AI Consulting for VPs of AI and Heads of AI
AIDOLS helps VPs of AI ship production systems faster, scale teams from 1 to 20 without vendor sprawl, and consolidate the MLOps stack — under fixed fees with no proprietary lock-in. Every engagement is staffed with senior engineers who write code on the call, integrates with the team and stack you already run, and ships handover artifacts your in-house team operates after the SOW closes.
What VPs of AI and Heads of AI get from AIDOLS
Specific outcomes scoped against the metrics the VP of AI office already tracks — not generic value props.
Capability bridge while you scale from 1 to 20
Senior AI hires take months to close. AIDOLS' Scale tier ($25K/month) bridges capability while the team scales — a common pattern is to ship the first 1–3 production systems with AIDOLS while the in-house team is hired and trained, then transition operations under a Scale wind-down.
MLOps stack consolidation, not proliferation
AI teams tend to accumulate MLOps tools quickly — model registry, feature store, evaluation harness, serving, observability, drift detection. AIDOLS Sprints produce a stack consolidation analysis against your existing platform commitments and ship Builds against the consolidated stack rather than adding to the inventory.
Faster time-to-production on the queue your team cannot get to
Every VP of AI has a backlog of high-value use cases the in-house team cannot reach without delaying current commitments. AIDOLS Build engagements clear specific backlog items in 90 days at fixed fee with full handover, freeing in-house capacity for higher-priority work.
Vendor evaluation grounded in production deployments, not vendor decks
AIDOLS produces vendor evaluation artifacts grounded in production deployments across the MLOps stack — model registries, serving infrastructure, evaluation platforms, vector databases — based on actual operational experience rather than vendor sales decks. Recommendations are vendor-neutral by design.
The 4 problems we solve for VPs of AI and Heads of AI
Your backlog is growing faster than your team. Hiring will not close the gap inside the budget cycle.
VPs of AI typically operate with a use-case backlog far larger than what the team can ship against current capacity, and hiring closes the gap slowly: senior ML engineers take months to recruit, and onboarding to productivity adds more time. The backlog grows during that window, and the gap between executive expectation and team output widens.
AIDOLS Build engagements clear specific backlog items in 90 days under a fixed fee with full handover to the in-house team. AIDOLS takes the priority backlog items the in-house team cannot reach without delaying current commitments — often 1–3 systems shipped externally while the team focuses on higher-priority strategic work.
Your MLOps stack keeps growing. Much of it overlaps. The team operates none of it deeply.
AI teams scaling from 1 to 20 accumulate MLOps tools as individual engineers select tools for specific projects. The result is operational debt: few team members operate any of them deeply, vendor contracts overlap on capability, and the procurement spend is not defensible at the next budget review.
AIDOLS Sprint engagements produce a stack consolidation analysis: capability map, overlap analysis, decommissioning sequence against existing platform commitments. Recommendations consolidate to a smaller set of strategic platforms aligned with what the team operates deepest, with clear sequencing for vendor decommissioning over 6–12 months.
You cannot afford to be locked into a platform vendor that pivots their pricing model.
AI platform vendors change pricing often — model API pricing changes, per-seat license adjustments, capability gating moves. VPs of AI who committed deeply to a single vendor face the choice between absorbing cost increases (margin pressure) and migrating off (a rebuild).
AIDOLS standardizes on portable open infrastructure (Kubernetes, vLLM/TGI, MLflow, OpenTelemetry, dbt) and builds vendor-substitutability into every Build via model routing and abstraction layers. Where commercial vendor capability is required, the integration layer is designed for substitution rather than lock-in.
Vendor evaluation eats time you do not have. The information you need is in production deployments, not vendor decks.
Evaluating MLOps vendors (model registry, serving, evaluation, vector database, feature store) takes weeks of senior team time per category. The information that actually matters — operational quality at scale, support response, contract realities, hidden cost — is rarely in vendor materials and only available from teams already running the vendor in production.
AIDOLS produces vendor evaluation artifacts based on actual production deployments across the MLOps stack. The artifacts cover capability, operational quality, total cost of ownership, vendor health, and substitution cost — grounded in operational experience rather than vendor sales material.
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Start the Assessment →How a VP of AI engagement typically works
Four steps from kickoff to production cutover. Fixed fee at every step.
