or cost guardrails.
Fixed fee or milestones. New priorities replace old ones or require a change order.
90-day Stabilization Retainer
$6k–$12k/mo working range
Operate and measure the delivered changes, close a bounded follow-up backlog, coach the team, and identify which needs are temporary versus genuinely recurring.
It ends with an explicit stop, extend, or productize decision—not automatic renewal.
Productized Managed Service
$8k–$15k/mo future hypothesis
A standardized co-managed reliability backlog, planned capacity, client-owned automation, outcome reviews, and resilience exercises.
Introduced only after four to six clients need substantially the same ongoing scope.
Managed service describes ongoing operational ownership. Subscription describes how it is packaged and billed. The company starts with managed projects because they are easier to scope, approve, deliver, and learn from.
— Commercial modelThe outcome scorecard
- Change failure rate, deployment lead time, and deployment frequency
- Mean time to restore and repeat-incident count
- After-hours pages and actionable-alert ratio
- Critical services with an owner, SLO, runbook, and tested recovery path
- Cloud spend allocated to an owner or product
- Aged reliability backlog and roadmap cycle time
Each client selects three to five measures after the baseline. No universal improvement percentage is promised before current performance is measured.
Beachhead ICP
Designed for a specific operating moment
- 75–300 employees and roughly 10–60 product engineers.
- Customer-facing production systems with real downtime, data, or contractual consequence.
- Azure is material; AWS is acceptable where the playbook transfers.
- No mature platform team, or one to two people carrying the entire operational surface.
- A forcing event: incident, audit/customer request, budget miss, delayed migration, AI workload entering production, or an open platform role.
- Buyer: CTO, VP/Head of Engineering, or technical COO.
Why not the obvious alternatives?
| Alternative | What it does well | The gap this concept tests |
|---|---|---|
| Internal hire | Context, control, permanent capability | Slow fixed commitment, narrow coverage, and key-person risk |
| Large cloud MSP | 24/7 scale, certifications, broad operations | Senior attention and product-aware improvement for smaller teams |
| Consultancy | Deep project expertise and transformation | Continuity after the project and ownership of the operating backlog |
| Freelancer | Speed, flexibility, low entry friction | Peer review, backup coverage, and a transferable delivery system |
| Platform/observability tool | Visibility, workflow, and automation | Prioritization, implementation, runbooks, and organizational follow-through |
Scope discipline
The boundaries make the service safer and more credible
Included at launch
- Reliability evidence and risk prioritization
- Observability, SLOs, incident learning, and recovery readiness
- CI/CD and infrastructure-as-code reliability
- Cloud-cost allocation and operating guardrails
- Client-owned runbooks, diagrams, code, and automation
- Peer review and named backup for production work
Not included at launch
- End-user help desk, devices, or generic IT support
- Unlimited migrations or project work inside a retainer or later managed service
- Body-only staff augmentation as the core model
- Compliance certification or legal assurance
- A guarantee of zero incidents
- Primary 24/7 incident command before the capability is genuinely staffed
AI can accelerate research and delivery under human review. In the AI production track, it still cannot approve its own release, grant itself production access, or bypass external authorization and human approval for consequential actions.
— AI operating boundaryBusiness model
A viable niche if projects fund productization
A bottoms-up model starts with 106,329 U.S. firms in relevant 50–499 employee sector bands. At a deliberately modeled 20% fit rate, that is about 21,266 qualified accounts. The useful conclusion is not a giant TAM claim: a focused specialist firm needs only a tiny share.
7,478 accounts
Massachusetts and North Carolina firms in the same broad size and sector bands, before stack, need, and buying-fit filters.
Baseline → project → retainer
Modeled gross margins are 71.8% for a $15k baseline, 65.6% for a $35k project, and 63.3% for a $9k monthly stabilization retainer.
55% minimum gross margin
Change orders protect project scope. A $9k retainer is re-scoped or ended when it repeatedly exceeds roughly 55–60 blended delivery hours.
Fit rates, price bands, delivery hours, and margin figures are management assumptions for sensitivity analysis. They will be replaced with observed sales and delivery data.
Evidence-gated growth
Capability follows proof and revenue
-
Validate two segments and the paid baseline
20–24 qualified interviews, five offer reviews, and two real paid-purchase paths. See the validation plan →
-
Prove the baseline and managed project
One paid baseline, one bounded project, peer review, insurance, change control, and actual delivery-hour data.
-
Three baselines and two managed projects
At least 55% project gross margin, documented scope patterns, and 10–15% of core-team capacity reserved for productization.
-
Two 90-day retainers reveal what truly recurs
The same delivery team operates, measures, documents, and classifies reusable versus client-specific work. There is still no separate subscription team.
-
Create a managed-service team only when the evidence funds it
Four to six similar clients, 70–80% standardized work, two renewal cohorts, ≥55% margin, two-engineer coverage plus buffer, proven handoffs, and six months of runway.
-
Expand AgentOps, FinOps, and 24/7 coverage only after separate gates
The AI production track requires dedicated buyer evidence and a controlled first workflow. Each recurring capability requires repeated client demand, safe controls, delivery coverage, and economics that do not weaken the core service.
The next milestone is not a launch. It is two paid signals.
The core discovery sprint validates the initial segment and paid baseline. AI production receives a separate extension with buyers who already have a funded workflow or prototype. Recurring service design comes from delivery—not guesswork.
Read the validation plan →