VP of AI + team kickoff
Joint workshop with the VP of AI, the engineering lead, and the use-case sponsor. Agree the scope (backlog item, MLOps consolidation, vendor evaluation, hiring bridge) and lock the SOW.
Architecture + integration design
AIDOLS engineers integrate with your existing MLOps stack, evaluation harnesses, and CI/CD. Architecture decision records produced for the in-house team to keep.
Build + handover prep
Engineering work in your repo, weekly architecture review with the in-house team. Handover documentation drafted progressively rather than at SOW close. In-house engineer paired on the Build for knowledge transfer.
Production cutover + handover
Production deployment, 30-day post-launch verification against KPI, full handover of source, weights, evaluation harness, runbook, and architecture decision records. Optional Scale retainer for ongoing operations.
Pricing model that fits a VP of AI's budget cycle
Three transparent fixed-fee tiers — no time-and-materials drift, no partner-pyramid markup, no surprise change orders.
Best for: VPs of AI scoping MLOps stack consolidation, vendor evaluation, or backlog prioritization. Produces an artifact set the in-house team can act on without further consulting work.
- •Data + infrastructure audit
- •Prioritized opportunity map with ROI estimates per use case
- •Target-state reference architecture
- •90-day implementation roadmap with named owners
- •Executive readout deck
Best for: VPs of AI ready to ship a backlog item in 90 days with full handover to the in-house team.
- •Production AI system shipped end-to-end
- •Data engineering, model development, MLOps deployment
- •Integration into existing systems
- •Post-launch evaluation harness + monitoring
- •For a Build, if the deployed system does not generate ROI exceeding its fee within 90 days, the fee is refunded in full; the Sprint, an assessment, carries no refund, and Scale's refund is prorated.
Best for: VPs of AI scaling teams from 1 to 20 who want capability bridge at $25K/month while in-house hires close, plus ongoing operations on shipped systems.
- •Ongoing engineering retainer post-deployment
- •Model retraining, monitoring, drift detection
- •Incident response + evaluation harness updates
- •One new feature release per month
- •An alternative to a full-time senior MLOps hire
AIDOLS vs Big Four for a VP of AI
| Dimension | AIDOLS | Big Four |
|---|---|---|
| Pricing model | Fixed fee per tier; written, no T&M drift | Time-and-materials retainer; partner-pyramid markup |
| Engineering integration | Inside your repo, your stack, your CI; in-house engineer paired | Vendor sandbox; handover at SOW close, knowledge gap inevitable |
| Vendor neutrality | No platform reseller relationships; recommendations are evaluation-grounded | Often platform-aligned; vendor recommendations follow partnerships |
| Time to backlog clearance | 90 days per Build; clears items in-house team cannot reach | Multi-month engagements; rarely clears the in-house backlog |
| MLOps stack posture | Consolidation-oriented; fewer platforms, operated deeply | Often expands stack via vendor partnerships |
| Hiring bridge | Scale retainer ($25K/month) bridges in-house hires | No structured bridge to in-house team |
| IP + handover | Source + weights + IaC + runbook transfer at SOW close | Often proprietary platform with vendor lock-in |
| Exit clarity | Scale: 60-day notice after a 12-month minimum; clean handover | Long retainers; knowledge often stays with the firm |
What a VP of AI should ask before hiring an AI consulting firm
Seven questions to put on every shortlist call. Firms that cannot answer crisply on all seven are not engineering-first.
- 1.Which engineer is personally writing the code, and what are their last 3 production AI deployments?
- 2.Will an in-house engineer be paired on the Build for knowledge transfer?
- 3.What handover artifacts ship at SOW close — source, weights, evaluation harness, IaC, runbook, ADRs?
- 4.Is the firm vendor-neutral, or are vendor recommendations driven by reseller relationships?
- 5.How does the firm scope MLOps stack consolidation vs. extension?
- 6.How does the Scale tier wind down once the in-house team is up?
- 7.How many production AI systems has the firm shipped at companies scaling AI teams from 1 to 20?
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Run the ROI Calculator →Common objections from VPs of AI and Heads of AI — and our honest answers
"My team should build this themselves."
They should — eventually. AIDOLS engagements at growth-stage AI teams are scoped against backlog items the in-house team cannot reach without delaying higher-priority strategic work. The Build clears the item in 90 days with full handover; the in-house team operates the system after SOW close.
"We do not want a consultancy in our codebase."
AIDOLS works inside your repo with PRs reviewed by your team, not in a vendor sandbox. The pattern is structured for handover from day one — in-house engineer paired on the Build, handover documentation written progressively, runbook designed for your team to operate.
"We already have an MLOps platform vendor we are committed to."
AIDOLS deploys against your existing MLOps stack rather than replacing it. Sprint engagements produce stack consolidation analysis against your existing commitments — typically the recommendation extends or rationalizes what you already own rather than introducing a net-new vendor.
"We tried a freelance ML engineer. The model never made it past staging."
Freelancers handle model development; production deployment requires data engineering, MLOps, integration, and evaluation in parallel. A 90-day production deployment takes hundreds of engineering hours; at independent-consultant rates of $150–$350/hr that is comparable to or higher than the AIDOLS Build tier — without the team coverage.
"A fee-refund guarantee sounds too aggressive for a research-heavy use case."
A Build's fee is refunded in full if the deployed system does not generate ROI exceeding it within 90 days; Scale carries a prorated refund, and the Sprint, an assessment, carries none. For research-heavy use cases where ROI is hard to measure inside 90 days, AIDOLS scopes a Sprint first and is explicit about the difference at kickoff.
FAQs from VPs of AI and Heads of AI
How does AIDOLS work alongside an existing in-house AI team?
AIDOLS works inside your repo, your MLOps stack, and your CI rather than in a vendor sandbox. Engagements pair an in-house engineer on the Build for knowledge transfer, write handover documentation progressively, and design the runbook for your team to operate. The pattern is structured for clean handover at SOW close.
Can AIDOLS clear backlog items the in-house team cannot reach?
Yes. VP-of-AI engagements are scoped against backlog items the in-house team cannot reach without delaying higher-priority strategic work. AIDOLS clears specific items in 90 days under fixed fee with full handover, freeing in-house capacity for higher-priority work.
How does AIDOLS approach MLOps stack consolidation?
AIDOLS Sprint engagements produce a stack consolidation analysis: capability map, overlap analysis, decommissioning sequence against existing platform commitments. The goal is fewer strategic platforms aligned with what the team operates deepest, with sequenced decommissioning over 6–12 months.
Is AIDOLS vendor-neutral on MLOps tool recommendations?
Yes. AIDOLS does not have reseller relationships with MLOps platform vendors and produces evaluation artifacts based on actual production deployment experience rather than vendor sales material. Recommendations are grounded in operational quality at scale, total cost of ownership, vendor health, and substitution cost.
How does the Scale tier work as a hiring bridge?
The Scale tier ($25K/month) provides ongoing engineering and operations capacity while in-house AI hires close. Scale (12-month minimum) bridges capability while the team scales from current size to target, then wind down the retainer once the in-house team is operational.
What handover artifacts does AIDOLS produce at SOW close?
Every Build hands over source code, model weights (where applicable), evaluation datasets, infrastructure-as-code, the on-call runbook, and architecture decision records. The handover documentation is written progressively during the Build rather than at SOW close, and is reviewed by the in-house engineer paired on the engagement.
Can AIDOLS deploy against our existing model registry and serving infrastructure?
Yes. AIDOLS deploys against your existing model registry (MLflow, SageMaker Model Registry, Vertex AI Model Registry, Databricks Model Registry), serving infrastructure (vLLM, TGI, KServe, SageMaker Endpoints, Vertex AI Endpoints), and evaluation harnesses. Where genuine capability gaps exist, the gap is scoped specifically rather than addressed by replacing the existing stack.
How does AIDOLS pricing compare to hiring senior ML engineers?
A senior ML or MLOps engineer is a permanent headcount commitment and a months-long hiring cycle. The AIDOLS Build tier ($75K–$150K) ships a production system in 90 days under a fixed fee, with full handover to your team. The Scale tier ($25K/month) bridges ongoing capacity while in-house hires close.
AI consulting for other roles
AIDOLS works across the full executive stack. If your engagement spans multiple functions, we scope it jointly.
Next steps
- What AI-native consulting is Engineers who build and deploy the system on a fixed fee, instead of a strategy deck billed by the hour.
- Course lesson: training that survives Friday A session counts as training only if everyone leaves with a working asset for a task they repeat.
- Course lesson: sequencing the second move Pick the first move for the evidence it leaves behind: evidence is what pays for the second.
Three ways to start, scoped to a VP of AI mandate
Pick the path that matches the next decision your office has to make.